Journal of Traffic and Transportation Engineering-English Edition最新文献

Left-turning vehicle movement at signalized intersections with permissive left-turn phases creates safety concerns due to the higher chance of conflict with opposing-through vehicles and pedestrians. In this research, a simulation-based study was conducted to evaluate pedestrians' impact on traffic conflicts between left-turning vehicles and opposing-through vehicles (V-V conflicts) as well as traffic conflicts between left-turning vehicles and pedestrians (V-P conflicts) in the permissive left-turn phasing scenario. Intersections with different opposing-through volumes, left-turn volumes, number of opposing-through lanes, and pedestrian volumes were modeled in VISSIM, a traffic micro-simulator. The surrogate safety assessment model (SSAM) was used to estimate the number of V-V and V-P conflicts. The effect of pedestrian presence on V-V and V-P conflicts was evaluated using simulation scenarios with and without pedestrian presence. Simulation results revealed that pedestrian presence increased both V-V and V-P conflicts. As pedestrian presence increased the total number of traffic conflicts, permissive left-turn phasing processed fewer left-turn vehicles to maintain the same level of intersection safety with pedestrian presence compared to the no-pedestrian scenario. Since current left-turn phasing decision guidelines do not consider the impacts of pedestrian presence, this research quantified pedestrian-influenced and pedestrian-involved left-turn traffic conflicts to determine appropriate left-turn signal phasing decisions to ensure safe and efficient crossing of pedestrians and vehicles at signalized intersections. This simulation-based study's findings can be beneficial in the decision making of left-turn phasing selection as the before-after intersection safety performance analysis by collecting field data often resources intensive.

The traffic flow pertinent modelling is essential for distinct strategy formulations. However, the present literature illustrated some needed improvements for such models, especially those related to lane-specific flow. Given such context, this study aims to bridge the research gap by generating regression models to investigate influential factors for traffic flow based on the data collected from one multilane freeway. With the Full Bayesian specification, the hierarchical models were built by accounting for three types of random effects: the structured and unstructured spatial effects, and the one addressing the multivariate heterogeneity across multiple lanes. The endogenous relationship of traffic flow of adjacent lanes was also explored by utilizing the capability of multivariate correlation structure for simultaneous estimation of lane flow. The model estimates revealed the presence of endogeneity with statistical significance for the flow of neighbouring lanes for both directions of travel. The impact of flow was not only limited to the adjacent lanes but also to non-adjacent lanes. The multivariate specification also confirmed interdependency for lane flows. Compared to conventional approaches, the more accurate model estimation in the study indicates the advantage of incorporating the various correlation structures in the models.

This study aims to investigate the unsteady aerodynamic performance of a high-speed train's pantograph with respect to two different dome shapes and without dome under a 20° yaw angle using a delayed detached eddy simulation method. Further, the influence of the dome shape on the simulation results is determined. The accuracy of the numerical method was validated by comparing a few of the numerical results with the wind tunnel test results, and high consistency was observed. An analysis of aerodynamic forces and flow structures around the pantograph was performed. The dome had significant influence on velocity field distribution surrounding the pantograph, particularly in the wake of flow region. Compared with the case where the dome was absent, vortex intensity around the pantograph increased after installing the dome. The existence of the bathtub-type dome resulted in greater flow field disturbance and vortex strength than the baffle-type dome. Moreover, the dome considerably affected time-averaged aerodynamic coefficients and their fluctuations, especially the bathtub-type dome. Additionally, the power spectral density of the unsteady aerodynamic coefficient of each pantograph component exhibited significant peaks and typical broadband distribution characteristics.

We sought to better understand how autonomous vehicle (AV) communication strategies impact human road users’ perceptions. More specifically, we explored the impact of different external human-machine interface (eHMI) designs on task load, comfort, trust, and acceptance. To accomplish this, we created virtual reality (VR) scenarios where human participants interacted with AVs in biking, driving, and pedestrian simulators. Participants were brought back after initial testing to explore acclimation and learning effects. eHMI designs included a text-based grille eHMI, a text-based roof eHMI, a text-based driver-side door eHMI, anon-textual LED windshield strip eHMI, and a non-textual side mirror arrow eHMI. The presence of an eHMI was the strongest positive predictor of comfort, trust, and acceptance outcomes in the statistical models when controlling for all other variables. There was a clear divide between text-based eHMIs and non-text eHMIs with text-based eHMIs experiencing better perception scores. The LED Windshield experienced the worst perception scores. There were perception acclimation effects detected which were most notable for task load (which decreased over time) and comfort (which increased over time). Perception scores for the different eHMI designs tended to cluster over time. However, the acclimation effects had less of an impact than the presence of an eHMI. Perception outcomes had weaker relationships with participant characteristics than with AV characteristics. Results suggest that eHMI presence and design, AV behavior, and acclimation are most impactful in terms of perceptions.

Traffic signal control is essential to the efficiency of the road network's operation. In recent years, more and more detailed detection data provide potential data support for traffic signal control, such as license plate recognition (LPR) data. This study aims to develop a traffic signal control optimization method based on model predictive control (MPC) and LPR data. The proposed framework of a closed-loop control system is described in detail. First, the control objectives and queue prediction model for signalized intersection are determined. Then, online optimization and feedback compensation are discussed and implemented. Calculations of the arrival rate at the downstream are based on the LPR data detected at the upstream intersection, and dynamic optimization method of the offset is proposed for a coordinated control. The model is validated using the LPR data of two consecutive intersections with a traffic simulation platform. Results demonstrate that the model can restrain extreme long queuing, improve intersection capacity, and reduce intersection average delay. The developed model promotes the system operating efficiency and shows the general advantage of real-time optimization, feedback, and control. The proposed framework can be potentially applied by local traffic management centers to improve the quality of traffic signal control.

Professional drivers are more frequently exposed to longer driving distance and travel time, leading to a higher possibility of safety risk for distraction and fatigue. The widespread and common use of commercial driver monitoring systems (DMS) provides a potential for data collection. It increases the amount of data characterizing driver behavior that can be used for further safety research. This study utilized DMS warning-based data and applied an association rule mining approach to explore risk factors contributing to hazardous materials (HAZMAT) truck driver inattention. A total of 499 HAZMAT truck driver inattentive warning events were used to find rules that will predict the occurrence of driver's fatigue and distraction. First, Fisher's exact tests were performed to examine the association between the frequency of driver inattentive behavior warnings and risk factors. Second, support, confidence, and lift values were used as measurements to quantify the relative strength of the association rules generated by the Apriori algorithm. Results show that speed between 40 and 49 km/h, relatively longer travel time (3–6 h), freeway, tangent section, off-peak hour and clear weather condition are found to be highly associated with fatigue driving, while nighttime during 18:00 to 23:59, speed between 70 and 80 km/h, travel time between 1 and 3 h, freeways, acceleration less than 0.5 m/s², visibility greater than 1000 m, and tangent roadway section are found to be highly associated with distracted driving. By focusing on the specific feature groups, these association rules would help in the development of mitigating distraction and fatigue driving countermeasures and enforcement approaches.

Traffic congestion in road transportation networks is a persistent problem in major metropolitan cities around the world. In this context, this paper deals with exploiting underutilized road capacities in a network to lower the congestion on overutilized links while simultaneously satisfying the system optimal flow assignment for sustainable transportation. Four congestion mitigation strategies are identified based on deviation and relative deviation of link volume from the corresponding capacity. Consequently, four bi-objective mathematical programming optimal flow distribution (OFD) models are proposed. The case study results demonstrate that all the proposed models improve system performance and reduce congestion on high volume links by shifting flows to low volume-to-capacity links compared to UE and SO models. Among the models, the system optimality with minimal sum and maximum absolute relative-deviation models (SO-SAR and SO-MAR) showed superior results for different performance measures. The SO-SAR model yielded 50% and 30% fewer links at higher link utilization factors than UE and SO models, respectively. Also, it showed more than 25% improvement in path travel times compared to UE travel time for about 100 paths and resulted in the least network congestion index of 1.04 compared to the other OFD and UE models. Conversely, the SO-MAR model yielded the least total distance and total system travel time, resulting in lower fuel consumption and emissions, thus contributing to sustainability. The proposed models contribute towards efficient transportation infrastructure management and will be of interest to transportation planners and traffic managers.

In the paper we discuss and compare two commonly used methods of finding the shortest paths in networks, namely Dijkstra's and A∗ algorithms. We compare their effectiveness in terms of traversing road network in circumstances that require swift decision making in the event of dynamically changing road conditions on the basis of studies conducted for evacuation plans. To build a proper model of such a network, a method of appropriate edge-weighting is introduced, based on empirical data collected by other researchers. Then, we use the basics of the theory of quasimetric spaces to introduce a heuristic to such graphs, which is easy to calculate metric. The heuristic we obtain is both admissible and consistent, which allows us to use it efficiently in A∗ search algorithms. The developed application can be used in studies into evacuation from hazardous areas. In this case, optimum calculative efficiency is achievable with a simultaneous reduction of calculation time (when compared to Dijkstra's algorithm). Our application can be applied during the first stage, i.e., prior to the occurrence of a disaster, since this is an appropriate time for preparation by planning, drilling, early warning, and designating the rescue services that are to participate in the following stages.

This study develops a differential variable speed limit (DVSL) which assigns different speed limits for car and truck, and varies speed limits based on traffic conditions. The proposed DVSL algorithm changes speed limits in real time based on truck percentage and occupancy immediately upstream of the ramp and the average speed of the control road sections upstream of the ramp. DVSL algorithm also considers spatial coordination of speeds, which gradually changes the speed limits in successive road sections upstream of the ramp when the severe congestion occurs. The study tested the impacts of DVSL and three other speed limit strategies on delay and safety for a section of the Gardiner Expressway in Toronto, Canada using the VISSIM traffic simulation model. The other strategies are 1) uniform speed limit (USL), 2) differential speed limit for car and truck (DSL), and 3) USL & DSL (U&D) – i.e., USL at low truck percentage and DSL at high truck percentage. It was found that DVSL showed the lowest delays for both car and truck among the four strategies. This is mainly because DVSL increased the spacing between vehicles in the right lane upstream of the on-ramp and facilitated vehicles' merging into the mainline freeway. It was also found that DVSL showed the lowest likelihood of rear-end crash between the lead and following vehicles among the four strategies. This study demonstrates that the proposed DVSL algorithm can better control car and truck speeds to reduce delay and improve safety of car-truck mixed traffic flow on freeways.

Determining the optimal vehicle routing of emergency material distribution (VREMD) is one of the core issues of emergency management, which is strategically important to improve the effectiveness of emergency response and thus reduce the negative impact of large-scale emergency events. To summarize the latest research progress, we collected 511 VREMD-related articles published from 2010 to the present from the Scopus database and conducted a bibliometric analysis using VOSviewer software. Subsequently, we cautiously selected 49 articles from these publications for system review; sorted out the latest research progress in model construction and solution algorithms; and summarized the evolution trend of keywords, research gaps, and future works. The results show that domestic scholars and research organizations held an unqualified advantage regarding the number of published papers. However, these organizations with the most publications performed poorly regarding the number of literature citations. China and the US have contributed the vast majority of the literature, and there are close collaborations between researchers from both countries. The optimization model of VREMD can be divided into single-, multi-, and joint-objective models. The shortest travel time is the most common optimization objective in the single-objective optimization model. Several scholars focus on multiobjective optimization models to consider conflicting objectives simultaneously. In recent literature, scholars have focused on the impact of uncertainty and special events (e.g., COVID-19) on VREMD. Moreover, some scholars focus on joint optimization models to optimize vehicle routes and central locations (or material allocation) simultaneously. Solution algorithms can be divided into two primary categories, i.e., mathematical planning methods and intelligent evolutionary algorithms. The branch and bound algorithm is the most dominant mathematical planning algorithm, while genetic algorithms and their enhancements are the most commonly used intelligent evolutionary algorithms. It is shown that the nondominated sorting genetic algorithm II (NSGA-II) can effectively solve the multiobjective model of VREMD. To further improve the algorithm's performance, researchers have proposed improved hybrid intelligent algorithms that combine the advantages of NSGA-II and certain other algorithms. Scholars have also proposed a series of optimization algorithms for specific scenarios. With the development of new technologies and computation methods, it will be exciting to construct optimization models that consider uncertainty, heterogeneity, and temporality for large-scale real-world issues and develop generalized solution approaches rather than those applicable to specific scenarios.