Research On Coordinated Control Methods Of Urban Traffic Signals Based On Big Data

In recent years,with the rapid development of social economy,the number of motor vehicles has rapidly increased,and the problem of urban traffic congestion has become increasingly significant.The traffic arteries in the urban road network are the major arteries of urban transportation and bear most of the traffic load.The coordinated optimization and control of traffic signals on the main roads is one of the important methods to solve urban traffic congestion.With the arrival of the era of big data,how to use massive traffic data and advanced distributed data processing platforms to provide data and decision support for optimal control of traffic signals is an important and urgent task.The thesis studies the basic principle of coordinated control of arterial road signals and the calculation method of main road signal control parameters.,and analyze the classic MAXBAND.In view of the uneven distribution of traffic flow during peak hours,MAXBAND based on different bandwidth requirements is improved.The number of selected vehicles(the sum of the two-way green-wave bandwidth is the largest)is a positive utility target,and the queue length is introduced as a negative utility target.Solve the calibration problem of proportional coefficient of bidirectional different bandwidth demand according to the length of queuing.In addition,the thesis uses LINGO software to optimize the main road green wave coordination control model.In order to ensure the reliability and accuracy of the input data of the control model for the coordinated optimization of the arterial signal,and improve the data processing efficiency,the paper also studied the estimation method of the traffic flow parameters based on the GPS data of the floating vehicle.The data of floating cars has become an important source of road traffic data collection.This thesis deals with the data of floating cars based on the Hadoop platform.Among them,HDFS is used for data storage and based on this,HIVE external tables are established to facilitate queries and simple data calculations;data cleaning principles are established to remove redundant and erroneous data collected;map matching is implemented based on grid search methods.Based on the calculation of the queue length and the instantaneous speed-time integral method,the average stroke speed is calculated.Finally,the effectiveness of the proposed model is verified by VISSIM simulation software.The data from the experiment is about 78 million taxi GPS data and the geomagnetic inductive coil flow data collected by the SCATS system at one hour in one day in Shenyang.The simulation results show that the traffic signal coordination control method for urban arterial roads based on traffic big data has a certain reduction in the delay time and the number of stoppages compared with the classic model and the existing improved model,which helps to improve the traffic efficiency of the arterial traffic.