Digital image-based automated pavement crack detection and classification technology has seen vast improvements in the recent years. Although crack lengths and widths can be evaluated using state-of-the-art software with a reasonable accuracy, no reported evidence is found in extending this technology to evaluate crack depths. As a supplement to the existing technology, additional information relevant to pavement crack severity could be revealed by the optical modeling of the image formation process and the subsequent analysis of the variation in pixel intensity profiles within images. A preliminary study was carried out to model the digital image formation of cracked concrete pavements based on the bidirectional reflection distribution function. This study was specifically focused on the optical modeling of shallow longitudinal and transverse cracks as well as joints of concrete pavements using the variation of reflection properties at surface discontinuities. Surface discontinuities were considered to be of regular geometrical shapes for simplification. The new image formation model revealed a definitive relationship among the crack widths and depths and the maximum pixel intensity contrasts seen in the images of the cracks. The model calibration involved the selection of reflection properties to match the pixel intensity contrasts across model generated images of cracks and joints against those of identical cracks formed in concrete pavements. The model predictions of crack depths were also verified using actual crack data not used in the calibration. Finally the usefulness of the calibrated model in evaluating the depths of shallow cracks and differentiating cracks from joints and other surface irregularities in concrete pavements is illustrated.

A method of deicing with carbon fiber heating wire (CFHW) buried inside concrete slab is presented in this paper, which avoids the negative effects on structure function and environment caused by traditional deicing salt. The rational interval range of CFHW buried inside concrete slab is determined by the temperature field of four heating CFHW and the minimum input powers for deicing under the conditions of different air temperatures and ice thicknesses are summarized by the laboratory experiments. Snow melts more quickly than the ice with same weight under the same input power condition by comparison the deicing processes in refrigerator. The field experiments of concrete slab performed satisfactorily: the snow on slab with the thickness of 10, 40, and 70 mm was melted in 0.75, 2.5, and 3.5 h, respectively, under an appropriate input power. It demonstrated that the electrothermal method of CFHW for bridge deck deicing is practicable.

One aspect of commercial truck safety on highways is the risk of skidding. Skidding accidents involving commercial trucks risk injury or death not only to their own occupants but also to other motorists and road users. Past research on truck skidding has been largely limited to empirical accident data analyses or experiments. Few analytical or numerical studies on the mechanisms of truck skidding on highways have been performed. This paper describes the development of a numerical simulation model that can evaluate the skid resistance available to commercial trucks on wet pavements. The model is developed using fundamental structural mechanics and fluid dynamics theories with the consideration of tire-pavement contact and tire-fluid interaction. A validation of the model was performed against experimental data found in the literature. A parametric analysis is presented in this paper to evaluate the effects of the following factors on truck skid resistance: truck speed, magnitude of wheel load, thickness of a water film on a pavement surface, and tire inflation pressure. The practical significance of the simulation model is illustrated with an analysis on the different skid resistance characteristics of trucks when unloaded and fully loaded, respectively.

Under the same applied traffic loading, time is traditionally assumed to be a function of only the vehicle speed and the depth beneath the pavement surface. Additionally, no definite loading waveform has been established and/or recommended for any one special loading situation. After numerous computations and analyses in this study, it was found that the loading time is not only a function of the vehicle speed and the depth beneath the pavement surface but is also a function of the moduli ratio between the layer of interest and the immediate succeeding layer below. Furthermore, the loading waveform changed with depth beneath the pavement surface and the moduli ratio. Based on the results of this study, new equations for more accurately determining the loading time were proposed. Additionally, the corresponding loading waveform, which is a function of the depth beneath the pavement surface and the moduli ratio, was recommended. Plausible results were also obtained when an iterated method was introduced to calculate the loading time using the equations proposed in this paper.

This paper describes a novel hybrid intelligent system approach to inversion of nondestructive pavement deflection data and back-calculation of nonlinear stress-dependent pavement layer moduli. Particle swarm optimization (PSO), a population-based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling, is fast emerging as an innovative and powerful computational metaphor for solving complex problems in design, optimization, control, management, business, and finance. Back-calculation of pavement layer moduli is an ill-posed inverse engineering problem which involves searching for the optimal combination of pavement layer stiffness solutions in an unsmooth, multimodal, complex search space. PSO is especially considered a robust and efficient approach for global optimization of multimodal functions. The hybrid back-calculation system described in this paper integrates finite element modeling, neural networks, and PSO in an efficient manner to mitigate the limitations and take advantages of the strengths to produce a system that is more effective and powerful than those which could be built with single technique. This is the first time the PSO approach is applied to real-time nondestructive evaluation of pavement systems.

The Beijing 2008 Summer Olympic Games was held from August 8 to 24, 2008, which definitely drew attention from all walks of life at home and abroad. Beijing 2008 Olympic Games was a great event, and there were huge demands for transportation services. How to coordinate the huge Olympic transportation demand of spectator and daily travel demand from local residents is one of the most important considerations for Olympic transportation agency. The Beijing Olympic transport model (BOTM) was designed to provide a tool for analyzing travel demand and forecasting traffic operation conditions during Olympic Games. This paper starts to search for the travel characteristics of local Beijing residents and spectators in game time by dividing travel demands into two parts: base demand and Olympic demand. Then, activity-chain-based forecasting methodology was used to develop model. BOTM included base model and game model by applying VISEM and EXCEL software packages. Finally, transportation demand management measures were evaluated as a case study of BOTM application.

Good visibility of the road and its surroundings is a basic requirement for safe driving. Glare caused by direct sunlight interferes with drivers’ vision performance. In high-glare conditions the visibility of objects is reduced thus increasing the probability of occurrence of incidents and crashes. Critical sections of existing or newly designed roads need to be analyzed in order to prevent these problems. The research presented in this paper provides a practical method to evaluate potential driver vision impairment situations caused by sun glare at tunnel exits. The methodology is based on the projection of the solar paths on a cylindrical chart; other geometrical variables involves in the model are also represented on the same projection. An application to an existing Spanish motorway tunnel is presented. Results show that the methodology can be successfully employed (1) to identify possible sun glare related problems; (2) to analyze different mitigation alternatives; and (3) to design physical countermeasures to avoid driver exposure to sun glare inside the tunnel and at its exit.

This work presents a microscopic stochastic simulation model that emulates the traffic movements at signalized intersections with semiactuated signal operation. This model uses the Awesim simulation language to implement and facilitate the evaluation of numerous operating scenarios associated with vehicle-actuated control strategies at isolated signalized intersections. A validation procedure was carried out, field data were collected, and delays and green times observed in the field were compared with those estimated by the simulation program. The adequacy of this model to represent real-world systems is shown. The structure of the model and the steps that were followed for its implementation are described. The relationship between simulated delays and the delays obtained from applying the Highway Capacity Manual model is analyzed. The impact of the maximum green time and the unit extension of green time assigned by the actuated system to each phase is also assessed. The model outputs include measures of effectiveness such as total vehicular delay, average green time, and average cycle length.

Path flow estimator (PFE) is a single-level network observer proposed to estimate path flows and origin-destination flows from traffic counts in a transportation network. The PFE model handles the traffic count inconsistency problem by allowing user to specify appropriate error bounds (or confidence interval) on the traffic counts. This approach, although flexible, introduces a systematic bias in underestimating the total demand when improper error bounds are specified. This paper presents an $L_{\infty }$ -norm PFE model that minimizes the systematic bias of the total demand estimate encountered in the PFE model by determining the least maximum absolute error needed to accommodate measurement errors and traffic count inconsistencies within the estimation process. A solution algorithm based on the dual formulation combined with a column generation procedure is developed for solving the proposed $L_{\infty }$ -norm PFE model. Numerical results are presented to illustrate the features and applicability of the proposed $L_{\infty }$ -norm PFE model and solution algorithm.

In this paper, a bilevel programming model for the bus frequency design is presented, which determines the optimal bus frequencies aiming to minimize the total travel time of passengers subject to the constraint on the overall fleet size of each company by accounting for the route choice behaviors of the users. The objective of the lower level is to assign transit trips to bus route network based on optimal strategy. In the upper level, bus frequencies of routes are optimized as a result of passenger assignment. An iterative approach, which consists of a genetic algorithm and a label-marking method, is used to solve the bilevel model. Finally, the model and the algorithms are illustrated with two test examples. The results show that the optimization can improve the local service level of one company, and the proper integration of several companies probably improves the efficiency of resources and the service level of the whole transit system.

To reduce traffic congestion in the United States, roadway maintenance and construction operations are widely performed during nighttime hours. This makes visibility a critical issue as workers need to be visible to oncoming traffic and heavy equipment operators in order to ensure their safety. A variety of high-visibility safety garments are available to increase the visibility of workers at night. The study presented in this paper assesses some of these garments from the perspective of drivers. The approach adopted includes the design of a field test setup in which eight safety garment assemblies were displayed in a replicated maintenance work zone. A video was created for each safety garment assembly being worn by workers to capture the approaching view of a driver entering the work zone. The videos were shown to drivers, who evaluated the visibility of the garments in pairwise comparisons. Two random effects binary probit models were estimated. One model was used to understand the characteristics that would make it more likely that the subject could detect a difference between a high-visibility vest used by the Indiana Department of Transportation workers and a competing assembly. The amount of background and retroreflective material, the driver’s age, and the speed at they which they traveled through the work zone were found to be significant in this model. A second model was a conditional one: given that a difference in garments could be detected, was the competing garment assembly more or less visible than the current safety garment used by the Indiana Department of Transportation? In this case, the mean and variance of the retroreflective material of the garment and the lighting in the work environment were found to be significant.

Pavement performance models are the key to efficient designs and effective management practices. Over the years, a number of researchers have developed reliability models to account for the uncertainty in pavement responses and utilization patterns. However, these efforts were mostly directed toward coupled capacity-demand limit state functions that do not take into account the discontinuities after the application of rehabilitation actions (e.g., overlays). The aim of this paper is to develop a pavement fragility modeling framework that accounts for the effects of the rehabilitation actions. The fragility, defined as the conditional probability of failure given a unit level of utilization, uncouples the reliability function into the capacity and demand functions, which are two separate areas of study in pavement engineering. Closed-form approximations of the fragility curves are developed to illustrate the process and allow for seamless integration in design and management optimization models.