Journal of Transportation Engineering, Part A: Systems

The presence of a high proportion of vehicles in the following condition indicates that a particular roadway facility is performing poorly. Due to lack of related standard guidelines, planners and engineers in India use the same definition presented in U.S. Highway Capacity Manual regarding following vehicles, which may not be appropriate because of the heterogeneity in Indian traffic conditions. After performing graphical analysis, it was concluded that the previously used methodologies cannot be used for the present study because it could mislead the final findings. Hence, a new method was introduced to identify following vehicles in which speed difference (SD) and gap threshold value are used instead of the headway between two consecutive vehicles. It was found that after a cut-off gap threshold of 10 s, all the vehicles, irrespective of their categories, started traveling in the nonfollowing condition. A SD limit of $-4$ to $+10\mathrm{km}/\mathrm{h}$ was used to identify the following vehicles below the cut-off gap threshold value of 10 s. By using the acceptance curve method, different cut-off gap values ranging from 1.9 to 4.4 s were observed at all study sites, beyond which probability of not following (PNF) increased. After validation of the results, two-way traffic volume was identified as the dominating parameter that affects the cut-off gap value.

Crash rate data are mainly analyzed using the Tobit model. However, there are three major limitations associated with the Tobit model when it is applied to crash data: (1) the assumption that zeros are originated from the data generating process, (2) the presumption of a normal distribution of the latent response variable, and (3) the Tobit proportionality assumption. Moreover, unobserved heterogeneities are usually present, which lead to biased results in crash analyses. To address these limitations, the marginalized two-part model with random parameter specification is proposed as an alternative to the Tobit model. For comparison purposes, four models are developed: (1) Tobit model, (2) fixed parameter marginalized two-part (FPMTP) model, (3) uncorrelated random parameter marginalized two-part (URPMTP) model, and (4) correlated random parameter marginalized two-part (CRPMTP) model. The proposed methodology is demonstrated by investigating daily crash rates on a major freeway in Colorado. Model estimation results show that marginalized two-part models outperform the Tobit model, exhibiting good potential for future adoption when studying crash rates. Among the three two-part models, CRPMTP outperforms the other two, indicating that the correlated random parameter model can better capture the unobserved heterogeneities. Furthermore, the time-varying variables, including traffic and weather variables, are also found to play a significant role in crash occurrence.

This study aimed at modeling dynamics of traffic systems with integrated and cointegrated stochastic processes using multiple time series of traffic flows generated by general purpose (GP) and high-occupancy vehicle (HOV) systems. Traffic flows on these systems are likely to exhibit trending behavior due to subsystems interactions both in the short run and long run. If they share a common trend, analytic technique is required to model common underlying stochastic trend that in the long run induces equilibrium behavior in a traffic system. Vector-error correction model (VECM) is used for multiple time series variables with persistent common trends generated by integrated variables and that the linear combination of these variables reflects cointegrated processes. Using VECM model, the results showed that traffic flows on high-occupancy vehicle (HOV) and general purpose (GP) traffic systems are integrated and cointegrated, and there are short run dynamics and long run linear equilibrium relationships between system variables. These findings are useful in studying the effectiveness of implemented traffic operation strategies and/or future planning of alternative strategies depending on equilibrium state of the traffic system.

Control delay, as a primary indicator of signalized intersection performance, is commonly estimated using analytical models due to difficulty of field measurements. This paper presents a new methodology for control delay estimation using vehicle speed profiles obtained by a global positioning system (GPS) device. An algorithm developed for critical delay points identification is based on threshold values of two parameters: average acceleration and deceleration rate, and a newly established parameter, phase duration. Based on the created algorithm, a five-step procedure is suggested for individual vehicle control delay calculation. The methodology was successfully tested on 112 sample runs at two signalized intersections. Detailed analysis of the acceleration and deceleration processes has shown that these two processes have different characteristics and effects on delay. In contrast to other studies, it was found that acceleration-deceleration delay depends on variation of acceleration delay and cannot be considered as constant. A slightly lower delay value was obtained by the suggested methodology, indicating that the new parameter could eliminate delay that is not affected by traffic signals.

This research evaluated the traffic performance of two new service interchange designs—the synchronized design which is related to a superstreet intersection and the Milwaukee B design that is related to a parclo B design—as possible substitutes where existing interchanges are failing. The travel time of vehicles was selected as the criteria for operation evaluation. Over 1,000 simulation scenarios modeled the traffic operation of six different interchanges (two new and four existing interchanges) in different conditions of traffic volume, traffic distribution, left/right turning volume ratios, and heavy vehicle percentage. As simulation validation, real travel times at three interchanges were collected using probe vehicle data that compared fairly well to simulation outputs. A two-way analysis of variance (ANOVA) was conducted to examine the differences in mean values of travel time between the interchanges and to investigate the variables that affected the travel time of each design. Overall, the Milwaukee B showed the best performance among all the interchanges. The synchronized interchange looks promising as a substitute for a diamond interchange with dominant through traffic. The synchronized and diverging diamond interchanges showed almost the same results while handling moderate levels of turning volume.

Typical vehicle detection systems used in traffic signal operations are composed of inductive loop detectors. Because of costs, installation challenges, and operation and maintenance issues, many alternative nonintrusive systems have been developed and are now commercially available. Field-testing was conducted to evaluate nine alternative vehicle detection systems (four video, two radar, one thermal, and two hybrid) at the stop-bar zone of a signalized intersection under varying time-of-day and weather conditions: (1) daytime versus nighttime; and (2) favorable versus windy, rainy, or snowy conditions. The systems were set up for two detection zones: one for the through and right-turn movements and one for the left-turn lane. Based on the results of this study, no single system universally performs best. Depending on a specific time of day or weather conditions, several of the system types tested could claim that their technology outperforms all others. However, based on the percentage of false and missed detections, all of the products representing different system types exhibit opportunities for improvement and enhancement.

Braess paradox is a well-known paradox in transportation researches. In urban cities, there are many different kinds of complex road networks. Unavoidably, some of them fall into the Braess paradox and it is hardly realized. In this paper, two proposed approaches are applied to find and avoid the Braess paradox in urban road networks. With the first approach, the links that cause the Braess paradox in the urban road networks with the current origination-destination (OD) matrix can be tested. The other approach is to calculate the range of the OD flows that makes these links fall into the Braess paradox. Unlike other approaches proposed in literature, this proposed approach can figure out the range of traffic demands in the networks with multiple OD pairs. Moreover, by applying these two approaches, the authors design a traffic management called link restriction which can easily figure out which link should be closed down temporarily and when to resume operation to reduce the total travel times of networks with flexible managements.

There are few research studies that addressed the impact of reduced visibility due to fog using real-time data. It is thus meaningful to conduct further investigation that can clearly describe the changes in driving behavior and traffic parameters under foggy conditions using real-time traffic and weather data. Field traffic and weather data were collected in this research and fog cases were selected and analyzed by comparing them with clear cases to identify the differences in traffic characteristics under the two different situations. Moreover, vehicles were classified into two types (i.e., passenger cars and trucks) to identify whether the impact of reduced visibility due to fog on traffic varies depending on vehicle types. Afterward, the traffic parameters under different visibility classes and the effects of reduced visibility on different lanes were analyzed using ANOVA. Finally, a matched case–control logistic regression model was applied to further confirm the relationship between traffic parameters and reduced visibility due to fog. It was concluded that the impact of fog on traffic varies by vehicle types and lanes. The impact was also different by visibility classes. The impact of reduced visibility on passenger cars is more significant compared with that on trucks. The effect of reduced visibility on traffic parameters is more significant on inner lanes than outer lanes. Under these weather conditions, drivers should pay more attention to the traffic because higher headway variance is more likely to result in the crash occurrence. The matched case–control logistic regression modeling results indicate that larger average headway, speed variance, headway variance, and occupancy were related to the increase of the likelihood of a reduced visibility. The results would be helpful to understand the change of traffic status and investigate the potential factors for higher crash frequency under foggy conditions.

This paper addresses signal transitions due to outsized or oversized pedestrian timing. This form of transition happens at coordinated intersections when the side-street required green time is less than the required pedestrian time (RPT) to cross the main street if there is a pedestrian call. To have a quantitative tool to analyze different transition options, mathematical models of transition methods were developed. Three new transition methods were also suggested. With this model, practitioners simply input the common timing parameters such as cycle length, vehicle and pedestrian volume, and RPT. The model will then give a recommendation based on the overall system delay or arterial delay and is expected to provide much improved recommendations when dealing with pedestrian crossings at coordinated signal systems.

Cycling has been considered as a healthy, environmentally friendly, and economical alternative mode of travel to motorized vehicles (especially private motorized vehicles). However, bicycles have often been neglected in the transportation planning and travel demand forecasting modeling processes. The current practice in modeling bicycle trips in a network is either nonexistent or too simplistic. Current practices are simply based on the all-or-nothing (AON) assignment method using single attributes such as distance, safety, or a composite measure of safety multiplied by distance. The purpose of this paper is to develop a two-stage traffic assignment model by considering key factors (or criteria) in cyclist route choice behavior. As an initial effort, the first stage considers two key criteria (distance-related attributes and safety-related attributes) to generate a set of nondominated (or efficient) paths. These two criteria are a composite function of subcriteria. Route distance consists of link distances and intersection turning penalties combined to give the distance-related attribute, while route safety makes use of the bicycle level of service (BLOS) measure developed by the Highway Capacity Manual (HCM) to determine the safety-related attribute. Efficient paths are generated based on the above two key criteria with a biobjective shortest path algorithm. The second stage determines the flow allocation to the set of efficient paths. Several traffic assignment methods are adopted to determine the flow allocations in a network. Numerical experiments are then conducted to demonstrate the two-stage approach for bicycle traffic assignment. Overall, the results of the Winnipeg network demonstrate the applicability of the two-stage bicycle traffic assignment procedure with the flexibility of using different criteria in the first stage to generate efficient paths and different traffic assignment methods in the second stage to allocate flows.

This paper addresses the issue of a bus model with rectangular service zones and the development of a new approach to find the criterion in which the existence of the optimal solution for headway and route width is guaranteed. A formulated solution is derived for headway that will be useful when establishing relations among variables and predetermined parameters. These relations will help researchers decide the importance of those predetermined parameters, thus giving them the ability to easily and effectively handle transit network design problems.

Congested cities rely on public bus transportation services for a high share of its urban mobility. Unplanned disruptions to these services, their availability, and response time have significant impacts on passengers’ satisfaction. Optimally allocating buses to depots can undermine the impact of these disruptions as well as significantly reduce operational costs. Bus depots should be equipped with the necessary tools to serve and maintain the allocated buses. This study optimizes the assignment of buses to depots while taking into consideration the availability of maintenance resources. A mixed-integer linear program (MILP) formulation was developed to reduce the overall operational cost, optimize the assignment of buses to depots, and determine the optimum allocation of the maintenance centers. The model was validated by comparing computed results with those published in the literature for Vancouver Regional Transit System. Then, the model was further used to analyze another public transportation system. Results reveal that 17% savings in deadhead kilometers cost per bus can be achieved if limitations in the maintenance resources are considered at the planning stage.