Study On Arterial Coordinated Optimization With Time-Of-Day Control In Urban Area

Arterial coordinated control plays an indispensable role in alleviating the urban traffic congestion and improving the efficiency of road traffic,while the adjacent intersections of urban arterial relate and influence each other.Nowadays,coordinated control in China is mainly based on collected data from loop vehicle detector whose failure rate usually reaches 30%,which result in barely satisfaction of control effect.On the other side,Time-of-Day control strategy has significant effect in adjusting traffic flow with obvious regularity,and high cost performance comparing to actuated control and adaptive control.In this study,the above two methods are combined to discuss the coordinated optimization with time-of-day control in urban arterial.At first,this study first mended the lost historical traffic volume data with the method of moving average convergence divergence(MACD).Furthermore,a mixed clustering which combined K-means clustering with system clustering was used to deal with the traffic data analysis,and the classic Silhouette index was recommended as the termination condition.Meanwhile,in consideration of time sequence of traffic flow,we eliminated singular points in clustering,and a mixed clustering based time-of-day control strategy was completed.Secondly,extend time-of-day to arterial coordination control strategy.The coordination research boundary was determined by the coordinatablity model of intersections,and the corresponding time-of-day strategies was designed by using the previous mixed clustering method to analyze the historical traffic volume data.On this foundation,the Multi-Objective Particle Swarm Optimization(MOPSO)algorithm was used to optimize average delay and maximal bandwidth.Also,some test conditions like vehicle queuing length,cycle length,the offset between methods' bandwidth were presented to verify the practical significance of the results.Furthermore,in order to reduce the disturbance that may be caused by scheme switching,it is necessary to design the corresponding signal transition strategy by considering the effect on the overall efficiency of traffic control.A kind of discrete choices models--the multinomial logit model was introduced in this situation,and the utility function was established by selecting characteristic variables,and after the parameter test,the transition method with the largest probability was selected under the target scene.Finally,Binhe arterial in Suzhou city,Jiangsu province was selected as case study site,due to its significant pattern of traffic flow.The simulation results indicated that,the average decrement rate of average vehicle of the method is 9.79%,in which the optimal TOD control plans can also reduce the fluctuation during TOD transitions with the consideration of time sequence;when compared mixed clustering optimization with current traffic control plan,the proposed strategy showed more advantages in bandwidth of traffic control plan and delay optimization,the final result of the decrement rate of average vehicle delay is 12.63% to current traffic control plan and 2.45% to mixed clustering optimization,while the increasing rate of bandwidth of traffic control plan is 0.98% and 23.51% respectively;and the final result is in agreement with the Multinomial Logit model prediction,that the transition method with the maximum probability choice showed the best performance in simulation.