Research On The Signal Control Optimization Of Urban Intersection Considering PM_2.5 Emissions

With the acceleration of urbanization,vehicle ownership and resident travel demand increase rapidly,which in turn results in more and more serious traffic congestion and vehicle emissions.Focusing on these two issues,this paper establishes a signal control optimization framework on the basis of Pareto optimality to both reduce the vehicle delay and control the PM_2.5.5 exhausts for an intersection.In this framework,a typical intersection in Chengdu is selected to study.The phase sequence,cycle length and splits in main signals are firstly optimized by using fast Non-dominated Sorting Genetic Algorithm.The signals for waiting areas are then adjusted with the concern of vehicle stops,which altogether builds up the complete intersection's signal control optimization model.Then,based on the measured data,models in microscopic traffic simulation VISSIM and microscopic motor vehicle emission simulator MOVES are established,based on which the joint simulation platform VMSim is further developed to realize the automatic calculation from traffic simulation to signal scheme evaluation.Finally,the optimization model and VMSim platform are connected in order to construct the feedback system of the entire signal control optimization.After intersection's calculation and simulation,2 Pareto optimal signal control schemes are finally obtained.The results show that optimal schemes can simultaneously improve the stop delay and PM_2.5 emission levels by more than 50%and 40%respectively.The signal control optimization framework established in this paper has achieved the highest performance for congestion and emission's simultaneous mitigation.It has provided a quatitive proof for the positive role that signal control optimization plays in improving traffic conditions,and offers some theoretical and practical reference for traffic control decision makers.