Journal of Transportation Engineering, Part A: Systems

Big data and data-driven analysis could be utilized for traffic management to improve road safety and the performance of transportation systems. This paper introduces a web-based proactive traffic safety management (PATM) and real-time big data visualization tool, which is based on an award-winning system that won the US Department of Transportation (USDOT) Solving for Safety Visualization Challenge and was selected as one of the USDOT Safety Data Initiative (SDI) Beta Tools. State-of-the-art research, especially for real-time crash prediction and PATM, are deployed in this study. A significant amount of real-time data is accessed by the system in order to conduct data-driven analysis, such as traffic data, weather data, and video data from closed-circuit television (CCTV) live streams. Based on the data, multiple modules have been developed, including real-time crash/secondary crash prediction, CCTV-based expedited detection, PATM recommendation, data sharing, and report generation. Both real-time data and the system outputs are visualized at the front end using interactive maps and various types of figures to represent the data distribution and efficiently reveal hidden patterns. Evaluation of the real-time crash prediction outputs is conducted based on one-month real-world crash data and the prediction results from the system. The comparison results indicate excellent prediction performance. When considering spatial-temporal tolerance, the sensitivity and false alarm rate of the prediction results [i.e., high crash potential event (HCPE)] are 0.802 and 0.252, respectively. Current and potential implementation are also discussed in this paper.

System engineering is used to analyze traffic delay on a test track as a dynamic process using microscale time data. Each driver is required to start from rest and stop at a specific location in the shortest time possible. As such, delay is carefully defined and used as a measure of performance. The focus of the study is to identify triggering delay events and explain their origin. Road network design is treated as an explanatory variable because the test track has two cruising zones with different speed limits. In one direction, the drivers are required to accelerate at the trip midpoint, and in the other direction, decelerate at that point. Stochastic models mimic the behavior of a driver of a leading vehicle and a car follower. Study findings, based on the analysis of 400 test track runs, identify two delay event types that warrant action: (1) eliminate speed drops at a nonbottleneck location, and (2) eliminate extremely long vehicle spacing gaps found on rare occasions in cruise zones. Various tools are used to obtain a firm understanding of the who, what, when, and where of driver decision making. They include acceleration, speed, and position trajectories to clarify driver actions at startup when cruising and stopping; time-series averaging to obtain a global view of operations; and histograms to study individual behavior. Stochastic roadway network model assembly is presented. Discussions on how to improve a behavioral car-following network model (bCFNM) computer simulator, data validity testing, and model parameter calibration are presented.

Intelligent traffic control at nonsignalized intersections under a connected and automated vehicle (CAV) environment can reduce traffic congestion, vehicle emissions [e.g., carbon dioxide $({\mathrm{CO}}_2)$, carbon monoxide (CO), nitrogen oxide (NO)], but it is still not safe enough for an unpredictable emergency. For example, an accidental stop or slow down of a vehicle in the conflict area will cause secondary accidents of vehicles from other directions because they do not have enough distance to brake. Such risks have not been given sufficient attention in the literature. This paper proposed a safety guarantee mechanism of intersection control to avoid secondary accidents by adding a safe buffer area outside the conflict area of the intersection. To improve the traffic efficiency of intersections and reduce vehicle emissions under the mechanism, we proposed a heuristic vehicle trajectory planning control strategy based on vehicle kinematics. Three planning principles are applied to generate the microscopic trajectory for each vehicle: (1) the vehicle first decelerates and then accelerates before passing the intersection; (2) try not to change the running state of the vehicle; and (3) the vehicles do not collide with each other. Finally, to further improve efficiency, we proposed the concept of a dynamic safety area, which reduces the delay by self-adjusting the safety area to each vehicle’s trajectory. The paper selected three simulation scenarios of road intersections to validate our methodology. The simulation results show that the proposed control method with the safe buffer significantly reduces average vehicle delay and emissions compared to traditional signal control systems. Compared with the fixed buffer, in the dynamic safe buffer scenario, the average vehicle delay can be reduced by 66.94%, the throughput is increased by 18.75%, and ${\mathrm{CO}}_2$ emissions are reduced by 9.9%. The dynamic safe buffer can not only relax the safety concern of a nonsignalized intersection, which is not well addressed in the literature, but also has much better performance than the traditional intersection.

Two major characteristics that can impact the frequency and severity of traffic crashes are average speed and speed variance. This paper investigated these characteristics on 49 multilane highway segments in Jordan and developed prediction models for three different conditions: free (no enforcement), camera, and police enforcement. Speed data were collected during off-peak periods with fair weather conditions. Multiple linear regressions and nonlinear regressions were used to develop speed variance and average speed models. For the free condition, speed variance mainly depends on the difference between design speed and speed limit (DS-SL), with the speed variance decreasing as the difference reduces, while the average speed was positively correlated with the design speed. Camera enforcement causes speed variance and average speed to be positively correlated and linearly dependent with the speed limit. A quadratic relationship was found between speed variance during police enforcement and DS-SL, while the average speed during police enforcement depended on the speed limit and design speed. These modeling results, along with analysis of the collected data, can help operating agencies and roadway designers determine how various enforcement strategies affect average speed and speed variance and better set speed limits on existing and future roadways to improve safety.

Vehicle lane changing is one of the most important topics in autonomous driving and is the behavior most likely to cause vehicle accidents. In order to optimize the performance of vehicle lane change by controlling the speed of the vehicle, which makes the lane change safer, more efficient, and more eco-friendly, a causal inference-based speed control framework is proposed in this study. First, variables affecting the lane change efficiency are selected and their treatment effects are obtained by conducting causal effect analysis and causal refutation analysis. Second, the conditional average treatment effect is estimated by taking the safety and eco-friendly requirements into consideration and applying the double machine learning version causal forest model. Finally, a controller is built, and grid search is used to optimize lane change decision-making. By conducting numerical experiments, the average lane change duration decreased by 5.9% and average velocity increased from $7.165\mathrm{m}/\mathrm{s}$ to $7.791\mathrm{m}/\mathrm{s}$. The results show that the framework has good performance on improving the efficiency and environmental friendliness of a lane change process while ensuring safety. The proposed framework provides a new approach to speed control for discretionary lane-changing behavior. The approach can be well adapted to a vehicle-to-vehicle communication environment and is expected to be applied to vehicle-assisted driving systems in the future.

Bus terminals function as both the origins of multiple routes and charging places for electric buses (EBs) in large cities. Coordinated scheduling of routes starting from the same terminal can avoid the simultaneous charging demands of EBs, reducing queuing times for charging. The synchronous degradation in the state of health of EB batteries can also be realized with the coordinated scheduling, so as to lighten the burden of public transit corporations caused by frequent battery retirements. This study proposes a multiroute coordinated scheduling method where an electric bus is not fixed to serve on a certain route but runs different routes based on requirements. Utilizing chance constraint programming, an optimization model was formulated considering the stochastic volatility in trip travel times and time-of-use tariff, addressing objectives to minimize the average queuing time for EB charging, the difference in operating intensities, and the charging cost. The Big M method was applied to convert the chance constraint programming model to a deterministic model, which is specifically a 0–1 integer programming model, and then solved by employing the branch-and-price method. Numerical tests were carried out using data from three real EB routes. Results indicate that the proposed scheduling method can reduce the average queuing time for charging, the daily charging cost, and the difference in operating intensities while maintaining the synchronous degradation of all EB batteries.

A rectangular rapid flashing beacon (RRFB) is a pedestrian crosswalk countermeasure system that cautions drivers by providing them with real-time warning about the presence of pedestrians in an upcoming crosswalk. This paper investigated the safety effectiveness of existing RRFBs installed in the state of Florida between 2013 and 2018. Additionally, traffic volume, roadway characteristics, and land-use mix were included as explanatory variables. The study evaluated 154 treated sites with RRFB installations and 158 control sites with similar characteristics. Safety performance functions (SPF) were developed, and crash modification factors (CMF) were calculated using the empirical Bayes (EB) methodology for various crash types. A CMF of 0.31 for total pedestrian crashes was estimated, showing that RRFBs have the potential to reduce 69% of total pedestrian-involved crashes. CMF for total vehicular crashes and rear-end crashes were also investigated to ascertain whether RRFBs have adverse effects on rear-end crashes. CMF for total vehicular crashes was found to be 1.14, showing that RRFBs do not have positive effect in reducing total vehicular crashes. Similarly, it was observed that CMF for the rear-end crashes was 1.11, indicating RRFBs might have adverse effect on rear-end crashes because vehicles needs to slow down or stop due to the presence of pedestrians. CMFunctions for significant variables for pedestrian, rear-end, and total crash models were also estimated in this study. An economic analysis indicated that the implementation of RRFBs will yield a benefit to cost ratio (calculated over 3 years) of approximately 85.5 with a 4% discount rate.

Abstract

This study contributes to our understanding of the changes in traffic volumes on major roadway facilities in Florida due to the COVID-19 pandemic from a spatiotemporal perspective. Three different models were tested in this study: (1) the linear regression model, (2) the spatial autoregressive model (SAR), and (3) the spatial error model (SEM). For the model estimation, traffic volume data for 2019 and 2020 from 3,957 detectors were augmented with independent variables, such as COVID-19 case information, socioeconomics, land-use and built environment characteristics, roadway characteristics, meteorological information, and spatial locations. Traffic volume data was analyzed separately for weekdays and holidays. SEM models offered a good fit and intuitive parameter estimates. The significant value of spatial autocorrelation coefficients in the SEM supports our hypothesis that common unobserved factors affect traffic volumes in neighboring detectors. The model results clearly indicate a disruption in normal traffic demand due to the increased transmission rate of COVID-19. The traffic demand for recreational areas, especially on holidays, was found to have declined after March 2020. In addition, change in daily COVID-19 cases was found to have a larger impact on South Florida (District 6)’s freeway demand on weekdays compared to other parts of the state. Further, the gradual increase of demand due to rapid vaccination was also demonstrated in this study. The model system will help transportation researchers and policy makers understand the changes in freeway volume during the COVID-19 pandemic as well as its spatiotemporal recovery.

Practical Applications

The model framework developed in our study provides transportation planners with insight on infrastructure usage across freeways in Florida. Within this broad context, the study makes three important contributions. First, the study highlights the impact of a host of variables on traffic demand under normal conditions and the varying impact of these variables due to a shock. The model developed quantitatively identifies the varying spatiotemporal influence of variables on demand evolution in response to a shock. The proposed approach can be applied in other contexts such as a recession to reflect changes in traffic demand over time. Second, using the model for Florida provides an understanding of the locations that exhibit faster recovery rates—such as recreational locations in Central and South Florida. Thus, in the future, transportation planning can accommodate for potentially faster recovery in infrastructure usage in these locations. The finding might also be important for policy making to support various economic sectors to diversify the workforce adequately. Finally, the overall framework will also assist policy makers in assessing infrastructure usage over time under various scenarios to obtain inputs for efficient transportation asset management. An accurate estimation of demand over time while recognizing the freight share (not considered in our work) will allow evaluation of infrastructure deterioration and upkeep.

The emerging technologies of connected and automated vehicles (CAVs) and deep reinforcement learning (DRL) provide innovative methods and have a great potential for developing new solutions to improve the efficiency of several intersection systems. Based on the multisource data collected from the transportation environments, CAVs with the cooperative adaptive cruise control (CACC) system could merge into platoons and traverse the intersection quickly and smoothly. Meanwhile, the traffic information about the CAVs enables intelligent traffic signal controls with the help of DRL technologies. This research investigates the performance of a state-shared multiagent deep reinforcement learning (MADRL) controlled signal corridor with platooning-based CAVs. A corridor with seven intersections from the Ingolstadt Traffic Scenario (InTAS) in Germany is selected as a case study. The state information is shared between neighboring intersections to overcome the partial information observation of the decentralized agents in the MADRL framework. A platooning framework with specific CACC systems for the leading and following vehicles is proposed. Results indicate that the state-shared MADRL with CAV platoons could significantly decrease the total waiting time, average queue length, and total ${\mathrm{CO}}_2$ emission of the corridor by 80%, 73%, and 54%, respectively, which could be beneficial in further improving the intersection efficiency, designing future intersections, and cooperating signals and CAVs platoons.

Abstract

After the occurrence of a disruptive event in an urban rail transit (URT) network, passengers’ travel is affected, and a large number of passengers are stranded in the stations. These stranded passengers have to be evacuated urgently. With its flexible deployment, a bus bridging service has become an effective solution to evacuate stranded passengers. In order to avoid large passenger flow at stations, in addition to evacuating the static passenger flow stranded at the disrupted stations, the bridging buses need to focus on the dynamic passenger flow arriving at the turnover stations along the short-turning trains. In this paper, we propose a mathematical model to optimize the timetable and vehicle scheduling of bridging buses considering the adjustment of rail transit operations. The model aims to minimize passenger waiting time, the number of lost passengers, and the amount of bridging buses used. The model makes buses operate flexibly on different routes, taking the bus capacity and number of buses into account. By creating an improved $\epsilon$-constraint method, the Pareto front of the problem is solved using the model with a commercial solver. Finally, the accuracy and validity of the model is verified by applying an example based on China’s Hangzhou rail transit line 4. The results show that the bridging bus timetable and scheduling plan generated by the model effectively solved the problem of large passenger flow at turnover stations. In addition, compared with bridging buses with an even headway timetable, the results demonstrate that bridging buses with an uneven headway timetable, which considers coordination with rail transit, could reduce the passenger waiting time and the number of lost passengers. The model has better performance with an uneven headway timetable in the face of large passenger demand. The computational experiment shows that the total passenger waiting time and the number of bridging buses was reduced when the bus capacity was increased. However, the effect on passenger waiting time and the number of lost passengers was limited when the number of buses available at bus depots was increased.

Practical Applications

When urban rail transit operations are disrupted, passengers must rely on bridging buses to provide transportation. In order to avoid large passenger flows at small turnover stations, the bridging buses need to consider the dynamic passenger flows at both turnover stations in order to avoid large passenger flows. This paper proposes a collaborative optimization model for the dispatch routes and scheduling of bridging bus vehicles considering urban rail transit operation adjustment scenarios. Our results indicated that the model-generated bridging bus dispatch routes and scheduling plan could effectively alleviate overcrowding at turnover stations. Furthermore, compared with bridging buses with equal-interval departures, the results of our analysis demonstrate that bridging buses with non-equal-interval departures can reduce passenger waiting times. The optimization effect of nonequal departure intervals is more pronounced when passenger demand is high. Our computational experiments reveal that increasing the bus capacity can reduce total passenger waiting time, but increasing more buses at bus garages and bus transfer stations has limited impact on reducing waiting time.

Excessive crosstie wear and abrasion and ballast wear and fouling are fundamental problems contributing to inadequate railroad track performance. This adversely affects the attainment and long-term maintenance of desired track geometric requirements. The magnitudes and distribution of the stresses at the crosstie–ballast (CT-B) interface must be known to determine the stress distribution on and within the ballast. However, the track design recommendations to determine these pressures, which are largely based on a methodology from the 1980s, are currently valid for modern-day railroad applications for multiple reasons discussed in this study. This study analyzed CT-B interfacial pressure data measured on an active freight mainline in Mascot, Tennessee. Dynamic contact pressures at the CT-B interface were measured using hydraulic earth pressure cells for various wheel loads and train speeds. The test train was a Federal Railroad Administration (FRA) test train consisting of a diesel electric locomotive, a test car that had different wheel loads based on the deployable axle load, and an inspection car. Although the maximum train speed was limited to $64\mathrm{km}/\mathrm{h}$, this research found that speed variation has a minimal effect on the CT-B interfacial pressures. From the measured data, a Gaussian stress distribution equation is proposed to determine longitudinal pressure distribution transmitted to the CT-B interface for static conditions. In addition, the stress distribution along the length of a crosstie was investigated via laboratory experimentation using a half-length crosstie. As a result of the experimentation, a dimensionless trilinear approximation was developed to estimate the stress distribution along the length of the crosstie. In general, this research recommends that the longitudinal and lateral stress distributions be considered together to design a better railroad track.

The focus of car-following models is to analyze the microscopic characteristics of traffic flows, with particular attention given to the interaction between adjacent vehicles. This paper presents a systematic review of existing studies on car-following models, with an emphasis on the behavior of both human-driven vehicles (HDVs) and autonomous vehicles (AVs). We classify car-following models based on their applicable background and structure. By considering driving behavior and specific model parameters, we identify the advantages and limitations of each microscopic simulation model in terms of accuracy and continuity. We also discuss model calibration methods, stability analysis, and the impact of complex traffic environments on the car-following process. Finally, we present detailed discussions of each model’s features and provide recommendations based on reviewed works and development trends for future research, including mixed traffic flow composed of AVs and HDVs.

The US 33 Nelsonville Bypass project involved construction of a new four-lane divided highway through the Wayne National Forest in southeastern Ohio, an area high in species diversity and home to several threatened and endangered species. The project included three wildlife underpass structures to promote habitat connectivity, 16 jump out structures to allow wildlife to exit the highway right-of-way (ROW), and 2.44-m (8-feet) tall exclusion fencing along the ROW boundary to discourage entry. This paper reports the results of a comprehensive research study undertaken to determine if these features were effective at reducing collisions between vehicles and wildlife (specifically, white-tailed deer). A multi-season camera-based monitoring plan was deployed to track the movement of animals within and around the highway ROW. The results of the analysis showed that the crossing structures were heavily used and that the tall fencing was effective at excluding deer from the highway area. Approximately 43% of deer that approached the jump out structures used them to successfully exit the ROW. Analysis of traffic crash data from six years after the opening of the highway suggests that the prevalence of deer crashes on the highway is lower than other highways in the region, indicating that the mitigation features appear to be successful at reducing such collisions. Considering the additional costs of constructing the mitigation features and the economic benefits of the associated deer crash reductions, the incremental cost-benefit ratio of the complete installation is estimated to be 1.65, indicating a net positive benefit realized by the mitigation plan. The results of this study suggest that wildlife mitigation structures can be effective in supporting highway development in wildlife-sensitive areas, particularly if the placement of the structures relates to existing wildlife patterns and the features are maintained post-construction.

The global outbreak of coronavirus disease 2019 (COVID-19) has affected the urban mobility of nations around the world. The pandemic may even have a potentially lasting impact on travel behaviors during the post-pandemic stage. China has basically stopped the spread of COVID-19 and reopened the economy, providing an unprecedented environment for investigating post-pandemic travel behaviors. This study conducts multiple investigations to show the changes in travel behaviors in the post-pandemic stage, on the basis of empirical travel data in a variety of cities in China. Specifically, this study demonstrates the changes in road network travel speed in 57 case cities and the changes in subway ridership in 26 case cities. Comprehensive comparisons can indicate the potential modal share in the post-pandemic stage. Further, this study conducts a case analysis of Beijing, where the city has experienced two waves of COVID-19. The variations in travel speed in the road network of Beijing at different stages of the pandemic help reveal the public’s responses towards the varying severity of the pandemic. Finally, a case study of the Yuhang district in Hangzhou is conducted to demonstrate the changes in traffic volume and vehicle travel distance amid the post-pandemic stage based on license plate recognition data. Results indicate a decline in subway trips in the post-pandemic stage among case cities. The vehicular traffic in cities with subways has recovered in peak hours on weekdays and has been even more congested than the pre-pandemic levels; whereas the vehicular traffic in cities without subways has not rebounded to pre-pandemic levels. This situation implies a potential modal shift from public transportation to private vehicular travel modes. Results also indicate that commuting traffic is sensitive to the severity of the pandemic. This may be because countermeasures, e.g., work-from-home and suspension of non-essential businesses, will be implemented if the pandemic restarts. The travel speed in non-peak hours and on non-workdays is higher than pre-pandemic levels, indicating that non-essential travel demand may be reduced and the public’s vigilance towards the pandemic may continue to the post-pandemic stage. These findings can help improve policymaking strategies in the post-pandemic new normal.

Low Speed Vehicle (LSV)/Golf Cart (GC) communities are those modern residential areas that fully or partially allow LSVs/GCs to travel within the community. Whereas LSV/GC communities are increasing rapidly, the safety impacts of LSV/GC on the community have rarely been investigated. Also, no national uniform manuals are available for guiding the proper design of signs, pavement markings, and signals in the community. In this study, five years (2016–2020) of fatal crash data that involved LSV/GC were extracted from the National Highway Traffic Safety Administration (NHTSA) Fatality Analysis Reporting System (FARS) database to: (1) provide an overview of LSV/GC fatal crashes in the United States, (2) discuss characteristics of LSV/GC fatal crashes, and (3) delineate significant contributing factors. A case study in a large LSV/GC community in Florida was conducted to identify current issues and recommend improvements on: (1) driving behavior of LSV/GC drivers, (2) LSV/GC-related traffic control devices, and (3) LSV/GC community planning. The findings can help national and state agencies make proactive decisions to enhance traffic safety and livability for LSV/GC communities.