Development And Evaluation Of Model Based Adaptive Signal Control For Congested Arterial Traffic
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Development and Evaluation of Model-based Adaptive Signal Control for Congested Arterial Traffic
Under congested conditions, the road traffic states of different arterial links will interact with each other; therefore, it is necessary to understand the behavior of traffic corridors and to investigate corridor-wide traffic coordinated control strategies. In order to achieve this, traffic flow models are applied in signal control to predict future traffic states. Optimization tools are used to search for the best sequence of future control decisions, based on predictions by traffic flow models. A number of model-based adaptive control strategies have been presented in the literature and have been proved effective in practice. However, most studies have modeled the traffic dynamic either at a link-based level or at an individual movement-based level. Moreover, the efficiency of corridor-wide coordination algorithms for congested large-scale networks still needs to be further improved. A hierarchical control structure is developed to divide the complex control problem into different control layers: the highest level optimizes the cycle length, the mid layer optimizes the offsets, and the Model Predictive Control (MPC) procedure is implemented in the lowest layer to optimize the split. In addition, there is an extra multi-modal priority control layer to provide priority for different travel modes. Firstly, MPC is applied to optimize the signal timing plans for arterial traffic. The objectives are to increase the throughput. A hybrid urban traffic flow model is proposed to provide relatively accurate predictions of the traffic state dynamic, which is capable of simulating queue evolutions among different lane groups in a specific link. Secondly, this study expands the dynamic queue concept to the corridor-wide coordination problem. The ideal offset and boundary offsets to avoid spillback and starvation are found based on the shockwave profiles at each signalized intersection. A new multi-objective optimization model based on the preemptive goal programming is proposed to find the optimal offset. Thirdly, the priority control problem is formulated into a multi-objective optimization model, which is solved with a Non-dominated Sorting Genetic Algorithm. Pareto-optimal front results are presented to evaluate the trade-off among different objectives and the most appropriate solution is chosen with high-level information. Performance of the new adaptive controller is verified with software-in-the-loop simulation. The applied simulation environment contains VISSIM with the ASC/3 module as the simulation environment and the control system as the solver. The simulation test bed includes two arterial corridors in Edmonton, Alberta. The simulation network was well calibrated and validated. The simulation results show that the proposed adaptive control methods outperform actuated control in increasing throughput, decreasing delay, and preventing queue spillback.
Proceedings of the Fifth International Conference of Transportation Research Group of India
Author: Akhilesh Kumar Maurya
language: en
Publisher: Springer Nature
Release Date: 2022-03-05
This book (in three volumes) comprises the proceedings of the Fifth Conference of Transportation Research Group of India (CTRG2019) focusing on emerging opportunities and challenges in the field of transportation of people and freight. The contents of the volume include characterization of conventional and innovative pavement materials, operational effects of road geometry, user impact of multimodal transport projects, spatial analysis of travel patterns, socio-economic impacts of transport projects, analysis of transportation policy and planning for safety and security, technology enabled models of mobility services, etc. This book will be beneficial to researchers, educators, practitioners and policy makers alike.
Development and Evaluation of a Multi-agent Based Neuro-fuzzy Arterial Traffic Signal Control System
Arterial traffic signal control is a very important aspect of traffic management system. Efficient arterial traffic signal control strategy can reduce delay, stops, congestion, and pollution and save travel time. Commonly used pre-timed or traffic actuated signal control do not have the capability to fully respond to real-time traffic demand and pattern changes. Although some of the well-known adaptive control systems have shown advantageous over the traditional per-timed and actuated control strategies, their centralized architecture makes the maintenance, expansion, and upgrade difficult and costly.