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High volume from urban freeway off-ramps coupled with extensive traffic weaving and limited capacity at downstream intersections create major bottlenecks in urban road networks. This article presents an integrated design model to eliminate traffic weaving and to maximize the section's overall capacity by using the presignal and sorting area concept. The selection of movements controlled by the presignal, the layout of the section, and the signal timing are optimized in a uniform framework by a mixed-integer nonlinear program model. The mathematical model was linearized and solved using the standard branch-and-bound technique. Extensive numerical analysis and a case study validate the effectiveness of the proposed integrated model in improving capacity with the comparison of conventional design under various geometric configuration and traffic demand pattern scenarios. The proposed model has promising application at locations where the queuing space is long enough and the number of exit lanes is enough to receive the traffic stream from the sorting area. [ABSTRACT FROM AUTHOR]

Most previous works associated with transit signal priority merely focus on the optimization of signal timings, ignoring both bus speed and dwell time at bus stops. This paper presents a novel approach to optimize the holding time at bus stops, signal timings, and bus speed to provide priority to buses at isolated intersections. The objective of the proposed model is to minimize the weighted average vehicle delays of the intersection, which includes both bus delay and impact on nearby intersection traffic, ensuring that buses clear these intersections without being stopped by a red light. A set of formulations are developed to explicitly capture the interaction between bus speed, bus holding time, and transit priority signal timings. Experimental analysis is used to show that the proposed model has minimal negative impacts on general traffic and outperforms the no priority, signal priority only, and signal priority with holding control strategies (no bus speed adjustment) in terms of reducing average bus delays and stops. A sensitivity analysis further demonstrates the potential of the proposed approach to be applied to bus priority control systems in real-time under different traffic demands, bus stop locations, and maximum speed limits. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the wake of traffic congestion at intersections, it is imperative to shorten delays in corridors with stochastic arrivals. Coordinated adaptive control can adjust green time flexibly to deal with a stochastic demand, while maintaining a minimum through-band for coordinated intersections. In this paper, a multi-stage stochastic program based on phase clearance reliability (PCR) is proposed to optimize base timing plans and green split adjustments of coordinated intersections under adaptive control. The objective is to minimize the expected intersection delay and overflow of the coordinated approach. The overflow or oversaturated effect is explicitly addressed in the delay calculation, which greatly increases the modeling complexity due to the interaction of overflow delays across cycles. The notion of PCR separates the otherwise related green time settings of consecutive cycles into a number of stages, in which the base timing plan and actual timing plan in different cycles are handled sequentially. We then develop a PCR based solution algorithm to solve the problem, and apply the model and the solution algorithm to actual intersections in Shanghai to investigate its performance as compared with Allsop’s method and Webster’s method. Preliminary results show the PCR-based method can significantly shorten delays and almost eliminates overflow for the coordinated approaches, with acceptable delay increases of the non-coordinated approaches. A comparison between the proposed coordinated adaptive logic and a coordinated actuated logic is also conducted in the case study to show the advantages and disadvantages. [ABSTRACT FROM AUTHOR]

Most existing analytical car-following models can simulate traffic flow realistically from some aspects, such as stop-and-go, congestion, and nonlinear characteristics, but cannot predict the driving behavior and psychological process as a driver is interacting with preceding vehicles. The reason for that is the difficulty that is generated by the impact of human factor. In this paper, a completely artificial intelligence car-following model, which has no analytical model incorporated, is developed to accurately imitate a human driver. This model comprises the classic stimulus–response framework, an extensive five-layer structure, Perception–Anticipation–Inference–Strategy–Action, and a fuzzy logic-based inference mechanism. A genetic algorithm is employed to calibrate the parameters of this model. The results of experiments, which were conducted by using Next Generation Simulation (NGSIM) dataset to validate the proposed model, indicate that the vehicle trajectories simulated by this model coincide with the actual vehicle trajectories in terms of mean value and deviation. In addition, they show that the proposed model has very good stability. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive model for ranking candidate location plans of multiple urban transit hubs, which can effectively capture various aspects of concerns in the transit hub location planning process, including the overall efficiency of the transit network, the transfer intensity, the proximity to major passenger generators/attractors, the effectiveness of hub service coverage, the compatibility with land use restrictions, and the adaptability to future developable transit concepts. Grounded on an analytical hierarchy process (AHP)-based framework integrated with fuzzy logic, the proposed model offers the strengths to effectively determine the weights for multiple evaluation criteria and to synthesize the final score of each candidate plan for comparison. Results from a case study in Suzhou Industrial Park, China, reveal that the proposed model offers some promising properties for transportation planners to use in planning of transit hub locations. Comparative studies with respect to different evaluation criteria have further demonstrated the effectiveness of the proposed model in capturing the impacts of different criteria on the decision-making process. [ABSTRACT FROM AUTHOR]

Tracking the source of congestion, namely where the congested traffic flow comes from and goes to, is a key prerequisite to understanding the causes of traffic congestion and facilitates more efficient strategies. In this paper, we track the congestion source by estimating the path flow passing through the congested link. A probabilistic sensor flow assignment model is first developed to infer the whereabouts of each vehicle converging into the congestion. Unlike classical path flow estimation methods, we view path flow as the assigned results of sensor flows rather than OD flows. With this new perspective, an assigned rule, which incorporates route choice preference of drivers and spatial–temporal constraint of vehicular trajectory, is constructed to output more realistic assignments. Moreover, as this model finds most possible destination-path combinations rather than partial paths as assigned results, the complete trip of tracking vehicles, including both driving paths and ODs, can be reconstructed. With the reconstructed trips, disaggregated and hybrid path flow estimation methods are developed to track the source of traffic congestion on the bottleneck link. The open-source pNEUMA dataset is employed to test the proposed and benchmark methods. It demonstrates that our methods can produce a more realistic traffic pattern for congestion tracking. Significant improvements in estimation accuracy have been achieved with the use of sensor flow assignment model. The proposed disaggregated method has also been tested with a city-scale road network. Experiment results demonstrate that our method is more robust to the uncertainty caused by possible destinations than benchmark. • Build a probabilistic Sensor Flow Assignment model to assign detected samples onto the road network. • Develop a trip reconstruction algorithm to identify tracking vehicles' complete driving path and OD. • Propose path flow estimation methods customized for traffic congestion tracking problems. • Demonstrate the advantages of proposed methods with field-test datasets. [ABSTRACT FROM AUTHOR]

The integrated variable speed limits and ramp metering (VSL-RM) strategy is a useful method to avoid crashes and alleviate congestion on expressways. Previous studies have mostly focused on specific roadway segments and optimized them with the single goal of efficiency or safety, which does not allow for a prompt response to high-risk moving vehicle groups to improve safety and efficiency. To reduce the crash and congestion risk of vehicle groups in real time, this study developed three VSL-RM strategies with different optimization objectives based on predicted risks in a mixed traffic flow environment including connected vehicles (CVs) and regular vehicles (RVs). Due to the different behaviors of CVs and RVs under the VSL-RM control strategy, a mixed traffic METANET model was introduced to predict traffic flow parameters, e.g., volume and speed. Furthermore, two risk prediction models were utilized to predict crash risk and congestion risk based on the traffic flow parameters predicted by the mixed-traffic METANET model. The objectives of the three VSL-RM strategies were to minimize the crash risk, congestion risk, and both crash and congestion risks of the vehicle groups, respectively. These strategies were evaluated using a well-calibrated micro-simulation network. The mixed flow METANET model and the risk prediction model were validated to be consistent with the simulated traffic flow. The results demonstrated that the three strategies could simultaneously improve safety and efficiency benefits in most scenarios. However, the safety-targeted strategy provided the highest safety benefits, while the efficiency-targeted strategy provided the highest efficiency benefits. The bi-objective strategy outperformed the other two strategies in balancing the benefits of safety and efficiency. Moreover, increasing the CV penetration rate resulted in higher benefits for all three strategies. • Improve the traditional METANET model to reflect the heterogeneity characteristics of CVs and RVs under the VSL-RM strategy. • Design three different target VSL-RM optimal control strategies to enhance the safety and efficiency of expressways. • Develop a vehicle group control framework that includes the mixed traffic METANET model, vehicle group risk prediction model, and VSL-RM control strategy. [ABSTRACT FROM AUTHOR]

• Systematic review on urban traffic control under mixed traffic with CAVs. • Cover signal timing, CAV trajectory/route planning, and their cooperative control. • Summarize mixed traffic control methods at intersections with CAV-dedicated facilities. • Outline promising research directions and challenges. Efficient traffic control can alleviate traffic congestion, reduce fuel consumption, and improve traffic safety. With the development of communication and automation technologies, regular vehicles (RVs), connected vehicles (CVs), and connected and automated vehicles (CAVs) will coexist on urban roads in the near future. In this paper, we systematically review the studies on urban traffic control under the mixed traffic environment, which utilize CVs and CAVs to improve the performance of urban traffic control. We first give a glance at the evolution of urban traffic control with the deployment of CVs and CAVs to identify the important and promising research fields in the mixed traffic control. We then review the recent advances in traffic signal timing with CAV/CV trajectory data, CAV trajectory/route planning, and the cooperative control of CAV trajectories and traffic signals from the intersection level to the network level under the mixed traffic environment. Besides, we summarize two types of studies on mixed traffic control at intersections with CAV-dedicated facilities: 1) traffic signal timing and vehicle trajectory planning at intersections with CAV-dedicated lanes, and 2) deployment of CAV-dedicated facilities including CAV-dedicated lanes, links, and zones. Finally, the future research directions and challenges are discussed. We hope that this review could provide researchers with a helpful roadmap for future research on urban traffic control under the mixed traffic environment. [ABSTRACT FROM AUTHOR]