Multi-objective Optimization Of Signal Timing Of Urban Intersection Under The Mixed Traffic Flow

Under the condition of urban road traffic in our country, the mixed traffic phenomenon of motor vehicles, non-motor vehicles and pedestrians at intersection is a common traffic characteristic. Mutual interference and influence among mixed traffic flow leads to traffic chaos, low operation efficiency, long delay time, frequent occurrence of traffic accident and many other problems. So, the study of intersection signal timing optimization under mixed traffic flow circumstances has a great significance.First of all, this paper analysis the traffic characteristics of mixed traffic flow, traffic conflict and solution at intersections and introduces the basic theory of traffic signal control. On this basis, the paper analysis three kinds of classical signal timing methods, improved Webster method and their advantages and disadvantages.Secondly, with the idea of overall optimization, the balanced coordination relation among these three modes of transport (motor vehicles, non-motor vehicles and pedestrians) and the weight coefficient of optimization target which changes with real-time traffic volume, this paper develops an optimization model of multi-target signal timing. In the model, we select vehicle delay, non-motor vehicle delay, pedestrian delay, vehicle parking times and traffic capacity as optimization objection function, each phase effective green time and cycle time as decision-making parameters, saturation as constraint conditions. Then a genetic algorithm is designed to solve this problem.Finally, this paper selects the typical mixed traffic signal intersection in Beijing— —Sidaokou as an example for analysis. According to the field survey and the existing traffic problems, corresponding channelization and signal phase optimization schemes are made. Comparison of multi-objective optimization scheme with the original scheme, the improved Webster scheme and VISSIM simulation scheme, multi-objective optimization model makes each performance indicator of intersection to be improved in different degree and the overall performance of intersection is greatly improved, thus verifying the effectiveness of the multi-objective optimization model and the feasibility of genetic algorithm. This paper also analyzes the influence on multi-objective optimization model by different crossing modes of non-motor vehicles. And non-motor vehicle twice crossing mode is recommended for the intersection which has a larger non-motor vehicle traffic flow and a geometrical condition’s permission. What’s more, the changes of multi-objective optimization model compared to the improved Webster method under different traffic conditions are studied. The results show that the multi-objective optimization model is more focused on enhancing traffic capacity during rush hours and reducing the vehicle delay and stop times during off-peak hours, which illustrates that the model can meet the practical requirement of traffic flow and be a better signal control optimization.