Applications of Advanced Technology in Transportation (2006)

The 9/11 terrorist activity followed by Madrid and London mass transit bombings have had far-reaching effects on transportation infrastructure security planning. Transportation infrastructures (such as, airports, metro and subway stations, bridges, and tunnels) are vital to any nation's economy; therefore, in the wake of recent terrorist incidents innovative and robust methods need to be exploited to predict likelihood of terrorist strikes. This research is motivated by the growing vulnerability of transportation infrastructures in the post 9/11 era. Bayesian Networks (BNs) have been proven to be very effective in predicting future scenarios, such as weather forecasts and market predictions. In this paper a BN model is developed for predicting likelihood of a terrorist strike at a critical transportation infrastructure facility, such as an airport or a mass transit station that acts as a hub. An example study is performed to demonstrate the effectiveness of the developed approach. Limitations of the proposed approach are outlined and Dynamic Bayesian Networks are suggested to be exploited in the future for solving real-world problems at the expense of lower computational burden.

Data describing the condition of physical facilities, such as bridges and pavements, provide the foundation for decisions about maintenance and rehabilitation. Condition data are collected and reported using a variety of technologies, with varying levels of error, uncertainty, and cost. Agencies practicing effective asset management would like to minimize lifecycle costs for their facilities; condition assessment affects lifecycle costs both directly (through the cost of collecting and analyzing the data) and indirectly (through the actions taken in response to the condition assessment). In addition to agency costs, user costs are affected as well. Uncertainty in pavement condition assessment can have significant effects on average network condition and lifecycle costs. Specifically, the work described uses simulation of pavement deterioration and rehabilitation to track pavement condition and associated maintenance and rehabilitation costs over time. Scenarios evaluated include uncertainty due to systematic errors as well as random variation. Finally, the paper reports the results of a statistical evaluation of the simulation scenarios.

In this research, a reliability-based optimization model of bridge maintenance and replacement decisions is developed. Bridge maintenance optimization models use deterioration models to predict the future condition of bridges. Some current optimization models use physically-based deterioration models taking into account the history of deterioration. However, due to the complexity of the deterioration models, the number of decision variables in these optimization models is limited. Some other optimization models consist of a full set of decision variables; however, they use simpler deterioration models. Namely, these deterioration models are Markovian, and the state of the Markov chain is limited to the condition of the facility. In this research, a facility level optimization model of bridge maintenance and decisions is developed, using a Markov chain whose state includes part of the history of deterioration and maintenance. The main advantage of this formulation is that it allows the use of standard optimization techniques (dynamic programming), while using realistic, history-dependent deterioration models. This research presents a method to formulate a realistic history-dependent model of bridge deck deterioration as a Markov chain, while retaining relevant parts of the history of deterioration, using state augmentation. This deterioration model is then used to formulate and solve a reliability-based bridge maintenance optimization problem as a Markov decision process. In a numerical example, the policies derived using the augmented Markov chain are applied to a realistic bridge deck, and compared to the policies derived using a simpler Markov chain.

Key to a successful building asset management plan is the ability to measure current condition and predict future condition and degradation trends over a specified planning horizon for each and every individual component-section present in a building. However, this ability poses a difficult challenge because of the vast array of different components, their material make-up, and type found in a building and because each has a different expected life and degradation curve. In addition, condition trends and service lives depend on the amount of preventative and corrective maintenance, including repair, invested during a component-section's lifecycle. Because of these variables, it is difficult to accurately project a condition-lifecycle trend for each individual component-section without a periodic inspection and a meaningful condition metric for measuring condition. This condition metric provides data for the lifecycle prediction process. However, condition data are most often very limited for any given component-section. This paper addresses the use of the Weibull probability distribution function with the data collected during component-section inspections to predict lifecycle condition and reliability over time. The prediction model is self-correcting using attribute information collected during both current and historical inspections to accurately project the unique lifecycle degradation trend for an individual component-section in a building.

We propose state-space specifications of autoregressive moving average models and structural time series models as a framework to develop and estimate performance models for transportation infrastructure facilities. Time series models in state-space form fit the maintenance optimization model of Durango-Cohen and are consistent with the latent performance modeling approach of Ben-Aldva and Ramaswamy. To illustrate the proposed framework we develop and estimate performance models for an asphalt pavement using pressure and deflection measurements generated by sensors and falling weight deflectometers, respectively. Analysis of the results shows that the ensuing models are consistent with physical properties of flexible pavements. The results also indicate that state-dependence may be statistically significant and further reinforces the computational and statistical advantages of the proposed framework over Markovian transition probabilities.

Performance model is one of the key elements in transportation infrastructure management systems. Both observed and unobserved difference among the sections (helerogeneities) should be accounted for in modeling the performance. The observed heterogeneity is addressed through explanatory variables; while the unobserved heterogeneity results from other factors not included in the explanatory variables. Most of the existing work only focuses on the observed heterogeneity. Thus, the uncertainty on performance modeling is not well captured. In this paper, the issue on performance heterogeneity is investigated. A structured econometric model with hierarchical parameter structure is established. The Maximum Simulated Likelihood approach is applied to estimate the model. It is found through statistical analysis and test that unobserved heterogeneity is significant. In addition to the population parameter estimates, the individual-specific parameter estimates are also obtained, which are helpful in revealing the deterioration behavior of individual sections.

In the proposed Mechanistic-Empirical Pavement Design Guide (MEPDG), the functional performance indicator is pavement smoothness as measured by the International Roughness Index (IRI). The MEPDG IRI prediction models were developed based on the general hypothesis that changes in smoothness result from various distress types that can be predicted by the MEPDG program. Using pavement distress data from the Long Term Pavement Performance (LTPP) database, traditional regression analysis was used to statistically establish the MEPDG prediction equations. This paper attempts to use a new technique for pavement smoothness prediction. The gray system theory was devised in the 1980s for modeling uncertain systems with the characteristics of partially known information. A pavement performance prediction system can fit the domain of the gray system. The gray theory based prediction method is used in this paper to develop IRI prediction equations. With the data exported from the LTPP database, it is found that certain specific types of distresses significantly affect the accuracy of the predictions. After trial and error calculations. Gray Model based smoothness predictions are established using influencing factors similar to the ones used in MEPDG. Based on the comparisons of results from the two prediction methods with LTPP field data, it is shown that the Gray Model based method provides promising results and may be useful for modeling pavement performance.

Roadway signs represent a substantial investment of public money in road and highway infrastructure. However, the current level of automation in sign identification and recognition, size dimensioning, and location identification is not satisfactory. This paper targets the development of an automated road inventory system addressing these issues. A framework of combining the conventional image processing methods with the Kalman filter tracking method is applied to improve the accuracy and efficiency of the ROW image processing. Through the tracking technique, the candidate region of the road sign in the picture can be predicted based on the previous image frame. Detection efficiency and accuracy of the sign can be improved. The methodologies described in the paper fit a dynamic and motion environment, appropriate for a highway survey vehicle.

This paper reports on the assessment of the effectiveness of an add-on software tool named QUALITIME, which is conceived to schedule and represent quality activities in a graphical mode. QUALITIME is expected to integrate with existing scheduling software tools such as Microsoft Project and Primavera, and provide an environment to schedule quality activities based on the frequency and timing of their occurrence, and in connection with schedules of construction activities. Its use will render the management integration between construction and quality more beneficial, and will furnish more effective automated schedules for improved analyses and decision-making processes. The assessment is performed by analyzing outcomes of case studies from the highway and bridge domains, in which QUALITIME capabilities are utilized to schedule inspection/test, activities. The major criteria for assessment include: (1) the needs and benefits to schedule quality activities, to computerize their information and tasks, and to represent them graphically and in association with construction schedules, (2) the determination of the relevant information to be captured by the tool, (3) the definition of the tasks to be performed, and (4) the implications of the consideration of the quality activities, as distinct time entities, on the duration of construction schedules. Refinement of the software tool design will be carried out based on the results of the case studies.

This paper discusses image processing algorithms for the recognition of environmental and road conditions from real-time camera images. This research addresses various implementation techniques for and considerations of the implementation of algorithms to extrapolate various features from images taken by stationary traffic cameras. These algorithms have uses in the areas of traffic pattern analysis, emergency response, real-time traffic situation awareness, and homeland security. This paper covers the design and implementation of an automated camera heading detection system to determine the directional components of a camera's position using the current camera image, various computer vision techniques, and a series of classification training images. Image processing topics addressed as part of this research include edge detection, line detection, and two-dimensional filtering.

Infrastructure management is the process through which inspection, maintenance, and rehabilitation decisions are made with the aim of minimizing the total life-cycle cost. The inputs to decision-making include current facility condition as assessed from field observations, and future facility condition as forecasted based on the current condition and possible maintenance and rehabilitation (M&R) actions using a deterioration model. The quality of the estimated current condition depends on the accuracy of the inspection technology, the spatial sampling employed, and the nature of correlation between condition variables at different locations. The uncertainty associated with spatial sampling has not been recognized and quantified in the infrastructure management literature. However, its impact on arriving at the optimal M&R decisions can be appreciable. This paper motivates the importance of sampling, focuses on quantifying the spatial sampling uncertainty, and investigates the effect of incorporating spatial sampling as a decision variable on the expected minimum total life-cycle cost.

This paper introduces application of Shackle's model for highway project evaluation under uncertainty. Shackle's model overcomes limitations of existing risk-based approaches for project evaluation by: (1) using degree of surprise as a measure of uncertainty instead of probability distribution, (2) introducing a priority index to jointly evaluate each outcome and degree of surprise pain, and (3) establishing a focus gain-over-loss ratio as the basis of project comparison under uncertainty. Survey data were utilized to calibrate systemwide focus ratio functions to help establish focus ratios as project benefits under uncertainty. The calibrated focus ratio functions were applied to data on past candidate projects for Indiana state highway programming in 1998–2001 to establish focus ratios for individual projects. The computed focus ratios as project benefits were further utilized in an existing optimization model for project selection. It was found that consistency matching rate between lists of projects selected using proposed methodology and list of projects actually implemented was at a minimum of 84 percent. The model could be used by transportation agencies for enhanced highway project evaluation under uncertainty in long-range planning and programming.

Federal, state, and local transportation agencies are faced by issues of steady increase in travel demand, deterioration of physical asset conditions, public demand for government accountability, and pressure of constrained budget. Responding to these challenges, transportation agencies have started to advance asset management concepts that cover all physical assets and usage of a highway transportation system, identify needs of the entire system, and determine investments to maintain and improve highway performance most cost-effectively. The existing models for project selection in investment decision process treat the optimization problem as a deterministic problem. The random nature of budget faced by the decision-maker is not considered, which limits achieving robust results. This paper introduces a stochastic optimization model for project selection that considers budget uncertainty. The model was formulated as the stochastic multi-choice multidimensional Knapsack problem with Ω-stage budget recourses. Multi-choice corresponds to multiple budget levels for different asset management programs, while multi-dimension refers to multiple years of analysis. The objective was to select a subset of candidate projects to achieve maximized system benefits under budget and other constraints. An efficient solution algorithm was developed using Lagrangian relaxation techniques. Data on candidate projects for Indiana state highway programming in 1998–2001 were used to apply the proposed stochastic model and solution algorithm. To assess the impact of budget uncertainty on project selection, the stochastic model was also applied to the same data set without considering budget recourses, namely, the model was treated as a single-stage deterministic model by only executing the first stage optimization using expected budget. The two sets of results generated were compared and it was revealed that the stochastic model and solution algorithm could assist transportation agencies for enhanced highway investment decisions for optimal system performance.

In order to proactively manage facility assets and allocate resources to optimize facility performance, infrastructure asset management professionals at all levels need improved decision support tools and processes. This paper addresses the business process framework for developing a comprehensive facility infrastructure investment plan using information about current and projected future asset condition. This process starts by identifying which building components are candidates for corrective repair or replacement using standards and policies linked to acceptable building performance requirements. Next, a prioritization schema determines which work actions are most important for funding when budgets are constrained. Priorities are developed through importance and condition metrics, risk assessment, penalty costs of not doing work, and return on investment (ROI) analysis and metrics. These financial metrics also help to determine the best option between component stop-gap repair, major repair, or total replacement alternatives by evaluating the economic return over the lifecycle of the asset. Finally, consequence analysis simulations determine the impact of difference standards, policies, and budget levels, further maximizing building performance and return on investment.

Based on fuzzy multiple objective mixed integer programming, a model is introduced to simultaneously determine the number of carriers to employ and freight volume to allocate to these carriers under a multiple- cargo, multiple- carrier scenario. The model jointly considers the objectives of transportation cost, cargo loss and damage, and cargo delivery performance as well as the freight rate discount based on total transportation revenue. Furthermore, the solution procedure for this model is illustrated. At last, a numerical example is provided to verify the availability of above model and perform the results analysis. It is shown from results analysis that the transportation cost, cargo loss and damage quantity, and cargo late delivery quantity are increasing in the degree of vagueness in carriers' transportation capacity.

Current Hot Mix Asphalt (HMA) pavements are designed to withstand expected traffic and environmental conditions for a particular roadway section. Segregation, or lack of homogeneity (uniformity) in the in-place HMA constituents, has been identified by the asphalt industry as one of the most common problems associated with premature failure of HMA pavements. Segregation leads to accelerated pavement distresses, which translates in a reduction in the level of performance and service life of the pavement. Therefore, HMA Quality Assurance procedures usually include provisions to detect and quantify the presence of this problem in newly constructed pavements. Traditionally, visually identified areas of non-uniform surface texture have been classified subjectively as segregated HMA and, therefore, as bad construction. To reduce the subjectivity involved in the quality assurance, an automatic measurement-based process is necessary. This paper presents a new methodology to detect and quantify HMA segregation using digital image analysis. The main objective of the non-contact system presented in this paper is the detection of segregated HMA areas and the identification of the locations of these areas along a road for HMA quality assurance purposes. The system uses relatively low cost off-the-shelf components for capturing images of pavement and innovative image processing and analysis software to automatically detect changes in the surface appearance due to segregation. The system can be used stand-alone or in combination with current laser-based systems for pavement quality assurance and has the potential to reduce the subjectivity in the HMA inspection process in addition to providing a permanent and periodic record of captured images, thus enhancing pavement quality assurance practices.

Pavement subgrade characterization in terms of resilient modulus (M_R) is essential for pavement design. This paper presented a new methodology to monitor and evaluate seasonal variation of subgrade resilient modulus. First, a kind of typical cohesive subgrade soil was tested to determine the relationship between tension (Ts) and saturation degree (Sr) utilizing soil tensiometer in laboratory, as well as the relationship between resilient modulus and saturation degree based on repeated triaxial test. The majority of the analyses had R2 values above 0.98, which indicated that a good correlation exists between tension, saturation degree and resilient modulus. Thus it is allowed to predict the resilient modulus of cohesive soil from the tension. In addition, a road section was instrumented, coupled with Falling Weight Deflectometer (FWD) deflection tests monthly over a period of a year. Further, AASHTO equation method and MODULUS program were both used to back-calculate the subgrade modulus based on the FWD deflection data. Finally, a corrective coefficient was suggested for pavement design when using the MODULUS program to back-calculate the sub grade modulus. A comparative analysis was conducted throughout the above process.

The Hamburg Wheel Tracking Device was developed as specification requirement for some of the heavy-duty road to measure rutting (potential) and stripping (moisture susceptibility). TxDOT adopted cylindrical specimens for testing with the HWTD (Hamburg Wheel Tracking Device) manufactured by PMW, Inc. Traditionally, the rut depth has been represented by the largest impression point which occurs randomly along the 11 points. The maximum impression is randomly located and more likely to be in the middle part of specimen. It is difficult to characterize with a single theoretical distribution such as a normal or other such function that defines one central tendency. This paper detailed the spatial distributions of Hamburg Wheel Tracking Device Test Results and demonstrated its predictive capability using statistics tools. In addition, the author conducted statistical analysis of all the test points in four patterns, and thus a comparison between all patterns were carried on. Finally, a more reasonable representative pattern is proposed. Central to the approach developed in this research was utilizing a statistics tool to better characterize the deformation. It is certainly more straightforward to represent them with maximum deformation rather than other averaged points. But in case where small amounts of observed data are available, large variance could prove prohibitive to reliable analysis, which will finally result in great uncertainty in predicting performance. Average of the next four maximum deformations presents a relative smaller variance compared to other method. In case observed data are limited, it will provide a relatively better choice.

Health monitoring systems that monitor location, characterization, and quantification of damage can benefit from model-based assessment to relate better the measured response to physical indicators of damage. Subsurface sensing technologies, one of many sensing tools available for health monitoring systems, produce wave-based responses that traverse the subsurface and can be converted to a subsurface image for further interpretation. An integrated modeling environment is under development by the authors. The authors use this modeling environment to simulate responses from a ground penetrating radar investigation of a reinforced concrete bridge deck. The focus of this paper is to analyze the differences in the simulated responses due to variability in the dielectric properties of concrete and the presence of air voids. Calculated dielectric properties can vary 10% – 15%. Quantification of the dielectric variability is necessary to develop robust solutions of the inverse problem that will characterize subsurface conditions.

Rail transit circuit generally passes through the downtown area in a city. The ambient vibration effect induced by rail transit systems has aroused a great deal of public attention. It is necessary to make a comprehensive evaluation on ambient vibration effect to protect a preferable environment. This paper presents a series of detailed analysis of vibration effects on urban environment as wave propagation by different attenuation factors with numerical methods. They make a contribution to study on how vibration effect cause harm to the human body, and damage to building structures. Moreover, this paper put forward some suggestion of countermeasures.

Flash LADAR (Laser Detection and Ranging) devices are one of the recent technology developments which allow rapid spatial data acquisition of scenes. Algorithms that can process and interpret the output of such enabling technologies into three-dimensional models have the potential to significantly improve work processes. A particular important application in transportation is modeling the location and path of objects to make construction processes safer and more secure. Once people and equipment are detected and mapped into a three-dimensional computer model, their path can be analyzed and access to hazardous areas can be restricted. This paper presents experiments and results of a real-time three-dimensional modeling technique to detect static and moving objects within the field of view of a high-frame update rate laser range scanning device. Applications related to transportation and construction are specified.

At present, there are many questions regarding the application of 3PLs (the 3^rd part logistics) in the logistics industry. 3PLs relate to meeting the needs of client sufficiency and efficiency, as well as improving the service benefits provided to clients. It has been determined that project management can offer the solution to 3PLs' developing logistics service projects. The 3PLs are also intent on implementing PM (project management) in spite of the absence of PM tools, which combine with logistics practice. This paper is based on analyzing the trade-off between time, quality, and cost, by using a multi-variable math optimization model in logistics service PM. In addition, a case study is provided to support the theory that the PM procedure can apply to 3PLs' service projects with the used of one 3PLs' true data.

Out-of-gauge goods are more prone to lead to train operation accidents than ordinary goods. Therefore, it is very necessary to keep safe distance between out-of-gauge goods and structure. According to this, the paper analyzes the constitutions of the safe distance and puts forward the mathematic model to determine the minimum safe distance. Also an example is applied to calculate the minimum safe distance.

The paper presents three container feeding forms between railway container junction station and its transaction station: Project I —Use usual container train directly to feed into junction station; Project II—Use collecting and distributing train directly to feed into junction station to rearrange; Project III—Use collecting and distributing train to feed into marshalling station to rearrange, and for container wagon suspended, use terminal transfer train to feed into junction station. The operation procedure of each project is analyzed, and the total consumption of each one is calculated. Finally, conclusions are reached by comparing consumption and feasibility with each other: Project I is regarded as the developing orientation of container transportation organization while Project II or III can be used as transition measure under the current situation.

Route guidance system (RGS) is widely used as one of main measures of Intelligent Transportation System, which means to maintain an acceptable service level for mobility of persons and goods. But the traffic benefits brought by RGS vary a lot with the characteristics of road networks and traffic flow. Then how to evaluate the traffic benefits is of importance to its implementation. The paper analyzed traffic benefits of RGS. Evaluation Measures of Effectiveness (MOEs) of RGS and its definition models are put forward. An application of the research results is performed with TESS (Tongji transportation nEtwork Simulation System). Comparison of outputs from TESS and VISSIM is provided, which proved the validity of the research. The research is important for evaluation of RGS and integration of RGS and other traffic management and regulation measures.

Collecting traffic counts on high volume and high speed highways is particularly troublesome. Even in the early morning hours, placing road tubes or other devices in the road can be extremely hazardous to the field technicians and motoring public. To this end, Portable Non-Intrusive Traffic Data Collection Systems have been developed through extensive research in states including Minnesota, Virginia, New York, Wisconsin, and Kentucky. The purpose of this past research was to develop a traffic counting system that did not require entry into the traveled way. The solutions developed included mounting microwave or acoustic sensors on temporary poles attached to existing roadside signs or trailer-mounted telescoping poles. The testing grounds for these devices have typically been freeways, which had ample roadside space, favorable roadside topography, and were free of utilities. This permitted the devices to generally be mounted at the ideal location according to the manufacturers' specifications. However, the counting performance of the sensors is affected by the height and distance from traffic at which the sensors are placed. Placing the sensors at the recommended heights and offsets can be difficult in more restricted roadside environments. For example, the acoustic sensors are optimally placed high above the traffic (25-ft to 30-ft, 7.6-m to 9.1-m) and close to the traffic stream (10-ft to 20-ft, 3.0-m to 6.1-m), while microwave sensors are optimally mounted lower (15-ft to 25-ft, 4.6-m to 7.6-m) and further from the traffic stream (25-ft to 35-ft, 7.6-m to 10.7-m). In non-freeway environments, limited right-of-way, adjacent property development, and overhead utilities can complicate equipment set up. In addition, because the sensors will tend to be located closer to the lanes, roadside safety and capacity restriction become issues. Finally, in non-freeway environments, pedestrian / security issues also become important. The purpose of this research was to test the portable non-intrusive traffic data collection systems in a variety of environments, including some restrictive non-freeway environments. This research demonstrates the sensitivity of the equipment in matching manual traffic counts and vehicle classification with various roadside restrictions.

Although public transportation has been using intelligent transportation infrastructure since the 1960s, recent advancements in technology and an increased agency focus to the Americans with Disability Act are benefiting the transit industry. Advanced Public Transportation System (APTS) includes Fleet Management Systems, Traveler Information Systems and Electronic Payment Systems. APTS utilizes advanced technologies ranging from Automatic Vehicle Location (AVL)/Computer Aided Dispatch (CAD) System, Automated Passenger Counter (APC) to Automated Fare Collection (AFC), Automated Vehicle Component Monitoring (AVM), on-board and wayside security systems, multi-modal coordination, and Transit Traveler Information (TTI). The long-term goal of collecting AVL and APC data is to use these data to improve transit Level of Service (LOS). LOS includes service reliability measured by both on-time performance and regularity of headways. In this paper we present a computer visualization prototype, which maps static bus schedules and can be extended to integrate "real time" active APC/AVL data measure the on-time performance and headway adherence. This is accomplished using ArcObject in ArcGIS and VBA programming language. Using this visualization tool, the difference between active and static data of a trip, or between active data from two or more continuous trips, can be readily measured and compared on a dynamic map, which updales the active/schedule location of one or more buses given a specified frequency of a time frame (i.e., a snapshot in time). We demonstrate how the mapping is accomplished using the Chicago Transit Authority (CTA) bus route schedules. Such a visualization tool is not only useful for transit planning and LOS measures but also beneficial to transit users if such a tool is made available online to the public. Travelers can gain real-time, accurate information about where the buses are and when they arrive at particular stops. Better utilization of APC/AVL data leads to better understanding of bus performance and thus results in better planning and operation management. The powerful functions of ArcGIS in visualizing spatial data and Oracle's database management capability will provides very strong IT support for transit data utilization. Lastly, future research directions with respect to full utilization of APC/AVL data are also identified in this paper.

Recently, many public transit systems have equipped their vehicles with automatic vehicle location (AVL) systems, which periodically provide position and speed for each transit vehicle in the fleet. Although AVL is deployed for transit operations, the vehicles also provide valuable information about the traffic stream throughout the road network. In this study we develop a methodology to mine the transit AVL data and find all trips that use any portion of a pre-specified portion of a freeway. These trips are then used to measure travel time and average speed over the freeway, thereby quantifying conditions on the facility. The results are validated against concurrent loop detector data from the corridor, though the greatest benefit would be expected in areas without fixed vehicle detection.

The study of lane-changing behavior of drivers is important for development of microscopic simulation models as they attempt to closely replicate real world phenomena. This paper presents the lane changing intensity at a ramp weave section observed from field data collected using aerial photography at 1-second intervals. Frequency and percentages of lane changing versus position of lane changes was observed for vehicles moving from the shoulder lane to the auxiliary lane and vice versa. The percentage of weaving vehicles from and to the auxiliary lane shows that the first 300 feet from the entrance gore represents a section of intense lane changing. The number of lane changes from the shoulder lane to the auxiliary lane was found to be double compared to the number of lane changes from the auxiliary lane to the shoulder lane. This paper also presents the frequency and percentages of lane changing from the shoulder to the median lane which occurs more heavily in the later part of the freeway section. This study may also be used to improve traffic operations by analyzing the adequacy of length of acceleration and auxiliary lanes.

Direct computation of life cycle cost consequences of as-constructed - as opposed to as-designed — quality measures, is presented as a basis for developing the pro-forma financial statements necessary for managing highway pavements as assets. The current and next-generation mechanistic-empirical pavement design and analysis models, combined with the development of easily deployable non-destructive testing technologies, increase the feasibility of estimating the combined effects of variations in the in-situ material properties that influence pavement quality and performance. These models can be deployed to develop the inputs to financial statements. In the process, one can not only develop a framework for monitoring financial health over any given performance period, but also estimate the difference between the life cycle costs associated with the as-designed and as-built pavement, which can, in turn, provide a basis for determining contractor penalties or bonuses, suggested here as being an important component of pavement asset management.

Proper evaluation of pavement conditions provides important decision-support to implement preventative rehabilitation. Traditional method for pavement inspection relies on human observation that has low-efficiency and is subjective to errors. More recent tool for pavement distress inspection utilizes digital video and image analyses to record and identify the pavement surface conditions. This significantly increased the inspection efficiency and reliability. However, the video based system requires large storage space and extensive computation for image processing. It is also difficult to automate the pavement rating. This paper describes the use of recent data acquisition hardware to develop a vibration-based system for preliminary evaluation of pavement conditions. In analogy to the video record which "looks" at the surface of the pavement, the vibration-based system "feels" the ground conditions based on mechanical responses of the testing vehicle. The interactions of the ground and vehicle can be described by a model where vehicle is under random force excitations. The distresses of the pavement, including the cracks and surface rutting, impose impacting forces on the vehicle. The frequency and magnitude of the forces are dependent on the extent and magnitude of pavement distresses. On the other hand, the pavement surface conditions can be estimated from the recorded responses of the testing vehicle when driving on the pavement. A testing system was set up and data were collected to validate the measurement concept. Analyses of the testing data indicate that there is a good correlation between vibration responses and the pavement conditions. Factors such as the driving speed and the transition of vehicle motion were investigated. The vibration-based pavement evaluation system described in this paper has the advantage of small storage requirement, cost-effective and amenable for automatic real-time data processing. While this system does not provide the complete details of distress characteristics as by video-based system, it can be an inexpensive tool for routine inspections to provide preliminary evaluation of pavement conditions.

The American Association of State Highway & Transportation Officials (AASHTO) LRFD (Load and Resistance Factor Design) Bridge Design Specifications, the nationally-accepted specifications for bridge designs, stipulate the design method for concrete barrier on concrete bridge deck. This study observes stress distributions on the concrete barrier and its adjacent structures when they are under a transverse impact load. Three research models are established for this study. An area of potential maximum tensile or compressive stress on the concrete barrier, which is called a "yield zone", can be considered the location of failure under the extreme transverse impact load. The results of this research show that the shapes and locations of yield zone are affected by the support structures and their rigidity. This study also discusses the limitations of the analysis method used by the AASHTO Specifications.

This paper reviews experiences with advanced technologies and asset management in Canada, New Zealand, Australia and England in national, state and local agencies visited on the Spring 2005 Asset Management International Scanning Tour supported by the Federal Highway Administration and American Association of State Highway and Transportation Officials. The paper describes both the technology and the context in which it is applied to Asset Management (AM) in terms of the institutional setting and the AM process used in order to identify how those countries implemented Asset Management Systems (AMS) using advanced technology in various areas. The paper concludes with lessons that can be translated to agencies in the United States in terms of technology adoption and implementation.

This paper formulates the maintenance and repair (M&R) decision-making process as a quadratic program. The functional interdependencies in transportation infrastructure are captured in the model, and the conditions under which it is optimal to synchronize maintenance schedules for groups of components are found. This result suggests that an effective management process depends on attending to the interdependencies that link a system's facilities.

Evaluation of the load capacity of existing bridge decks is a major concern for the Louisiana Department of Transportation and Development. Available funds must be expended in a rational strategy, which would allow for inspection, maintenance or rehabilitation. This paper presents the results of the study on the proposed higher truck loads and their economic impact to the state and to the industry. The impact of adapting such regulations on the existing Louisiana bridge decks is determined based on a deterministic load capacity evaluation as well as a reliability assessment. The target reliability level is derived from bridge deck structures designed to satisfy AASHTO standard design specifications and to satisfy safe and adequate performance levels. Fatigue analyses are performed using finite element modeling with detailed load capacity evaluations and reliability analyses on a representative sample of bridge decks to provide specific examples of expected changes in rating and safety levels.

European regulations for the management of roadway tunnels became stricter after the 1999 Mount Blanc inferno. Several video image processing (VIP) devices for traffic surveillance have automated incident detection (AID) capabilities. Autoscope, Citilog and Traficon were tested with eight pre-existing cameras in Attica Tollway tunnels. The suppliers of the competing devices were responsible for setting and calibrating the devices for best results. No literature was found in which VIPs developed in year 2000 or later were evaluated for incident detection. The results show promise for the future, but the evaluated performance was poor. This is due to three reasons (1) low maturity of the technology, (2) complex algorithms due to the provision of extensive functionality and (3) suboptimal camera location and height for image processing, but VIP devices must adapt to tunnel limitations.

During the past 10 years, different approaches of traffic monitoring have been reported. Most of these methods either rely on a large knowledge-base or are subject to many restrictions. That means they are time-consuming and difficult be carried out in real time. This paper tries to present a new algorithm to solve this problem. We divide the process into two main procedures. Firstly, an improved method is put forward to perform background subtraction, which segments slow moving objects from an illumination-invariant background. Secondly, object tracking is performed with an improved particle filter, which can avoid the difficult problem of matching. The proposed approach has been tested on several highways and satisfactory results have been obtained.

This paper presents a video-based method for evaluating and validating volume and speed data collected with traffic detectors. Assessing the detector data reliability is a critical task for all Intelligent Transportation Systems (ITS). Their performance will be significantly impacted by the data quality. Most existing studies mainly use volume as the only variable for evaluating the detector quality. A cost-efficient and rigorous method that can concurrently evaluate both volume and speed from the detectors is not available yet. The video-based method presented in this study is both cost-efficient and sufficiently reliable for evaluating detector data for ITS system operations. For example, the performance of a travel time prediction system is very sensitive to the quality of the detector data. This paper will mainly detail the key features of the proposed system and its application in a case study of RTMS detector data on I-70 corridor in Maryland.

Over the past several years, traffic analysts and researchers have been exploring the use of airborne imagery to understand traffic behavior and to analyze traffic speeds, volumes, densities, delays, queue lengths, and other traffic parameters. As part of the National Consortium on Remote Sensing in Transportation - Flows (NCRST-F), researchers at the University of Arizona have been developing software tools to assist in analyzing traffic behavior, at the level of the individual vehicle (or, "microscopic" traffic behavior). To do this, we have gathered digital video segments taken from a helicopter over traffic facilities, using a digital video camera. The video image sequence is then processed using software TRAVIS (Tracking and Registration of Airborne Video Image Sequence). The software registers the video sequence (eliminating the movement of the aircraft from the image), identifies vehicles in the images, and tracks the vehicles through the video image sequence. The vehicle coordinates in the image sequence can then be transformed into relevant traffic measures: location, speed, acceleration and deceleration, and position in terms of its lane on the roadway. With a large set of video segments, the software has been able to identify and track individual vehicles automatically, thereby significantly reducing the need for manual data reduction for some traffic applications. We also discuss the applications of the data from airborne imagery for understanding microscopic traffic flow and for the development and calibration of traffic simulation models.

This study proposes the method of predicting the temporal variation in crash risk on freeway ramps and at the intersections of ramps (the junction of ramps with crossroads). Using a 5-year ramp-related crash data on a freeway in Orlando, Florida, the study develops probabilistic models that relate the frequency of crashes with the factors such as ramp type (off-ramps and on-ramps), ramp configuration, daily and hourly ramp traffic volume, and 5-minute average speed and volume in the mainline close to ramps 5∼10 minutes prior to the time of crashes. The study estimates crash rates (the expected number of crashes divided by ramp traffic volume) as surrogate measures of ramp-related crash risk using log-linear models. The parameters of the models showed that the frequency of ramp-related crashes generally increased with ramp traffic volume and crash rates were higher on off-ramps than on-ramps. It was also found that higher volume and lower speed at the locations near ramps significantly contributed to higher crash risk. Considering the temporal variation in traffic condition, it was found that crash rates were higher during dawn (12∼6 am) compared to the other time periods of day and they were higher on loop and outer connection ramps than diamond ramps. The study demonstrates how the models can be applied to freeway traffic management in predicting temporal variation in ramp-related crash risk based on ramp geometric and traffic flow characteristics. Findings of the study suggest some traffic control strategies that may mitigate high ramp-related crash risk.

Data mining is the analysis of large "observational" datasets to find unsuspected relationships that might be useful to the data owner. It typically involves analysis where objectives of the mining exercise have no bearing on the data collection strategy. Freeway traffic surveillance data collected through underground loop detectors is one such "observational" database maintained for various ITS (Intelligent Transportation Systems) applications such as travel time prediction etc. In this research data mining process is used to relate this surrogate measure of traffic conditions with rear-end crash occurrence on freeways. Crash and dual loop detector data from 36.25-mile instrumented Interstate-4 corridor in Orlando (FL) are used in this study. The research problem is set up as a classification problem and separate data mining based classifiers are developed to discriminate crashes belonging to different categories from normal conditions on the freeway. Based on the models developed in this study one can identify the traffic conditions prone to rear-end crashes 5–10 minutes prior to the crash. The findings of this research are proposed to be used as a proactive traffic management system which could warn the drivers about potential rear-end crashes.

This paper investigates factors that can be used to quantify pedestrian counts in urban areas. The best subset regression is used to develop models to estimate pedestrian counts considering variables identified using general linear regression. The F-test is used to support the analysis. The models are developed using data collected at 15 selected locations with high pedestrian activity in the Las Vegas metropolitan area. The findings show that the pedestrian counts are a function of number of lanes, average annual household income and residential area proximate to the study location. Results show that the pedestrian counts are independent of the commercial area and the number of bus stops in the vicinity of the location. The developed models can be used to estimate pedestrian counts at any high pedestrian activity location provided the socioeconomic and demographic characteristics are known. The methodology is also applicable to other urban settings.

This paper describes a dynamic segmentation application that allows roadways to be quickly segmented for highway safety analysis. After roadways are segmented (either variable-length or fixed-length), the system can automatically calculate the crash statistics associated with each segment, which can then be used to identify high-crash locations, to model the relationships between crash experience and geometric features, etc. The system can output segmented records in different formats, including in tables, 2-D and 3-D charts, and GIS shape files.

This project involved the construction of a 3-D Miami downtown transportation simulation model to study and visualize the traffic operations of different transportation modes and investigate the impacts of potential security and safety events in the Miami downtown area. The 3-D model (including buildings, highways, and elevated guideways) was constructed using AutoCAD and a Geographic Information system (GIS). A traffic simulation model was built based on Cube Base/Voyager/Dynasim. In this study, the 3-D simulation model was used to assess the elapsed time for a specific evacuation scenario. The results show that it would take at least six hours to evacuate the traffic in the downtown area to the nearest external stations for a given emergency event.

The world of telecommunications is providing us with a lot of communication paradigms that can be utilized as a means of wireless communication between mobile and stationary agents or between mobile agents themselves. As transportation engineers, we are interested in knowing the schemes that can be applied to the field of transportation engineering to alleviate traffic problems like safety, congestion, environmental degradation and off late, energy consumption by the vehicles, to name a few. Intelligent Transportation Systems are undergoing a transition from demonstration projects to becoming part of the mainstream set of options available to transportation planners. Hence, evaluation of ITS is one of the most critical and important steps to be taken before any ITS technique can be deployed. Safety has been recently emerging as an area of increased concerns, attention and awareness within transportation engineering. It has been extremely difficult to evaluate safety for new and innovative traffic treatments. In this paper, we first review all the available techniques for communication between various vehicles among themselves and with the infrastructure. The aim of this is to have a complete overview of all the possible communication techniques that the world of electronics and telecommunications have proposed in the last years that can be applied for accomplishing the task of enabling vehicles communicate and interact with other vehicles as well as with the infrastructure. Then we propose a method to evaluate safety based on safety parameter called Risk Index (R.I.) to evaluate the use of vehicle to vehicle and vehicle to infrastructure communication technologies for safety.

In China, people have been paying more and more attention to incident response since the outbreak of SARS in the spring of 2003. Traffic incident impacts the normal operation of traffic system, even results in paralysis of partial functions of one city. Researches have shown that automated real-time intelligent decision support system can reduce the work intensity of operators in Traffic Operations Center (TOC). This paper introduces the Intelligent Decision Support System (IDSS) for traffic incident operators in Beijing. The prototype system and its main modules are presented. It identifies and verifies incidents that occur on the road network, then regenerate incident response strategies and render advice on what action should be taken. Preliminary applications in fire incident show it is effective to save time and avoid confusion.

Roadway networks span large distances and can be difficult to monitor. Most efforts to collect roadway usage data either require a large fixed infrastructure or are labor intensive. Advances in electronics and communication have recently enabled an alternative. Unmanned Aerial Vehicles (UAVs). UAVs capable of carrying sensors and communications hardware to relay data to the ground are now commercially available. In this paper we investigate the feasibility of using a UAV to monitor roadway traffic and develop several applications. UAVs can cover large areas, travel at high speeds and are not restricted to traveling on the road network. This paper demonstrates several applications using data from a UAV flying in an urban environment: determining level of service (LOS), estimating average annual daily travel (AADT), documenting intersection operations, and measuring origin destination flows. The descriptions of these empirical tasks are intended to provide the main concepts and methods applied to derive useful information for both off-line planning and real-time management, and to illustrate the challenges and opportunities such applications pose.

The Florida Department of Transportation (FDOT) has implemented freeway service patrol programs at major urban areas to ease traffic congestion due to non-recurring incidents. This paper describes an effort to use data mining techniques to extract information from the service patrol databases for Broward and Palm Beach counties, Florida. A pattern-based geocoding process was developed to geocode the databases. Geocoding was needed because the incident records do not contain specific geographic coordinates, but only a general description of the incident locations. An intelligent parser program was developed to scan and parse location information patterns to identify specific geographic locations. The program was able to achieve a high geocoding rate of over 95% for the service databases. A spatial analysis example making use of the geocoded data was demonstrated.

Little is known to date about the value of applying data mining, especially association mining, to transportation-generated data. The rapid development of intelligent transportation systems (ITS) has generated large amounts of data for transportation professionals. Some of these data are being used in data mining investigations following on the success researchers on areas like business and computer sciences have had, yet there is still much to be learned. The methodology used in this study consisted of using association methods to obtain new information from pairs of data such as time and level of service. In this paper, how traditional views of traffic data can be complemented with data mining techniques is explored and described, thereby demonstrating the opportunities that data mining could offer in better understanding the situation and, furthermore, help yield better decisions. The results present graphical illustrations of the associations found between the different pairs of data and the corresponding discussions.

Activity-based transportation models have set the standard for modelling travel demand for the last decade. It seems common practice nowadays to collect the data to estimate these activity-based transportation models by means of activity-travel diaries. This paper presents a general functional framework of an advanced activity-travel diary data collection application to be deployed on a GPS-enabled personal digital assistant (PDA). The different modules, which are the building blocks of the application, will be scrutinized as well.

In the early 1990's, an in-vehicle navigation and route guidance project called ADVANCE was conducted in the northeastern suburbs of Chicago. It proved that travel time data could be updated on in-vehicle devices (albeit not in real-time) to assist drivers in choosing faster routes to their destinations. This past spring, about a decade later, a more progressive but similar 3-month field experiment was conducted in upstate New York. Nearly 200 participants used state-of-the-art, in-vehicle navigation and route guidance technology in conjunction with GPS tracking and broadband wireless to share travel time data and pick the shortest paths through a congested network. The route guidance devices observed travel times, uploaded them to a central server that updated a travel time database, and then downloaded every minute to each of the probe vehicles to ensure the latest travel time information was being used while enroute. The experiment resulted in a total of 4,111,210 latitude-longitude position/ speed/ time points. The largest number of location points per user was 98,018 while the smallest was 117; the average per user was just over 26,000 location points, or 325.5 points per trip. There were 12,629 probe trips for a traveled distance of 147,316 miles over a duration of 3,945.8 hours. This paper presents a discussion of the Capital District ATIS project including the parallels and differences with the ADVANCE effort. Areas covered are: travel time data, project background, description of the study area, participant statistics, experiment design, sample results, and a summary with future research directions.

This paper presents a robust background image generation and update. The foreground (i.e., moving objects) is typically identified by the difference between two consecutive images or between a background (i.e., non-moving parts) and an input image. The former can detect moving objects faster while the latter can detect more accurately. The accuracy of the background image is very important for detecting the foreground image, especially in the background subtraction method. The speed of background generation and update is also important in real-time based operation. The proposed method can generate the background image well enough to detect the foreground objects and update the image fast enough to reflect the environmental changes in illumination and weather and scene changes due to camera motion. The proposed method includes two steps, the background generation and the background update. In the background generation step, a differential image of two consecutive images was created to separate moving and non-moving parts. That differential image was compared with the previous background image to generate an updated one. The experimental results using online CCTV videos at several different locations and traffic situations show that our method can perform robustly and effectively.

This paper presents a study as part of a growing body of research on traffic information systems implementation and their use within transport and traffic units. The paper starts by discussing traffic management systems in improving the competitive edge of transportation sectors and traffic divisions in particular. The paper then discusses the process of building an information management system and technology to improve the transportation and traffic units. The paper concludes with lessons learned from the experience of building traffic information systems and the steps needed to improve it. These steps will enable traffic managers to adapt their decisions; deliver efficient traffic management; make decisions that suites changing traffic patterns associated with incidents and special events on the road network. Consequently, traffic departments will no longer need to rely on snapshot views of traffic conditions. Traffic planning will be revolutionised because the system will incorporate traffic simulation models that will enable traffic departments to analyse congested networks.

This paper proposes two shifted continuous distribution models for preferred time headway (Tp) and time headway of drivers in steady state car-following (Tws), i.e. the lognormal and gamma models. Lognormal distribution with a shift of 0.21 seconds provides a good fit to Tp, whereas a shift of 0.26 is proposed for Tws. Similarly, shifted Gamma distribution is proposed for Tp and Tws with shifts of 0.21 seconds and 0.26 seconds, respectively. Detailed data sets (FHWA Data) observed at one-second intervals collected from aerial photography for four sites have been used in this paper. The data sites are a basic freeway section, a ramp merge, a lane drop and a ramp weave section. Tp and Tws were found to be the shortest (mean, median and st. dev.) on the ramp weave section compared to all other sites. It was also found that the shifted Lognormal distribution provided a better fit to all the observed sites compared to the shifted Gamma distribution.

The Georgia Institute of Technology research team has recently conducted an arterial performance monitoring study on a 15-intersection signal control system in Cobb County, Georgia, using GPS-instrumented test vehicles. This paper describes the defined GPS error detecting criteria and the data reduction procedure developed by the research team. Error detecting criteria are developed based on distance between two consecutive GPS points, acceleration rate, deceleration rate, horizontal dilution of precision (HDOP), and number of satellites. The impact on percentage of GPS data eliminated as a result of these varying criteria is given and performance measures are calculated for several different criteria values, allowing for a determination of the sensitivity of the performance measures to the error checking criteria set.

This paper investigates the characteristics of speed variation obtained from speed profiles of GPS-equipped instrumented vehicles driven by the participants of the Commute Atlanta Project, an ongoing instrumented vehicle research program deployed in Atlanta, Georgia. In particular, this research effort examines the relationships between acceleration noise and the level of traffic congestion on freeway segments. The results of this study may provide another prospective use of Vehicle Infrastructure Integration (VII) data which are likely to play a significant role in monitoring traffic congestion.

The use of simulation as an integral element of transit facility design has become increasingly popular with the development of new software in a transportation engineer's toolbox. Beyond the most basic operational assessments, these tools have the ability to demonstrate people movements, frequently traveled paths, and separately, levels of service. Of particular interest to architects and designers, the simulations provide improved and, at times, more realistic results that better clarify and illustrate the "effectiveness" of their work in a dynamic manner. Furthermore, gone are the days of only relying on spreadsheets and bringing to the public a few rudimentary tables listing analysis results. It is now commonplace for public interest groups to frequently expect consultants to have such a dynamic graphical tool at the ready for them to examine. STV involved in a number of projects that require input as to how transportation terminals and stations will function well before design alternatives have been finalized. In fact, the provision for simulation efforts has been a required evaluation criterion for the acceptance of specific conceptual and preliminary design schemes. Given today's heighten security concerns, accounting for rapid emergency egress is of paramount interest to designers. Pedestrian simulation modeling allows for the relatively quick analysis of multiple pedestrian scenarios including emergency evacuation and normal circulation possibilities, which realistically assigns people to the nearest exits, measures egress times, identifies points of congestion, and requires minimum preparation time. The capabilities and advantages of pedestrian simulation modeling will be demonstrated based on analyses performed for projects such as the design of the Port Authority of New York and New Jersey's World Trade Center Transportation Hub and the reconstruction of New York City Transit's Cortlandt Street 1 Train station within WTC Site. Both projects are intermodal in nature, and all involved the complex "mixing" of people from many origins and destinations. The images and videos provided by the simulation, and included with this paper, have proven to serve as powerful and conclusive input to designs that provide for the smooth and efficient movement of people through stations and terminals.

We present a large-scale vehicle routing architecture and the associated protocols that aim at delivering real time vehicle guidance. In our architecture, communication functions and vehicle route processing are separated into distinct subsystems, where the former deals with wireless communication through a roadside mesh-like network and the latter involves a design framework for low-complexity real-time vehicle routing table computation. We demonstrate that the bandwidth requirement is modest, the processing requirement within the vehicle is minimal, and vehicle routing complexity can be drastically reduced.

The terrorist attacks of September 11, 2001 and afterwards have caused renewed interest in developing effective policies and strategies for evacuating densely populated areas. The large-scale evacuations caused by Hurricane Katrina and other recent hurricane events have reinforced this need. Unfortunately, the current analytical tools for dealing with such evacuations are sorely lacking, in both theory and practice. The model and its computational implementation presented in this paper attempt to close this gap and make significant progress in traffic management and hazard response systems. The overall goal of this research is to develop a fundamental understanding of the evolutionary and emergent behavior of transportation systems that are operating under emergency evacuation conditions. Initial ideas on building conceptual models of evacuation scenarios utilizing cellular automata, evolutionary computation, and advanced traffic simulators were presented in the authors' previous paper. This paper describes computational implementations of proposed conceptual models. It also discusses preliminary results of several computational experiments in which the models were used to determine robust configurations of traffic control systems operating under emergency conditions. In these experiments, optimal evacuation strategies were sought for vehicles located within a representative urban area affected by various types of terrorist attacks.

Vehicular traffic monitoring and control has had a strong infrastructure bias—data is collected centrally, processed, and then redistributed to travelers and other clients. There are several efforts to decentralize traffic monitoring by leveraging advanced local area wireless technology. This paper describes our implementation of such a traveler-centric system, called Autonet. Each Autonet client exchanges network knowledge wirelessly with other, nearby clients. It is demonstrated that knowledge about traffic state can be propagated using this system. The client programs were also used to test the actual throughput possible for messages sent from one vehicle to another using 802.11b wireless hardware. These measurements establish the maximum throughput at about 4,000 incidents for two vehicles moving in opposite directions at highway speeds.

The Operate-Design-Build-Operate (ODBC)) highway infrastructure-financing approach presented in this paper attempts to eliminate recurring freeway congestion by introducing congestion-based tolls prior to infrastructure investment with all surplus revenue dedicated for infrastructure expansion. Not only would the existing highway system operate more efficiently; but so would the expanded system. Up-front toll revenues would help pay for expensive urban freeway expansion projects, making them more financially feasible. New public-private partnership approaches are suggested that employ outcome-based contracting systems and financial incentives to maximize public mobility goals. Implementation of road pricing is faced with numerous constraints, including economic, financial, operational, political, social, and technological considerations. This paper attempts to deal with multiple considerations on a comprehensive basis, illustrating ways to accommodate high levels of system performance and economic efficiency within a broad framework of public acceptance and political reality.

Use cases specify the higher-level functional requirements of a system under design and misuse cases specify its possible misuses. Analyzing them together prevents the latter from occurring while ensuring that the former are implemented. Independently, functional fault trees (FFT) hierarchically breakdown anticipated system failures with respect to its functional architecture. This work presents an algorithm that transforms higher-level use-misuse case to FFTs, and thereby allows the application of the analytical methods available for the latter to the former. The utility of such a mechanism is illustrated by studying the security vulnerabilities that can be introduced to Positive Train Control (PTC) systems - wireless command and control systems for the safe operation of freight and passenger trains.

The countermeasure "Turning traffic must yield to pedestrians" (Manual on Uniform Traffic Control Devices (MUTCD) code sign R10-15) is used to address problems such as pedestrians not waiting for signals or an acceptable gap before crossing the streets resulting in conflicts between right turning vehicles and pedestrians. The aim of this paper is to evaluate the effectiveness of this installation. Various measures of effectiveness (MOEs) were identified. These MOEs are pedestrian / vehicle conflict, presence of pedestrians in the crosswalk during the flashing DON'T WALK and during the all red, percent of vehicles blocking the crosswalk, percent of right turn on red drivers coming to a complete stop, percent of turning drivers yielding to pedestrians, percent of pedestrians who look at the start of WALK signal for turning vehicles, pedestrian delay, and vehicle delay. The study site is Harmon Avenue / Paradise Road intersection located in the Las Vegas metropolitan area. A "before-and-after" data collection strategy was applied to test any significant difference in the identified MOEs between the two study periods. Data were collected during AM and PM peak hours. Statistical tests, test for two proportions and two-sample t-test, were used to test the significance of differences in MOEs during the two study periods. The result shows that motorists yielding behavior while turning either on red or green increased during the after study period. A significant reduction was observed in vehicles blocking the crosswalk while a significant increase in vehicles stopped completely before turning on red (P < 0.001). Average pedestrian delay increased during the after study period from 44 sec/pedestrian to 61 sec/pedestrian whereas the average vehicle delay increased from 67 sec/vehicle to 76 sec/vehicle. The installation of R10-15 effective increases the yielding behavior of turning traffic at green in presence of pedestrians which also leads to increase in both pedestrian and vehicle delay.

Adaptive Transit Signal Priority (TSP) is increasingly becoming a dominant form of preferential treatment for transit vehicles along urban arterials. The optimization of real-time adaptive traffic signals is both complex and demanding. Conventional optimization methods, including calculus-based, enumerative, and random search methods, lack both the speed and robustness needed for such applications. Parallel Genetic Algorithms (PGAs) have the potential to overcoming these obstacles. This paper applies PGAs to optimize an adaptive strategy for TSP. The results show that the PGAs can offer more efficient and faster optimization for the adaptive TSP strategy in terms of convergence speed and required computation resources, especially for the complex case of congested traffic conditions.

A useful and very significant leap forward in simulation technology is to be able to evaluate synthetic simulated conditions in realistic settings. This technology is based on Augmented Reality "AR". Augmented reality is a paradigm which creates a combination of real and virtual objects in REAL TIME (for example in movies they combine real and virtual objects but not in real time) in which the user cannot tell the difference between the real and augmented world. The AR technology can offer a very realistic environment for driving enhancement as well as driving performance testing under different scenarios. This can be achieved by adding virtual objects (people, vehicles, hazards, and other objects) to the normal view while driving an actual vehicle in a safe environment. This paper documents a summary of AR technologies on the market, illustrates the different components of the Augmented Reality Vehicle System "ARV System" built at the University of Central Florida, and discusses the validation and application of such system to traffic engineering research. The ARV System is a promising tool for transportation research for improving roadway safety and decreasing the number of traffic collisions.

Vehicles are detectable in high-resolution satellite and airborne imagery of highway segments. Given the increasing availability of such remotely sensed data and recent results on estimating annual average daily traffic (AADT) and quantifying their uncertainty, the image-based data could conceivably be useful for other transportation planning purposes. In this work we develop a Bayesian methodology for estimating statewide annually averaged origin-destination (OD) flows using link-level AADT estimates obtained from remote sensed imagery as well as from traditional ground counts. We demonstrate the methodology, which accounts for different levels of uncertainty involved, using a simulated set of statewide intercity OD flows and a highway network, and quantify the benefits of adding increased levels of remotely sensed data.

The application of Mobile Ad Hoc Network (MANET) technologies in the service of Intelligent Transportation Systems (ITS) has brought new challenges in maintaining communication clusters of network members for long time durations. Stable clustering methods reduce the overhead of communication relay in MANETs and provide for a more efficient hierarchical network topology. During the creation of Vehicle Ad Hoc Network (VANET) clusters, one vehicle node per cluster is selected as the cluster head to act as the routing node. In this paper, we propose a Compound Utility Function (CUF) clustering algorithm which takes into consideration the degree, position, velocity and acceleration of a vehicle altogether, and the invocation of this algorithm is not periodic as in earlier research, but reactive on the dynamism of the nodes, in the paper we experimentally compare CUF with Highest-Degree and Lowest-ID) algorithm. The results show that CUF gives the best cluster stability.

Since a built-in toll plaza model is not available for use with any of the widely used microsimulation models, almost every researcher independently develops a model using some programming language (VB, Java etc.) and customizes it for his or her study. Most of these models are queuing theory based input-output models, which lack the element of traffic flow theory. This paper presents a simple approach to develop a microscopic toll plaza model by customizing standard tools available in PARAMICS. A model for Holland East mainline plaza in Orlando, Florida has been developed and successfully calibrated for volume and delay values for morning peak-hour. The procedure is generic in nature, and can be used to develop a model for any other toll plaza with appropriate modifications.

Freight railroad operations involve complex classification and train formation decisions. Optimization with respect to these decisions can be quite difficult due to discrete and non-linear characteristics of the problem. The train formation plan is one of the important elements of railroad system operations. While mathematical programming formulations and algorithms are available for solving train formulation problem but CPU time required for their convergence is excessive. In this paper, 0–1 bi-level Linear Programming (BLP) is examined for obtaining good solutions with reduced complexity for block container trains formation plan (BCTFP). The minimum spending of container hour is taken as linear step function for optimization of the train formation plan. Cooperative multi-colony genetic algorithm (GA) is proposed for the solution of the block container train formation plan. Finally, the main contribution of the paper is to show the reduced complexity of the algorithm i.e. O(αn⁴lnβn²). The efficiency of the algorithm is also shown for convergence to global optimum.

Computer simulation is well accepted as a technique to support the planning of transportation systems and analyses of infrastructure capacity. With the advance of computer simulation technologies, it requires more and more precise results from the simulators. As a critical step of a simulation experiment, the initialization process and the initial state of the simulator has a profound impact on the simulation process and the accuracy of the simulation experiment. To satisfy the need of providing a more realistic initial state for the simulation, a steady-state initialization process is preferred to the traditional transient process. Based on the railroad transportation system this paper proposed a model structure for the initialization process and the criteria set for checking whether a steady-state condition is reached during the initialization.

In this paper, the authors propose a genetic algorithm approach for solving the problem of optimal location of traffic count sections. This approach aims at assessing the efficacy of a transportation based rule proposed by authors in previous studies. These approaches are tested on a real size extra-urban road network and are compared to a state-of-the art rule that is widely used in practical applications.

Incident related traffic congestion leads to enormous economic loss each year in the world. To predict the traffic situation when an incident occurs and disseminate the information to the traveling public can alleviate the traffic congestion caused by the incident. This research collected traffic incident data and traffic condition data from a highway corridor (Interstate Highway 66 Eastbound) in Northern Virginia. After pre-processing, data fusion between the two different data sources was successfully conducted and the cross reference between the incident and traffic condition data sets was set up. Based on the fused data sets, a neural network model for corridor travel time prediction in the presence of traffic incidents was developed. After the model was trained and optimized, randomly selected new data was used to test the performances of the proposed model under three different scenarios: (1) the input variables were incident related information only; (2) the input variables were current traffic condition information only; (3) the input variables included both the incident related information and the current traffic condition information. The performance indicators of the model were calculated under the three scenarios, and the statistics were compared. The results demonstrate that it is possible to accurately predict the future travel time within a corridor in the presence of traffic incidents when given sufficient amount of data. With exceptional learning ability, neural network is proven to be an effective tool in modeling this travel time prediction problem. The developed neural network delivers a good fit in most cases, indicating that it is a successful model. It is also observed that incident related information roughly dictates the trend of the impact on traffic, while current traffic condition provides a dynamic environment where the incident occurs. Addition of current traffic condition information can further improve the prediction accuracy. The predicted travel time information is valuable for traveler information systems and traffic incident management systems.

In this paper, we propose a multi-layer traffic signal fuzzy control model based on genetic algorithm (GA) for isolated intersections. The model includes three layers. The first layer is traffic demand prediction. By using a comprehensive index, traffic demand intensities (TDI), we estimate the traffic demand of each approach lane during green time. The second layer, called phase sequence fuzzy controller, is implemented to optimize signal phases according to traffic flow conditions. The third layer is green time fuzzy controller. TDI and the phase sequence are used to determine whether the current signal phase will be extended or terminated. In our research, generic algorithm will be adopted to determine the membership function of this multi-layer fuzzy control model. The performance of this control model will be compared to the model without membership function optimization at a simulated four-approach intersection, which will show the control model presented outperforms the traditional model in reducing average total delay at an intersection.

A route O-D matrix is the base data for transit line designing and optimizing. This paper presents an OD estimation model, which applies a modified BP artificial neural network (ANN) to track the weights of OD pairs within a transit route using the number of boarding and alighting passengers at each stop. The paper details the key issues associated the estimation process, including constraints for OD estimation models, design of ANN, the learning and stopping rules, and the choosing of model parameters through computational experiment. The results of a rigorous validation with on/off data from a real bus route reveal that the proposed model is quite effective and reliable in estimating the OD matrix for identification of the underlying demand pattern of a transit route.

The results from the application of two macroscopic (FREQ, KRONOS) and two microscopic (Integration, VISSIM) traffic simulation models on the same freeway network are presented. The data were collected contemporaneously on a heavily congested 15-mile section of H-1 Freeway in Honolulu, HI. The models were calibrated so that field-collected speeds at five cross sections were replicated adequately. The models were applied to evaluate a $50 million freeway widening project which was constructed between 2004 and 2006. Model predictions on the savings of the widening varied widely.

Among the many software packages for arterial traffic signal timing, PASSER, Synchro, and TRANSYT-7F are the three most popular ones in the United States. Each of them has its own pros and cons, and it is very important to choose the appropriate software for a traffic study in order to obtain the most suitable signal timing plan. Although several studies have been conducted in the past to compare the performance of these three software packages, a lot of logic changes and new features have been implemented recently and an up-to-date comparison based on the latest versions of these software packages is necessary. In this paper, we use PASSER V, Synchro, and TRANSYT-7F to develop arterial signal timing plans independently for a four-intersection arterial segment in College Station, TX. Real-world traffic volume data during morning, noon, and afternoon peak hours are used to generate the timing plans using all three packages. The performances of those timing plans are evaluated using CORSIM simulation. Delay, stops, and speed are compared based on the outputs of CORSIM simulation runs. Based on the comparison results under various traffic conditions, an assessment of performance and suitability is provided with recommendations on selection criteria.

This study provides an analysis of the methodology of using Crash Prediction models in simulation to evaluate different scenarios related to safety in dynamic traffic conditions. The analysis is carried out on a network calibrated according to the needs of the crash prediction algorithm. Multiple scenarios for ITS alternatives viz. ramp metering, variable speed limits, and their different combinations, are tested and a methodology developed for quantifying their safety benefits over multiple locations at different time intervals. The Crash Prediction models used have been developed based on I-4 loop data on a 36 mile (58 km) stretch in Orlando, Florida. A 9 mile (14.5 km) section has been calibrated using PARAMICS micro-simulation. Along with validation of volumes special emphasis has been placed on speeds and queues at the locations of interest in the network (Loop Detector stations). Also, since the crash prediction algorithms use data aggregated over 5 minute intervals, the simulation has been calibrated for Traffic Counts and Speeds at 5 minute intervals. Loop data was used to arrive at 5 minute means and variances of speeds at each of the locations in the field. The speeds in simulation were then compared to the actual scenarios to determine the best cases. The calibrated network was then used to test the safety benefits from using the different ITS strategies mentioned above. This meant testing the strategies implemented in a coordinated manner. An experimental design was used to arrive at the best possible scenario on a whole section of the freeway. The main conclusions of the study include: 1) The Crash Prediction Model used on the network calibrated at 5 minute intervals provided values closer to the actual Crash risk indices at these locations. As such any conclusions from the testing on simulation could be more effectively traced to field implementatio, 2) Safety Benefits at each location could be quantified and compared across locations and these measures could be used to determine an overall Safety Benefit Index between cases, and 3) The key effect of the ITS measures especially Ramp Metering and VSL was reducing the Variances in speed leading to lowered risk of crash.

This study presents an attempt to simulate heterogeneous traffic using the principles of cellular automata. A conventional cellular automata model for the homogeneous traffic is modified to handle heterogeneity. The principle adopted in this study is to retain the passenger car equivalent concept of HCM and the basic structure of the CA model. The modifications are done at three levels: First, the cell size is reduced to take the concept of passenger car equivalents. Second, the randomizing rule is modified to suit heterogeneous traffic conditions. Finally, the lane changing rules are modified to handle overtaking maneours and considers different types of vehicles characterized by their dynamic characteristics. The models were calibrated and validated with limited data sets and the performance of the model is presented.

The objective of the paper is to compare the performance of VISSIM, FRESIM and CELLSIM based weaving model and provide recommendations for calibration of these models. This paper analyzes a weaving section with these microscopic traffic simulation models and compares their results with the field data. The field data was collected on Baltimore-Washington Parkway, on a Type A ramp weave section, part of a cloverleaf interchange, at every second for individual vehicles using aerial photography. The comparison between field data and the results of simulation models is made for traffic volume, section density and average speed. Linear regression analyses (R-squares) are used to compare output of simulation models. The error tests are performed to evaluate which simulation model better fits the field data for a weaving section. The comparison shows that the CELLSIM based weaving model is the easiest to calibrate and validate and it provides the best fit to the field condition. The results of section density are closer to the field data compared with the results of average speed. The important variables which greatly influence the results of simulation programs are also provided in the paper.

A series of ramps could be metered to avoid flow breakdown or to lessen the extent of congestion at freeway downstream bottleneck locations. Although ramp metering might improve the traffic flow on the freeway but it increases the delays to vehicles behind metered ramps and induces ramp vehicles to divert if alternative routes can be found. The benefits to the overall system from metering ramps depend on factors including freeway demand flow rate immediately upstream of metered ramps, demand flow rates at metered ramps, ramp storage length and metering rate, freeway bottleneck section capacity (both normal and breakdown conditions) and traffic diverting rate around the bottleneck. The focus of this paper is to examine how above factors affect the operational benefits of metering ramps. In a simple transport network, various cases are designed and simulated using CORSIM micro-simulation model, while ramp-metering benefits are investigated. It is found that benefits from ramp metering control can be achieved the most if stable operations can be maintained on both freeway and diverting route. Benefits also depend on the demand flow rate that passes through the bottleneck sections under ramp metering control condition and this study suggest that selection of metering rate to minimize the probability of flow breakdown is important in achieving maximum benefits. It is also found that ramp metering may not be beneficial if diverting route operates poorly and congestion likely to propagates onto freeway.

This paper proposes a system for automatic calibration of two-lane traffic simulation models based on a genetic algorithm. The calibration process requires searching for values of selected model parameters, as to minimize the differences between simulation results and the observed data. The calibration parameters are usually related to driver behavior and because of their large number and the fact that their affects are often highly correlated they are difficult to calibrate for specific applications. In this paper the differences between the simulation results and the observed data are assessed based on performance measures that are related to the application of the model (e.g., average travel speed, percent vehicles in platoons, etc.). The proposed procedure calibrates each model using several different highway sections to find the set of calibrated parameters that can best represent a typical two-lane highway in Brazil. Data for the calibration were collected in five different locations in the state of São Paulo which were considered representative of roads in the region in terms of terrain and traffic mix. Two data sets were collected, one for model calibration and the other for model validation. The results indicate that the proposed approach is highly efficient, albeit computationally intensive; the average difference between simulated and observed traffic data was less than five percent.

The US maritime ports are vital multi-use facilities both for commercial and military transportation activities and their safety and security is essential to the nation's economic well-being; it is, therefore, critical to have the ability to conduct comprehensive safety and security analysis of the seaports. Approximately 95 percent of our nation's trade, valued at nearly $1 trillion, enters or leaves our 361 seaports annually. A key analysis tool for safety and security is advanced simulation technology that depicts (in four dimensions: 4-D) the air-land-seaport access and potential vulnerabilities in a virtual real-time format. This technology allows for the development of surface and underwater scenes in order to evaluate incident response training, conduct tabletop exercises, train management personnel and more effectively introduce transportation security systems.

This paper presents an application of a hybrid heuristic algorithm as an optimization method to find a suitable combination of micro-simulation parameters. The calibration is based on field data collected on weekday peak time of an urban downtown road network in Hefei, China. The tests indicated that the hybrid algorithm can well combine the advantage of Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. It can enhance the efficiency of convergence, and effectively reduce the likelihood of premature and converge to local optima. Meanwhile, eight important parameters and their proposed values were found which will affect the simulation accuracy specially when modeling the local peak time traffic condition.

The cell transmission model (CTM) developed by Daganzo in 1994, represents a reliable and efficient simulation environment mostly for transportation planning applications. This research study demonstrates that specific improvements can convert CTM into an operations model for large-scale traffic networks. These improvements include modification to allow for a variable cell length and adjustments to the flow advancing algorithms for a better representation of traffic flow. Evaluation and comparison of the improved version of CTM versus CORSIM has been performed using a freeway network of I-10 corridor in Baton Rouge. In addition, a sensitivity analysis demonstrates the model's good performance under the improvements developed in this study.

Short-term traffic volume forecasting is a key component of many intelligent transportation and traffic control systems. The Autoregressive Integrated Moving Average (ARIMA) models have been widely studied and used for short-term traffic volume prediction by many researchers. However, the fluctuations due to local noise make it difficult for ARIMA models to obtain high prediction accuracy. This paper investigates the use of Discrete Wavelet Transform (DWT) to improve the performance of ARIMA models for short-term traffic volume prediction. We first use the DWT to denoise the original traffic volume data such that less fluctuating data are obtained. An ARIMA forecasting model is then fitted based on the denoised data series. Real-world data from Interstate 80 in California are used to test and compare this proposed wavelet ARIMA approach with ARIMA models.

Feedback approach for real-time traffic state estimation on urban road with signalized intersection is presented. The conventional discretization of kinematic wave model for freeway based on demand and supply concept is modified in order to be capable of describing traffic state on urban road with signalized intersection and to better estimate of traffic speed. The novel filter, namely Unscented Kalman filter (UKF), is used as a correction algorithm to make the estimate to be consistent with observed traffic data. Numerical tests show superiority of the modified model over the conventional model on speed estimation and the improvement in flow estimation when combining the model with UKF.

This paper puts forward two revised approaches for estimating the real-time O-D flows in freeway corridors. The first approach is formulated in the form of parameter optimization (PO), with the objective function to minimize the absolute deviations between estimated and observed traffic counts. The second approach is formulated as a state-space model using Kalman filtering (KF), with O-D flow deviations as state vectors. These two models make full use of information from ramp traffic counts and mainline flow measurements, which are provided by traffic surveillance systems. The models also take into account the traffic delay by integrating the dynamic route travel time, which is estimated using a recursive method reported in this paper. A genetic algorithm is designed to solve the PO model, as well as an extended KF to solve the state-space model. The results of simulation examples indicate that the proposed approaches are rather promising for potential applications.

This study presents a robust algorithm that can deal with the incomplete volume information so as to significantly improve the estimation accuracy. To tackle the inevitable measurement data error or only partially available information, the proposed robust algorithm converts each piece of model input data into one interval with its upper and lower bounds best approximated from historical data or/and prior knowledge. A simulated system, the I-95 freeway corridor between I-495 and I-695, has been created to generate example data and to perform the numerical evaluation of the developed robust algorithm.

This study presents a new approach for estimating travel time information along freeway corridors, which experience recurrent congestions but have only a limited number of available detectors due to budget constraints. The proposed iterative estimation procedure, based on a set of empirically calibrated regression models, intends to rebuild the relations between travel times and accumulated flows within each segment of the target freeway corridor. To evaluate the effectiveness of the proposed methodology, this study has conducted extensive numerical experiments with simulated data from a CORSIM simulator. Experimental results under various traffic volume levels have revealed that the proposed method offers a promising property for use in travel time estimation based on sparsely distributed sensors.

Urban traffic management is important for the growth of national economy. An integrated evaluation system is first proposed to evaluate the level of traffic management of each Chinese city, as well as to guide the developing direction of Chinese urban traffic management. An evaluation model is then formulated to evaluate the national economic benefit produced by the development of traffic management. The benefit consists of three aspects: time saving, energy saving and traffic accident decrease. All key parameters in the model are discussed thoroughly. Field data of 178 Chinese cities in 2000, 2001 and 2002 are collected, and the national economic benefit of these three years are evaluated. The results show that the integrated evaluation has greatly improved the level of Chinese urban traffic management, and has produced a large amount of national economic benefit.

Generally, road users in most urban areas suffer from an increasing congestion and inconvenice. Mainly, because of the definicy in satisfying the eccentuated demand within these areas. To evalaute and enhance the traffic flow performace within Baghdad city, three arterial streets were selected. These are located within the Centeral Business district (CBD). Evaluating the existing traffic coditions has revealed that the area undergos a great deal of deterioration as represented by level of service (LOS) F. Resources constraints have obliged to investigate the traffic management meseaures first. Several operation and geometry alternatives were designed and tested. For the selected case study it appears that urgent and costy treatment would be the proper answer to the current problem.

Understanding the response and acceleration/deceleration rate of driving populations to a yellow phase is essential for estimating the dynamic distribution of intersection dilemma zones. This paper presents a video-based method for measuring driver responses during a yellow phase, including their speed evolution profile, acceleration/deceleration rate, and the approximate reaction time. Such information is critical for understanding the spatial distribution of dynamic dilemma zones and the design of strategies to improve intersection safety. This paper details the key components of the proposed system and the systematic procedures for both field operations and data extraction. The results of a rigorous validation with an advanced experimental vehicle provided by Nissan are also reported in this paper.

This paper presents the results from field deployments and tests of eight portable and semi-portable detector systems. These tests are the culmination of four years of research conducted to determine suitable, primarily non-intrusive, traffic detector systems for use by the Hawaii DOT for its traffic monitoring program. Three types of sensor installations were made, underground or sub-surface, on-ground and above ground, side-fired. All three above ground, side-fired, unintrusive sensors (SAS-1, SmartSensor and RTMS) provided reliable and reasonably accurate volume and speed data when properly installed and calibrated. Among the four on-ground sensors capable of providing vehicle classification data (in 13 classes), the Roadtrax BL piezoelectric sensor was the only one that was capable of providing adequate and reliable data. The systems tests included evaluations of telemetry and other services for data retrieval and remote diagnostics. TrafInfo's Trafmate satellite modem and digital pager, and TrafficWerks' near-real time data retrieval via cellular sub-carrier and integrated archival system performed satisfactorily and were far superior to classical Hayes modems. The research was extended to evaluate the potential for modifying signalized intersections for traffic data collection given the expanded functionality of the sensors, systems and services purchased. The remote collection of volume data without interfering operations at a busy 5-phase signalized intersection was possible from an intersection's 332 traffic cabinet with a loop actuated Type 170 controller using Canoga loop boards, and adding a TransHub and TrafInfo digital pager. Other tests showed the potential of remote collection of near-real time freeway data from legacy equipment by adding Canoga detector cards, CDMA modems and service integration with Internet data retrieval by TrafficWerks.

Traffic roundabout is a priority junction, whose capacity is typically captured by the entry capacities of individual approaches while considering the effects of conflicting flows. For a single-lane roundabout, entry capacity is traditionally analyzed based on gap acceptance where entering vehicles seek an acceptable gap among the circulating flow. An example of this approach can be found in HCM (2000). This paper provides an extension to this approach for multi-lane roundabouts. Through an analysis of lane utilization, entry capacity is estimated for each entry lane. Reserve capacity is then used as a measure to assess the overall roundabout performance. This paper also studies the sensitivity of drivers' lane choices on the overall capacity of traffic roundabouts.

Since its inception in 1969, the National Environmental Policy Act (NEPA) has provided a basis for preventing damage to the environment, stimulating the health and welfare of citizens, and enriching the understanding of ecological systems and the natural resources important to the nation. Processes and understanding for the implementation of NEPA continue to evolve and improve. In transportation facility planning, design and construction, an innovative and effective use of NEPA is referred to as Context Sensitive Solutions (CSS). CSS provides a framework for elevating the traditional NEPA - highway design process to a need based concept approach for planning and design. The CSS concept emphasizes the same concepts as the NEPA decision framework, promoting a multidisciplinary collaboration to develop transportation resources in the context of physical, scenic, aesthetic, historic, environmental, safety, and mobility constraints. CSS emphasizes in a more direct manner the need to have community insight as well as defining at the early planning stages the community values (defining the context where project is located) while striving for interdisciplinary collaboration as a mechanism for system consideration and coordination, to evaluate all aspects of purpose and need. The Arizona Department of Transportation (ADOT) and the Federal Highway Administration (FHWA) in Arizona are using some elements of CSS for analyzing successes and identifying areas of potential improvement (lessons learned) in implementation of NEPA in project specific situations by: 1) Using a documented, systematic and interdisciplinary approach; 2) Giving the environment appropriate consideration with economic and technical considerations; and 3) Including in proposals details on the environmental impacts, adverse impacts that can't be avoided, alternatives to proposed action and, consequences of taking the proposed action. Components and approaches to a CSS program within Arizona are evaluated and discussed. Elements and challenges of a particular ADOT example are presented, along with lessons learned.

This paper presents the architecture and implementation for the online deployment of DynaMIT, one of two traffic estimation and prediction systems developed under the aegis of the Federal Highway Administration, in Hampton Roads, VA. Appropriate interfaces between real-time data (sensor counts and incident information) and DynaMIT were developed and tested. In addition, the run time of DynaMIT on a 64-bit computer was tested to ensure that online deployment could meet real-time requirements.

The Traffic Operations and Safety (TOPS) Laboratory at the University of Wisconsin-Madison, in partnership with the Wisconsin Department of Transportation (WisDOT), has started development of a data management system (WisTransPortal) to facilitate continuous collection and archiving of Intelligent Transportation Systems (ITS) data in Wisconsin. The basic objective of the WisTransPortal project is to develop capabilities for a statewide ITS data hub to support multiple applications in traffic operations and safety. Those capabilities include integration, management, analysis, and dissemination of real-time and historical ITS / traffic operations data through a centralized database and communications infrastructure. The data archiving component of the WisTransPortal consists of a collection of automated services that connect to various WisDOT and other ITS data sources and prepare the data for archiving in a common relational database. The current development phase of WisTransPortal is focused primarily on replicating and integrating five existing WisDOT and regional data sources: traffic detector data, lane and ramp closure data, traffic incident data, historical crash reports, and road weather information. The design of the WisTransPortal system is based on a distributed model that separates logical software components such as Web presentation, application logic, and data management components. The benefit of this design is in the ability to support the development of a wide range of user applications and to allow, at the same time, for gradual improvements to the underlying database model. In addition to the core archiving and data clearinghouse functions of the WisTransPortal, the system is designed to provide a platform for the development of high-level software modules to support existing and new WisDOT transportation operations applications. In this paper, we describe the ITS architecture and technical organization of the WisTransPortal. The key components of the system include a direct link from the WisTransPortal system to the WisDOT fiber optic network, data management in Oracle Database 10g, and web-application development based on Java Enterprise (J2EE) technologies. An important technology direction of the project is the use of Open Source Java and J2EE frameworks such as Struts and Hibernate to facilitate robust code development, multi-layered design, and portability.

Traffic signal timing optimization and control is one of the most cost-effective ways of improving urban arterial network congestions. Actuated traffic signal control system is widely deployed around the world to provide green times where needed. The actuated control uses a pre-specified gap-out time to determine an early termination of current phase green time. However, the existing traffic signal control system relies on fixed point detectors that are installed on near stop bar and/or a few hundred feet upstream of the stop bar such that the system cannot capture entire vehicular movements along the approach link. This paper presents a dynamic gap-out concept and evaluates it using a microscopic simulation model with a COM interface. The results based on microscopic simulation runs under two one-way streets with 50 volume scenarios indicate that the dynamic gap-out can reduce vehicular delays by 20% when compared to existing regular gap-out.

Loop detectors are the most common vehicle detector for freeway traffic surveillance. Little is done in conventional practice to ensure that the detectors are functioning properly. When validation tests are applied the most common are to check that the detector seems to be counting the correct number of vehicles or that average speeds are reasonable. But such tests rely on aggregate measures across many vehicles and cannot catch many errors in occupancy, a common input to ramp metering. Our group has already shown the benefits of examining individual vehicles at dual loop detectors, this paper extends that strategy to single loops. In particular, we examine the mode on-time, where the on-time is the time one vehicle is over the detector. The strategy exploits the fact that at most locations, most vehicles will be passenger vehicles of known length range, and similarly, that most vehicles should be traveling at free flow speeds. Since on-time is the ratio of a vehicle's length over speed, the mode value at the end of a day should fall within a small range.

Providing speed feedback to motorists may improve their compliance with speed limits in work zones. Speeding has been identified as one of the major reasons for crashes in work zones and there is a need for effective speed reduction strategies in construction zones. Speed photo enforcement with speed feedback system is a promising technology for speed management in work zones. A component of the photo enforcement system is the speed feedback display device that has some effects in reducing the speeds of motorists in work zones. Data were collected on an interstate highway work zone in three phases and statistical analysis was performed to determine the temporal effect of the speed feedback device. It was found that feedback device was effective in reducing average speed by 4.4 mph in a few days after the deployment. After three weeks of operating the feedback system, the average speed was further reduced by 2.3 mph. Both these reductions were statistically significant. Thus, at this location, providing speed feedback reduced the speeds of the motorists not only immediately after the system was deployed, but also three weeks after deployment.

A recent assessment by the National Transportation Operations Collation revealed unsatisfactory performance in a number of traffic signal operation areas and an overall score of D- for the nation's traffic signals. This poor rating affects commuters nationwide and contributes to traffic congestion. Numerous traffic simulation models have been developed to analyze more complex transportation facilities. However, these models may have some shortcomings in accurately modeling complex traffic signal controller functions (which could have a drastic effect on operations). Hardware-in-the loop simulation (HILS), which replaces a simulation's signal control logic with actual controller hardware, allows advanced signal functionality to be tested and analyzed. Moreover, this simulation process opens new research areas in traffic controller functionality. This paper discusses the development of a controller interface device (CID) by the Advanced Traffic Analysis Center (ATAC) to support HILS. This CID improves upon existing devices in a number of ways, including: it works with the VISSIM traffic simulation model, uses current off-the-shelf technology, more cost effective, easy to setup and use, small in size, and its Ethernet connectivity allows it to communicate with CIDs/controllers virtually anywhere in the world.

Accurate traffic data are essential for supporting a multitude of transportation-related decisions which affect transportation system operations, management, and planning. However, the lack of accurate and timely data is often cited as a common problem for both transportation practitioners and policy makers alike. This paper discusses the temporary use of advanced sensor technologies for collecting and presenting traffic data for supporting transportation system operations, It illustrates the use of video-based and radar-based traffic detection technologies. The range of applications included in this paper comprises site-specific traffic assessment, traffic signal operations, freeway performance evaluation, and highway work zones. In addition, the paper illustrates the significant value of video data in supporting transportation decisions among policy makers and the public. The paper covers two classes of advanced traffic detection sensors; the Autoscope Video Vehicle Detection system and Wavetronix SmartSensor Traffic Detector based on Digital Wave Radar. It discusses the potential applications, typical set up requirements, types of data generated by these systems, and the performance of these technologies under different traffic collection environments. The information contained in this paper is based on various traffic operations projects conducted at the Advanced Traffic Analysis Center at North Dakota State University. These projects range from analysis of school traffic safety to evaluating traffic operations during a major multi-year construction project along Interstate Highway 29 in Fargo.

Origin-destination (OD) flows are important inputs to both off-line planning and real-time traffic management applications. Typically OD flows are estimated from time-consuming and expensive travel surveys or license plate matching studies. These estimates can be updated using link-based observations. The use of Unmanned Aerial Vehicles (UAVs) to monitor traffic networks introduces the possibility of directly determining OD flows for small networks. A methodology that combines non-overlapping imagery is developed and applied to a two-intersection network. The empirical findings show that even with short imaging time, interesting and nontrivial results can be obtained.

In October 2004, a 9 lane-km stretch of badly deteriorated truck lanes on Interstate-15 (I-15) at Devore in Southern California were rebuilt during two 9-day, one-roadbed continuous closures, and around-the-clock (24/7) construction operations utilizing the counter-flow traffic system. A preconstruction traffic sensitivity study anticipated that a nominal reduction of 10 percent of peak-hour traffic demand through the construction work zone (CWZ) would cause maximum traffic delays of as much as 95 minutes, while a 20 percent reduction would reduce delays to as little as 45 minutes. The I-15 Devore Project aimed to achieve a 20 percent reduction in traffic demand by encouraging road users to change their travel patterns; to do this the project implemented both the Automated Work Zone Information System (AWIS) and a proactive public outreach program via the Internet. This paper describes the configuration and performance of the AWIS, which provided road users with estimates of travel times through the CWZ. AWIS did this by using a built-in algorithm based on measured real-time traffic speed and occupancy data. AWIS travel times were displayed on-site on portable and permanent changeable message signs and off-site on the project web site. The AWIS information contributed to reducing more average daily traffic demand reduction (up to 19 percent) thorough the CWZ, producing high traffic volume increases to detour freeways. The operation of AWIS and the public outreach effort contributed to decreasing the maximum weekday peak-hour delay through the CWZ from 95 min. to 50 min. (on average). The outcome of this case study will help transportation agencies and practitioners efficiently design and operate AWIS for highway rehabilitation under high-traffic volume.

This paper presents a work tour mode and destination choice model within an activity-based modeling framework. The case study is Jakarta, Indonesia as one of the largest metropolitan areas in Asia. The dataset obtained from a large household travel survey provides daily travel patterns and detailed information on household socio-demographic characteristics. Eight most commonly used combinations of travel modes observed in the region are considered: drive alone, shared ride, motorcycle, taxi, motorcycle taxi, transit with motorized access, transit with non-motorized access, and non-motorized transport. In addition, eleven representative destinations are also considered for each tour and are sampled from the 336 traffic analysis zones using the stratified importance sampling method which is based on the distance as well as total jobs as an indicator of the magnitude of attraction in the destination. A wide variety of different types of variables contributed significantly to the model; including variables related to trips, activities/tours, households, individuals, and destination zones. The modeling results suggest that, in the context of Jakarta, choice alternatives, structure of the model, and key variables are quite different from those observed in the developed world. This study is part of a larger effort with ultimate goal to develop a comprehensive activity-based microsimulation modeling system for developing countries.

It has been shown that the lifestyle is a key factor in shaping household daily activity schedule, travel pattern, and trip generation. Therefore, the analysis of the choice of lifestyle is an important element in explaining household travel behavior and to address a wide range of policy issues related to transportation equity, mobility, and accessibility. This paper aims to explore typical household lifestyles in the US that are transferable to other urban areas for travel demand forecasting. Several variables that appeared to have strong relationships with the lifestyle choices were used in a principal component analysis. Using a TwoStep clustering schema, extracted factors were employed to identify typical lifestyles that shape the observed household activity patterns. Seven distinguishable clusters were identified that present national level standards of living with respect to travel behavior. Furthermore, exploring lifestyles, socio-demographics, land-use variables, and graphical representation of the household location facilitated the process of explaining each lifestyle more precisely.

The increasing development in technological capabilities and resources is pushing the transportation industry to implement new and better technologies to improve techniques for collecting travel behavior data for transportation planning applications. Travel surveys are the most common procedure used for this task and the development of more efficient ways to gather data is a major concern for transportation agencies. Improvement of data collection methods would produce great benefits for policy makers and taxpayers because of potential savings in time, cost and resources. The ever-increasing availability of the Internet creates an opportunity for a potentially more efficient, less costly means of collecting travel behavior data. An efficient Internet-based travel survey has the potential of being a very effective means of collecting data for inclusion into transportation planning models (e.g., trip generation, distribution). This project examines the usefulness of an Internet survey through a pilot study in Huntsville, AL. The study specifically addresses the data (types, range, etc.) obtained from the Internet travel survey and efficacy of applying the collected data for transportation planning applications.

This paper discusses the need for, and illustration of, a methodology for accessing community-based resources by older resident populations. The methodology involves: 1) identifying census blocks groups with older, non-institutionalized populations that may be "at high -risk" or "at moderate -risk" of a decreased ability to reside in a non-institutionalized community-based setting; 2) geocoding locations of community-based resources (e.g. health and social services, law enforcement and emergency services, and local government offices) key to the well being of older residents; 3) conducting a transportation network analysis; 4) identifying the best travel path for older residents to access various needed community-based resources. "At high -risk" and "at moderate -risk" census blocks groups are identified as a function of three demographic characteristics: the relative proportion of the population 65 and older in a census block group (1) with income below the poverty threshold, (2) with one or more disabilities, and, given those two factors, (3) living alone. Network analysis tools in standard GIS software are used to identify the best travel path to access community resources from "at high -risk" and "at moderate -risk" census block groups. The identified "best travel path" is based on travel distance, travel time, traffic volumes, number of intersections along the path, and the number of crashes involving older residents. The methodology is illustrated using data from a project that used GIS methods to indicate the physical relationship of older sub-populations to needed community resources in a section of the Las Vegas, Nevada metropohtan area.

The goal of the study is to develop a comprehensive activity-based modeling system in the context of developing countries, providing accurate estimates which are expected to serve as better inputs for evaluation of different transportation policy scenarios. The case study is Jakarta, Indonesia as one of the largest metropolitan areas in Asia. The modeling system primarily adopts a tour-based structure in which the tour is used as the unit of modeling travel instead of the trip, preserving a consistency in destination, mode, and time of day across trips. The model is developed using the available activity diary survey and household travel survey data. It is a system of disaggregate logit and nested logit models assuming a hierarchy of the model components, with three major models (choices of daily activity-travel pattern, times of day, and mode and destination) with two additional sub-models (choices of mode and destination for work-based sub-tours, and intermediate stop locations). In Jakarta, a variety of urban transportation policy scenarios such as extension of bus rapid transit, area pricing, and license plate restriction are currently being examined, discussed, or implemented. It is hoped that the new models developed in this study will contribute to better understanding of urban travel behavior and improvement of the travel demand forecasting models that will result in better evaluation of urban transportation policy scenarios in the region.

The major thrust of urbanization in India is on metropolitan cities due to their acquired status of growth magnets providing ample employment opportunities. This has resulted in simultaneous densification and expansion in spatial dimension effecting changes in the socio economic characteristics of city households, land use and travel pattern. Eventually the impact has been on urban trip rates with respect to the socio - economic stratification of urban society. The present paper displays trip rate pattern in an Indian metropolitan city of Surat in Gujarat state. The study highlights trip rate patterns for different travel purposes and modes under the influence of socio - economic characteristics with reference to city geography.

Survey data, collected to calibrate discrete choice models, are often with partially missing information on some attributes. Ways to fill in missing data are needed in order to apply standard calibration procedures; however, this may result in seriously biased estimates. In this paper, we propose a calibration approach that takes advantage of the information of the observed attributes as well as to infer the missing data statistics. In doing so, we show in a simulated dataset that the calibration can produce fairly good estimate and correctly account for tendency of bias between users and non-users.

Computer-assisted scheduling and dispatching (CASD) systems have arguably shown to facilitate operational improvements and coordination in paratransit. However, the level of realized positive impacts depends on the specifics of each implementation plan and overall deployment strategy, as well as the attitudes of potential users. In addition, the concerns of paratransit providers about implementing new software given their level of technology readiness need to be addressed. This paper examines the state of practice technology readiness of paratransit providers in the State of Illinois. It is shown that for most operators, implementing CASD systems would require developing new computer skills and training existing personnel.

In the long run, there will be changes in the population, socio-economic factors, land-use, etc. within a region. To cope with these changes, it will be necessary to improve the transportation network and alter the tolls, if any, accordingly over time. In this study, we extend the current technique in network design as a static, one-time event to an approach that explicitly captures the time dimension in the network design problem. Thus, we can make optimal decisions on project initiation time, scaling, and phasing in accordance to the design objectives. Through a small network example, we demonstrated that a well-planned, sequential upgrade plan performs better than any one-time network improvements.

In this paper, we will apply the cellular-based location technology to the urban transportation modeling system (UTMS). We first discuss existing cellular-based location technologies, and then propose a cost-effective and efficient method of using the cells in wireless cellular communications to achieve cellular location information. The cellular location data obtained through this method will be applied to the four-step procedure in UTMS. Comparisons with traditional methods will be made and the advantages of the new method will be presented.

AVL system provides a rich and extensive database that can be analyzed to improve transit management and performance. This paper discusses the procedure of using archived AVL data to extract transit performance measurements. GIS representation of AVL data and spatial-temporal data aggregation level are developed. Samples of performance output, based on AVL data collected from Broward County Transit, are presented. The research had shown that extracting performance measures from AVL data is a cost-effective way for transit service analysis.

Transport systems are subject to various types of short term incidents which affect their ordinary functions. Unlike the natural disasters in which the elements of the network would usually fail for long periods, short term incidents temporarily degrade the link capacities and often the network would not reach a new equilibrium before the normal functions return. This paper presents a method for a system level network reliability evaluation with a special attention to the role of network topology. The study is aimed at representing a framework for analyzing vulnerable networks first by introducing a reliability measure and secondly by applying a mathematical analysis method that efficiently executes the algorithm for a large network. The results of the proposed evaluation procedure for transportation system are promising. The study demonstrates that the reliability measure and network topology concepts can be included in the transportation network modeling process at a reasonable effort.

An iterative approach for bus network design is developed, which optimizes bus network and transit assignment simultaneously. The approach takes the changed stops and OD traffics into account during designing bus network. An improved ant colony optimization (IACO) and label-marking method are designed to get a Pareto optimal solution. The model and the algorithms are tested with the bus network in the city of Dalian and some conclusions are drawn.

The bus transfers synchronization problem is to find an optimal bus schedule for the entire transit system that minimizes riders' transfer time. This is a complicated problem because of the large set of binary and discrete values involved. The combinatorial nature of the problem poses a computational burden and makes it difficult to solve using classical methods. This paper presents a Genetic Algorithm (GA) approach to synchronizing bus schedules to minimize transfer times. The algorithm takes existing scheduled timetables and ridership data at transfer locations as input and addresses randomness in bus arrivals. The algorithm attempts to find an optimum solution for the bus schedule synchronization problem by shifting existing timetables. Using scheduling data from Broward County Transit, total passenger transfer times were calculated for the existing and the proposed systems. The algorithm was able to reduce system-wide transfer time by 13.4%. Results from this case study and recommendations for future research are presented.

This paper describes the results of a research project to better understand bus riders' satisfaction with and the change of the operating index of Zhongshan's Smart Bus, a national ITS demonstration project in China. The objective of the research is to let the demonstration project play an important reference role to the development of national ITS-Transit in China by collecting and analyzing data associated with bus rider' perceptions related to the use of the Smart Bus and the operating index of Smart Bus. The research methods involved a questionnaire investigation of system users and contrasting the bus' operating indexes before and after the demonstration project implemented. The overwhelming majority of the Smart Bus users survey reportes that Smart Bus could improve punctuality, safety and so on. Through contrasting the bus' operating indexes we have found that Smart Bus reduced average fuel consumption and maintenance expense. But it also shows that there are some disadvantages of the demonstration project. For example, the intelligent dispatching subsystem was not used fully.

It is known that urban density is positively correlated with transit service usage. Previous studies, however, analyzed the problem descriptively based on statistical approaches. This study seeks to derive prescriptive results of the relationship between urban density and rail service sustainability. As a start, this study considers an idealized metropolitan region with a uniform urban development density and a central business district (CBD) at its center. Trips generated from the region to the CBD are either served by the rail service or private cars, as described by a discrete choice modal split model. We study the sensitivity of urban development density on the financial sustainability of the rail service by examining the supply and demand patterns. Through the analysis, the result sheds light on the threshold urban density required, below which the service cannot be sustained without subsidy.

Advanced countries are implementing "true" High Speed Rail/HSR systems and the US is earnestly trying to also; of the many concepts proposed, the Great Lakes HSR-GL/TGV system should be the one built. Many millions of people would be able to use the system and even more benefit from it's numerous advantages. I've received positive reviews relative to this concept from academics, consultants, the transport industries and others. This is not the Midwest HSR initiative, rather, another transportation choice/mode, a separate dedicated "true" HSR / "bullet train" system. The Great Lakes to North East US regions=25% of all US inter-city travel by road and air! The benefits of the brilliant safety records(no deaths on similar decades old Shinkansen or TGV HSR systems), non-reliance on oil(electric powered), less pollution(air and noise), and less road congestion true HSR/TGV systems offers outweigh the initial startup costs and land expropriations necessary for this new transport system. Commercial jets expel thousands of gallons of petroleum exhaust into the atmosphere and create dreadful amounts of noise(TGVs uses domestic coal and other alternative electric power and is much quieter). Ohare airport generates thousands of additional traffic congesting and polluting vehicles daily-not a concern with the Great Lakes/GL/TGV central business district/CBD or current Northeast HSR corridor/NEC CBD destinations. Astoundingly!, estimates of life expectancy of people that live within several miles of a major airport is reduced by 6 or more years due to toxic airplane emissions. In Illinois, it's also reported that the air pollution created by Ohare airport alone is greater than all electric power plants in the state combined! TGV, an alternative to more airplanes/autos. This TGV system would conserve over 2 billion gallons of fuel a year(500,000 flights), relying on alternative energies. In addition, a new airport consumes double the land that the entire GL/TGV system concept would, 15,000 vs. 7,000 acres. Lastly, discount airlines with multiple airplane/airport transfers per route have as long or longer travel times in the Northeast quarter of the US than most GL/NEC HSR route travel times.

Study of the basic traffic flow characteristics and estimation of PCU values are the pre-requisites to develop standards for capacity and service-volume and to formulate effective traffic regulation and control measures. This is better done by modeling the system, which will enable the study of the influencing factors over a wide range. Computer simulation has emerged as an effective technique for modelling traffic flow due to its capability to account for the randomness related to traffic. This paper is concerned with application of a simulation model of heterogeneous traffic flow, named HETEROSIM, to derive PCU values of the different categories of vehicles, taking a stretch of an intercity road in India as the case for the study. The results of the study, provides an insight into the complexity of the vehicular interaction in heterogeneous traffic.

In this paper, we analyze the regional supply chain economic and environmental effects of shifting 10% of intercity freight carried by trucks to rail. Compared to truck, the freight-rail network provides a cost competitive, more fuel-efficient shipping alternative that can also help to reduce roadway congestion. At the same time, the increased demand for rail transportation raises the need for the expansion of the railroad system. In our case study, we assume that 10 percent of intercity freight moved by trucks in Pennsylvania is shifted to rail, and that this shift requires a 10 percent expansion of the freight-rail system: construction of new tracks, stations, maintenance and repair shops, and manufacturing of new locomotives. Using the national and regional 1997 benchmark economic input-output life-cycle assessment models (EIO-LCA and REIO-LCA) developed at Carnegie Mellon University (www.eiolca.net). we estimate the change of total supply chain economic activity, electricity and fuel use, emission of carbon dioxide and conventional air pollutants (CO, S0₂, NO_x, VOC, PM₁₀) induced by the shift between rail and truck transportation in Pennsylvania and the Mid Atlantic economic region (including DC, DE, MD, NJ, NY, and PA). We find that the shift has environmental benefits considering only the transportation sectors and their supply chains themselves. If the railroad infrastructure investment effects are allocated over a period of time, the overall supply chain effects are also positive.

The concept of integrated corridor management (ICM) has received a lot of interest recently because of the perceived potential benefits it can offer. Several regional agencies have initiated the development of ICM plans to facilitate vehicular movements more effectively within the concerned corridors. However, very few of such studies have focused on quantifying the benefits associated with implementation of ICM strategies. This study, therefore, aimed at using traffic simulation as a tool for evaluating ICM strategies to obtain a preliminary understanding of the range of benefits that ICM can offer. Two levels of ICM strategies-low and high-were evaluated against a base case for four different traffic conditions, including normal, incident, special event, and inclement weather for peak traffic volumes. Each level of ICM strategy was compared to the base case using performance indicators such as network performance and person-hours of travel time. Based on the assumptions, the results indicated reductions in travel times in the range of 20 percent from base to low ICM and 27 percent from base to high ICM.

Speed monitoring displays (SMDs) were installed in four reduced speed school zones in Utah in order to evaluate the effectiveness of these dynamic signs at improving speed compliance. Despite the data loss due to the malfunctions of the SMDs and speed data collection equipment, substantial speed data were collected, analyzed, and compared from before and after the SMDs were installed. In some cases the SMDs maintained their effectiveness at increasing speed compliance; in other cases they lost some of their effectiveness possibly due to higher percentages of commuter traffic. For the most part, these SMDs helped improve school zone safety by decreasing speeds and increasing speed compliance as manifested by the decrease in mean speed, standard deviation, 10 mph pace range and the percentage of vehicles exceeding the 20 mph school-zone speed limit.

As more and more transportation projects are located in highly congested metro settings, many projects require that adjacent utilities be adjusted to make room for new or expanded highway facilities. The adjustment of utilities prior to highway construction is a highly challenging operation from many perspectives. One major strategic approach that has emerged over the last 15 years is for state Departments of Transportation (DOTs) to combine utility adjustment work with the highway contractor's scope of work, thereby eliminating or reducing some of the associated complications and risks. While many benefits can result from this combined approach, it does have its disadvantages and own set of challenges. Hence, in the highway planning and design phase, a decision support model and a Group Decision-Support System (GDSS) were proposed to provide guidance to both state DOTs and utility decision-makers as to when the combined approach could be applied. This paper discusses the design of the decision support model and its system. With the use of GDSS, state DOTs and utility decision-makers can quickly realize the most important problems that need to be addressed and discuss solutions. A scenario using the proposed system on a hypothetical utility adjustment is presented.

Traffic flow characteristics like volume, density and speed are used to calibrate and validate current simulation models. However, these models do not validate microscopic characteristic like lane changing behavior of drivers. To get realistic results in terms of each lane of a freeway, the frequency of lane changes should be accounted for in validation of simulation models. A study of lane change frequency on a multilane freeway on I-80 California is presented in this paper. A half a mile section with five lanes, an on-ramp and a shoulder lane drop is studied. The numbers of lane changes in terms of origin and destination lanes and their percentages with respect to the entrance volume are presented for each freeway lane. Comparisons of lane change frequency towards the shoulder lane and towards the median lane are carried out. The lane changes are classified into anticipatory, mandatory and discretionary lane changes on the basis of observed data. Due to mandatory lane changes, near the vicinity of entrance gore, a zone of intense lane change has been identified. This zone is prone to higher frequency of accidents as a result of higher number of conflicting points. As the shoulder lane drops, a late mandatory merge is identified downstream of the section. This late mandatory merge accounts for about 29% of the total vehicles moving from the shoulder lane to the adjacent lane.

In this paper, C-MIXCROS and D-MIXCROS, 2 new feedback-based coordinated ramp metering strategies, that explicitly consider ramp queues, are proposed and then evaluated using both macroscopic (Rutgers Macroscopic Simulation Environment) and microscopic (PARAMICS) simulation models (on an 11-mile-long corridor of I-295 in South Jersey) under different demand conditions. In addition to the newly proposed coordinated ramp metering strategies, a well-known coordinated strategy (METALINE (5)) and 3 other local strategies (ALINEA (6), New Control (1), and MIXCROS (1)) are also implemented using the same network and results are compared. The proportional-derivative state feedback control logic and direct regulation of on-ramp queues are employed in the derivation of this new proposed coordinated ramp metering strategy. The results from the microscopic simulation are consistent with the macroscopic simulation, where D-MIXCROS and C-MIXCROS both perform better than all other control strategies tested for all the demand scenarios. The deteriorating effect of large on-ramp queues on the total travel time is especially observed for METALINE results; the total travel time is approximately 22% greater than those of C-MIXCROS results. MIXCROS (1) is also successful in keeping the on-ramp queues at a reasonable level for each ramp. However, because it is a local ramp metering strategy, coordinated versions of MIXCROS are observed to be more beneficial both for the ramp system and at the network level.

Traffic network design problem attempts to find the optimal network expansion policies under budget constraints. This can be formulated as a bi-level optimization problem: the upper level determines the optimal link capacity expansion vector and the lower level determines the link flows subject to user equilibrium conditions. However, in the context of environmental concerns, driver's route choice includes travel time as well as emission pricing. This study is an attempt to solve network design problem when the user is environment cautious. The problem is formulated as a bi-level continuous network design problem with the upper level problem determines the optimal link capacity expansions subject to user travel behavior. This behavior is represented in the lower level using the classical Wardropian user equilibrium principles. The upper level problem is an example of system optimum assignment and can be solved using any efficient optimization algorithms. Genetic Algorithm is used because of its modeling simplicity. The upper level will give a trial capacity expansion vector and will be translated into new network capacities. This then invokes the lower level with these new link capacities and the output is a vector of link flows which are passed to upper level. The upper level then computes the objective function and GA operators are applied to get a new capacity expansion vector and the process is repeated till convergence. The model is first applied to an example network and the optimum results are shown. Finally the model is applied to a large case study network and the results are presented.

Measuring airspace sector capacity is the basic foundation for researching airspace management problems. It's important to measure the effects of the flow management in airspace by the research of the right estimator. Therefore, this study is basically on the safe and effective purpose in airspace to develop appropriate measuring models of sector capacity that can predict. This study researches into those key factors that can construct mutually influential model in order to measure the capacity of airspace before/after resectorization, factors such as airspace sector characteristics, controller's workload, air traffic flow, and the procedure of air traffic control. At the same time, this research is applied with factor analysis methods to measure and analyze influence factors of sector capacity. This research proposes two-stage regression model to measure sector capacity, analyze sector capacity before/after resectorization, realize the utility of sector capacity, and reflect the issues of combination/decombination for sector airspace. The outcomes of factor analysis show the most evident factors that influence of airspace sector workloads are as follows: sector complex traffic, air traffic control, sector characteristics, time period, and inter-sector traffic. The regression outcomes show that per hour threshold of sector capacity before resectorization is more than per hour threshold of sector capacity after resectorization.

The problems of the urban poor, especially that of housing/shelter have been studied and documented by many researchers and social scientists. The state of affordable housing has been a cause for concern in the state of Illinois and especially in the six-county Chicago region. This concern stems from various factors. While studies have shown that a significant portion of a household's expenditure is dedicated to housing/shelter, it has also been documented that low-income families that make minimum-wage struggle to cope in the more expensive urban housing markets such as Chicago. This coupled with the inadequate affordable owner-occupied and renter homes is contributing to the complexity of the situation. In such a situation where both the supply and demand are not adequate, one needs to maximize the supply with ajudicious reallocation of resources (demand). Knowledge about the location of affordable housing, travel times to job destinations, along with familial factors such as the quality of local transit access, proximity of daycare centers, schools, and crime in a neighborhood are all viewed as important attributes in the relocation decision of a household. The lack of clear, transparent, and timely information at a disaggregate geography makes this task daunting. The research team at the Urban Transportation Center (UTC) at UIC developed a spatial decision support system to facilitate individuals to rank census tracts and in turn neighborhood based on their personal preferences about the various criteria mentioned in the beginning of the paragraph. This paper extends the concept of this spatial decision support system, by describing the template for a web-enabled interactive spatial decision support system (web-SDSS). The objective of this web-SDSS is to facilitate the housing relocation process for mobility counselors using decision criteria such as transit access, affordable housing, daycare, schools, crime, and jobs. These various criteria and sub-criteria are used in a hierarchical manner framed by the Analytical Hierarchy Process (AHP). The web-SDSS is intended for the primary use of mobility counselors assisting individuals/families seeking to relocate within the six-county metropolitan Chicago region. The web-SDSS will be designed using Mapserver 4.0 and linked with the decision-support system and the database housed at UTC.

The notion of traffic patterns plays a vital role in the transportation profession. Demand patterns, for example the time-series of traffic volumes experienced on highways during a.m. and p.m. peak commuter travel, are used in many applications such as performance measurement, planning and operations. However, due to limited data collection in the past for the specific purposes, there was no need for automated algorithms to identify these patterns. Now, huge data archives are increasingly available for many locations with the vast deployments of ITS systems across the nation. Taking advantage of this unique opportunity, this paper identifies and applies solutions to automatically identify traffic patterns. Clustering algorithm, logistic regression, and tree-based methods are adapted and applied. Based on application to real datasets, clustering algorithms hold high potential for use in automated traffic pattern identification.

This paper presents a summary of a process used to develop a spatial data system to help manage transportation networks. Issues related to the system architecture, and identification and integration of software and hardware elements are addressed. Commercial off-the-shelf software and hardware, along with customized interfaces are used to develop the system. Hardware considered includes portable digital assistants, Tablet PC, and Laptop. Key aspects that were considered in selecting the hardware include the ease with which they can be used in the field, their durability, effectiveness, and portability. Compatibility and ability to integrate with other application software were also critical considerations. The software is selected based on the ease with which they can be used and integrated with web authoring software, thus enhancing collection, processing, and dissemination of data. In addition, the compatibility of the software with various data formats (i.e., prior versions of databases), and the potential for such compatibility to continue into the future is also important. Software considered and tested includes Autodesk MapGuide, ESRI ArcIMS, ESRI ArcPad, and Terrasync software. Using selected hardware and software, a pilot project is used to demonstrate the use of the system to assimilate, manage, process, and communicate transportation infrastructure related data.

This paper proposes a mathematical program with equilibrium constraints (MPEC) model for the optimal contraflow operation problem that intends to determine the number of lanes of the candidate links that should be reversed their travel directions during the peak hour, while taking into account the user equilibrium (UE) behavior of drivers in route choice, so as to minimize the total travel time over a study area. The MPEC model includes a parametric variational inequality (VI) that reflects the reaction resulting from drivers with respect to a contraflow operation solution in the case of drivers follow the UE principle in route choice. To solve the MPEC model, this paper develops a genetic algorithm incorporating with a user equilibrium traffic assignment procedure. Finally, a numerical example is carried out to evaluate the proposed model and solution method.

The sensitivity analysis is a means for extracting the cause and effect relationship between inputs and outputs in the network model. The paper presents how to find a set of equilibrium path flow pattern by applying the path-based Newton method to deterministic user equilibrium (UE). The solution of path flows becomes candidate flow patterns, from which we can abstract extreme points specially used for sensitivity analysis, by using a linear programming. The numerical example shows that plural extreme patterns exist for sensitivity calculation, but the sensitivity of link flow to the input variables is still unique.

Environmental regulations have increasingly focused on mobile-source pollution and in particular on the automobile itself. More recently, focus has shifted toward the energy source used to propel vehicles. In the United States, in particular, the attainment of emissions standards at the local and regional level has led to a proliferation of special motor vehicle fuels. This has occurred because federal environmental standards must be achieved, but regional authorities are able to choose how to achieve those environmental standards. As a result, regional jurisdictions—sometimes as large as an entire state—have adopted specialized motor vehicle fuels also known as `boutique' fuels. The heterogeneity in motor fuel markets created by this proliferation of fuel types has stressed an already taxed supply infrastructure and led to generally higher and more volatile gasoline prices. In this paper we provide a brief summary overview of the North American market for motor vehicle fuels with a particular emphasis on gasoline. We execute a statistical analysis of the price effects of the proliferation of unique blends of motor fuels in the achievement of federal environmental standards. We find that the effects of adding more fuels to the supply chain increases overall fuel prices, in particular for those areas using unique gasoline blends. In addition, the proliferation of unique gasoline blends increases the likelihood of supply disruptions, particularly in cities or areas that are isolated by virtue of using an uncommon motor fuel or because they are located far from sources of supply. The higher and more volatile prices of boutique motor fuels must be balanced against the environmental benefits of cleaner burning fuels.

Recently, there has been resurgence of interest in road pricing. Along with the popularity of the concept of sustainability, transport planners no longer ignore the environmental considerations when analyzing and designing a transportation system for pricing. This paper develops an analytical model to determine the optimal tolls over time to control the traffic emissions while capturing the land use-transport interaction. To illustrate the effect of tolls on the transportation system with land use and environmental considerations, a numerical study is performed. The results show that the implementation of tolls can reduce the overall traffic emissions, generate more transit revenue and alter the travelers' choices of modes, routes, living and working locations.

Logistics public information platform involves many aspects, and it is the key of the whole logistics system efficiently that how to ensure the information flow right, just-in-time, efficiently and fluently. Based on the analysis of logistics public information system demand and the function design, this paper analyzes three modes of multi-system interconnections of logistics public information platform and points out the suitable condition of each mode. The author proposes multilevel network system more suitable for the development object and function frame of logistics public information platform in China. At the same time this paper discusses the key technology of multi-system interconnections.