Proactive Signal Control Method Based On The Prediction Of Urban Traffic Congestion

Traffic congestion in urban road networks is a persisting or even increasing problem of modern society.The traffic flow conditions in large-scale urban networks depend critically on the applied signal control strategies.Hence,improved traffic signal control methods can play an important role in alleviating congestion.In China,Metropolitan transportation networks have a hierarchical structure which essentially consists of different urban roads.Traffic flow usually presents a chaotic scene because of heterogeneous transport modes traveling in the same road.Therefore,the development of efficient and practicable signal control strategies for urban road networks under congested traffic conditions is a major challenge with significant scientific and practical relevance.This paper presents a proactive signal control method to manage the traffic congestion especially at signalized intersection.Two cellular automata(CA)models are developed and validated to predict the traffic flow under oversaturated traffic conditions and interferences between vehicles and bicycles.Adaptive signal control is utilized to optimize the fluctuant traffic flow and macroscopic fundamental diagram are proposed to evaluate the traffic evolution.The aims of these two methods lie to improve the traffic throughput and prevent the traffic congestion.Firstly,the characteristic of traffic congestion and difficulties in signal control are analyzed.Traffic congestion are trigger by saturated traffic conditions and interferences between vehicles and bicycles.Traffic spillback and starvation and intersection gridlock are generated by saturated traffic conditions.Additional delay may appear under mixed traffic during the vehicles through movements at signalized intersection.The existing signal methods are unable to cope with these main difficulties from signal optimization and signal evaluation and traffic prediction.Secondly,the principle of two branches in proactive signal control,adaptive signal control and macroscopic fundamental diagram are summarizd by literature review.Adaptive signal control aims to utilize the road space-time resource to satiety the traffic flow fluctuation.Macroscopic fundamental diagram can be used to feedback the control results.These evaluations are crucial to further guide the adaptive signal.Macroscopic fundamental diagram aims to prevent the development of traffic congestion or speed up the clearance of traffic congestion.Both of these two branches require the traffic predictions from simulation platform.Hence,the cellular automata are selected as the simulation tool.Eight kinds of driving behavior under saturated traffic conditions are illustrate.According to these behaviors,the CA model are developed to predict the saturated traffic.Model verification and simulation at four intersections in Nanjing shows that the proposed CA can reproduce these oversaturated phenomena.The dispersion effect during the bicycle through movements at signalized intersection is the key causes of the interferences in mixed traffic.The other CA model are proposed to simulate the vehicle delay caused by the dispersion effect.Model verification and simulation at seven intersections in Nanjing and Ningbo shows that the proposed CA can accurately predict the traffic flow.These obtained traffic flow are important to support the proactive signal control.In order to realize the proactive signal control,the self-organization rules of adaptive signal control are proposed to control the inferences between vehicle and bicycle,the traffic spillover,the traffic accumulation and the intersection gridlock.Furthermore,the Simulink model for CA implements are established.Based on the Simulink model,the proactive signal control can be tested on the Actual road network.The results show that the traffic flow can be optimized,the oversaturation can be restrained and the traffic congestion can be eased.