The collection of traffic data in the field, both from manual and automated methods, is vital to the management and maintenance of the transportation infrastructure. Historically, many methods for manual data collection have been utilized, ranging from pencil and paper to portable computers. At present, with advances in computing performance and technology over the last several years, speech recognition technology has emerged as a potentially viable tool for data collection, a tool with very distinct advantages over some existing methods of manual data collection. This paper discusses the development and testing of a speech recognition enabled software program for data collection in the field. The specific program developed was designed to enable the collection of vehicular license plate data from roadside observation locations. This particular application proved to be a very demanding test for speech recognition due to several factors—loud background noise conditions at roadside locations, the need for high accuracy rates, and the need for sufficient computational speed to handle potential high sampling rates. Many field tests were conducted and key measures of effectiveness were compared between the speech entry method and a method utilizing portable computer keyboard-based data entry. Overall, the results showed promise that speech recognition could become a method that will improve the ease and efficiency of collecting transportation data in the field, which should also lead to increased data quantity and quality.

One of the key functions of an effective Advanced Traveler and Management Information System is the ability to model short-term predictions of traffic conditions on major freeways and arterials with reasonable accuracy. The impetus of forecasting traffic information rather than relying on real-time information is to allow the traveling public to be proactive in their travel decisions at both pretrip planning stage and en route. Traffic information is usually reflected by the traditional performance measures such as travel times and delays. Such measures are well perceived by the traveling public and, hence, are adopted in most short-term traffic prediction studies. When disseminated to the traveling public, forecasted travel time information will help the travelers make better trip decisions in terms of departure time, route selection, and mode selection, when transit becomes competitive. Several traffic prediction models have been developed in the past using a wide spectrum of modeling techniques. Most studies showed that prediction accuracy is often compromised by the underlying mechanism of prediction methods more than other influencing factors. The main objective of this study is to investigate the factors that have a significant impact on the forecasting accuracy of travel times using a nonlinear time series traffic prediction model. The ultimate goal is to identify the operational settings and the anticipated forecasting accuracy of the model before it can be fully implemented. The study was conducted using extensive amount of real-time data collected from the 62.5 km corridor of Interstate-4 in Orlando, Florida. Various scenarios were generated from a combination of model parameter settings and different traffic conditions in order to test the performance of the model. Relative travel time prediction errors were used as a measure of performance and were based on speed predictions at freeway detector stations. Statistical analysis was conducted to identify the parameters with a statistically significant effect on the model's performance.

Public transit agencies in the United States have been under mounting pressure to reduce operating costs and subsidies and to improve productivity, ridership, and service levels. Given the growing emphasis on quality and efficiency, there is an impending need to develop a formal procedure to assess transit performance. Considerable disagreement exists among experts about the most effective way to measure transit performance and the degree to which performance may be used as a basis for funding allocation. The purpose of the project that serves as the basis of this paper was to develop a performance assessment tool for Michigan transit agencies that receive operating assistance from the Michigan Department of Transportation (MDOT). In this paper, the writers present the application of two techniques: the Analytic Hierarchy Process (AHP) and the Goal Achievement Technique (GAT) for evaluation of one of the five peer groups specially created for the project. The paper concludes that both AHP and GAT are viable tools for conducting transit performance assessment. Both of them are capable of using a wide range of performance data and developing a composite performance index for each transit agency. The writers, however, recommend AHP as a better multicriteria assessment tool because of its stronger mathematical foundation, its ability to gauge consistency of judgments, and its flexibility in the choice of ranges at the subcriteria level.

A two-stage procedure to optimally allocate limited resources for the dual purpose of purchasing new transit buses and rebuilding existing buses is presented in this paper. In stage 1, estimates are derived for the maximum investment that can be justified for rebuilding buses that have fulfilled their service life expectations. In stage 2, optimization by linear programming is used to estimate the number of buses in each category. Two optimization approaches are presented. In approach 1, the objective is to maximize the weighted fleet life, subject to budgetary and demand constraints. In approach 2, the objective is to minimize the total capital investment in the fleet, subject to minimum fleet life and demand constraints. Case studies are presented to demonstrate the application of two approaches. The proposed procedure can be used by state Departments of Transportation to allocate capital dollars among various transit agencies for the dual purpose of purchasing new buses and rebuilding older buses. While it is desirable to replace all the old buses with new ones, budgetary constraints often make it impossible to fund the purchase of all new buses. The proposed approach provides a tool to identify the “next best solution,” a solution that will result in an optimum combination of new and rebuilt buses that will satisfy the requirements of the minimum fleet size as well as the budgetary constraints.

In this paper, results of an evaluation of an innovative strategy for minimizing bus bunching are presented. The strategy involves continuous monitoring of bus positions along their route and controlling bus spacing when necessary by employing signal priority and bus holding actions. The evaluation exercise aims to establish traffic and bus operating conditions when the strategy performs best. A simulation model of a hypothetical bus route developed using a PARAMICS (Parallel Microscopic Simulator) is used for this purpose. Bus bunching, measured in terms of disruptions to headways, is found to increase with decreases in design headway, increases in route length, and increases in traffic congestion. When bus spacing is controlled, a significant improvement is seen in cases of shorter design headways of 5 and 10 min, resulting in 80% of cases having more than 10% reduction in excess waiting time and 60% of the cases having more than 20% reduction in excess waiting time.

This research assessed the feasibility of using trucks in an existing Los Angeles Freeway Service Patrol (FSP) fleet as probe vehicles to measure ambient speed on freeway segments. Floating car speeds were the basis of comparison with other speed data. Loop detector-based segment speeds and FSP truck speeds were compared against a common baseline. Unfortunately, FSP truck speed data available from the existing system are of insufficient quality to estimate ambient speeds. Comparisons between floating car and FSP truck speed records reveal that FSP truck speed is a poor measure of floating car (ambient) speed. FSP truck speed records underestimate floating car speeds overall, and covariance between the two is weak.

Microsimulation modeling is an emerging approach to activity-based travel forecasting. Household automobile-ownership models are being included in microsimulation travel-demand models more. Implicitly, vehicle price is an important attribute of vehicles in all automobile-ownership models. In order to update prices at each point of time within the simulation, a modeling tool is required to estimate the price of each vehicle at any time. This paper develops a hedonic price model to estimate the expected price of vehicles to be used in a comprehensive urban-transportation modeling system. In this study, the use of a linear hedonic price model was investigated in terms of its application to the market price of automobiles.

A flexible pavement life-cycle model has been developed to yield an optimum maintenance and rehabilitation plan. The model incorporates into the optimization process both performance and cost associated with a life-cycle analysis period for a given pavement structure (project). A single life-cycle indicator called “life-cycle disutility” has been introduced and defined as the ratio of cost to performance. The optimum plan is the one associated with the minimum life-cycle disutility value. The model evaluates several potential maintenance and rehabilitaton plans generated according to two defined decision policy options. The first decision policy option requires a fixed analysis period, whereas the second one involves a variable analysis period. Both options require a specified number of major rehabilitation cycles. Pavement life-cycle cost includes initial construction, scheduled major rehabilitation cycles, and routine maintenance and added user cost. Pavement life-cycle performance is defined as the area under the life-cycle performance curve either generated from actual pavement distress data or based on an incremental analysis of the American Association of State Highway and Transportation Officials basic design equation of flexible pavement.

The precision of pavement moduli values is affected by the selection process of the signals from the seismic pavement analyzer (SPA). If improper signals were selected, moduli values from the SPA were lower than those obtained from the falling weight deflectometer (FWD). The precision was determined using a 95% confidence interval. The discrepancies in the seismic testing results were attributed to the wide range of irregularities in some of the SPA signals. Based on the crossing analysis of a narrow-band process, the irregularity factor α was introduced in this study to evaluate the degree of distortions in the seismic signals. A threshold of 0.8 was determined to be the minimum acceptable limit for α. Lower values of α associated with irregular signals caused drastic increases in the ratios of the confidence intervals between the SPA and FWD results. The need for having a rigorous calibration protocol for the SPA has been emphasized. Using the proposed method enhanced the quality of the seismic analysis for pavement evaluation.

A great deal of research has been carried out over the past few decades into developing models for estimating road transport costs derived from maintenance investment policies. These existing models attempt to estimate the best investment strategy for fixed budgetary resources. However, they do not take into account the effects derived from work productivity increments arising from new contracting formulas. This paper describes a simulation model for estimating the overall benefit derived from the use of different systems for financing road maintenance as well as the productivity achieved in the management of the work. In order to validate the model, the paper ends with an application on a secondary road in Spain. The results of the simulation show that work productivity is extremely important to the optimal level of investment. Moreover, the simulation provides some relevant conclusions in respect to the financing mechanisms tested.

Pavement management systems (PMSs) are increasingly important tools in the decision-making processes regarding the maintenance and rehabilitation of road pavements. A new optimization model to be used within network-level PMSs is presented in this paper, together with a genetic-algorithm heuristic to solve the model. The model assumes decisions to be segment linked, thus overcoming the principal drawback of the widely used Arizona PMS. The objective of the model is to minimize the expected total discounted costs of pavement maintenance and rehabilitation actions over a given planning time span, while keeping the network within given quality standards. The model was applied to three test problems involving the road network of Coimbra, the third-largest Portuguese city. The results obtained for these problems clearly indicate that the model is a valuable addition to the road engineer’s toolbox.

The deterioration of flexible pavements due to low-temperature cracking is a significant and costly problem in the state of Nevada. The Nevada Department of Transportation initiated several research efforts aimed at exploring Nevada’s problem with this distress. The research evaluated several newly developed low temperature performance tests under Nevada’s conditions. The goal of the research was to determine the applicability of the tests for characterizing the low-temperature response of Nevada’s asphalt binders and hot-mixed asphalt (HMA) mixtures. This paper summarizes Nevada’s experience with the Strategic Highway Research Program low temperature tests and specifications, highlighting the effectiveness of the Superpave Performance Binder Grading System and the thermal stress restrained specimen test (TSRST). The contribution of asphalt aging to Nevada’s cracking problem is also included in the paper. An investigation of the Superpave Performance Graded Binder tests has determined that the bending beam rheometer and the direct tension test correlate very well and it may not be necessary to run both tests, as they are set up in the current Superpave specifications. The TSRST appears to provide the greatest value for evaluating low-temperature properties of HMA mixtures. Findings from the research indicate that there are some significant correlations between the low-temperature properties of asphalt binders and HMA mixtures if the mixtures are aged appropriately. This emphasizes the need to implement the appropriate conditioning procedure when low-temperature cracking is used as part of the mix design and evaluation process. On the other hand, the research showed that, when using polymer-modified asphalt binders, the low-temperature grade of the asphalt binder may be conservative enough that testing of the HMA mix may not be necessary.

Research on short-term traffic conditions prediction has been largely concerned with parameters such as flow, occupancy, and speed, ignoring at the same time predictions during congestion, a period when predictions are needed the most. Stemming from the practical need to predict traffic parameters during congested periods, this paper uses the principles of duration modeling to address an important question: given the onset of congestion, how long will it last? As such, the goal of this paper is to propose an approach for estimating the duration of congestion on a given road section and the probability that, given its onset, congestion will end during the following time period. The results indicate that the Loglogistic functional form best describes congestion duration, and that the probability of congestion ending within a specified time period is likely if it has lasted up to approximately 12 min (with a peak at 6 min). Further, it was found that if congestion lasted over 21 min it was probably caused by something external to the traffic system events.

Pavement surface texture greatly contributes to tire-pavement skid resistance. Pavement surface irregularities are continuous from pavement macrotexture to pavement microtexture. Their pattern is also roughly repetitive. This existence of self-similarity encourages an attempt to approximate the pavement surface texture with fractals in order to take advantages of fractal modeling. The mathematical background is based on $C^0$ and $C^1$ fractal interpolation curves. Through this approximation, the procedure of pavement surface wear is evaluated, in respect to its effect to skid resistance values. Fractal potential is simultaneously tested in representing pavement surface contamination. A qualitative relationship between the skid resistance values of characteristic pavement surface texture profiles and the fractal dimension of their interpolation function is recognized. The whole attempt yields encouraging results, although it is apparent that further research is required.