Urban Road Traffic Simulation Based on Robot Motion

Abstract

With the rapid growth of residents’ economic level, the number of private car users has also exploded, and the consequent problems such as traffic congestion, environmental degradation, and increased road accident rates have become factors restricting the healthy development of cities. Urban trunk roads have a unique position in the urban road system because of their highly complex functions of traffic, places, and landscape. The urban road traffic simulation technology proposed in this paper provides an effective way to solve these problems. The purpose of the research is to quantitatively evaluate a variety of arterial road traffic optimization methods, identify and summarize the key factors affecting urban road traffic optimization, and verify the conclusions through case studies. This paper creates a framework for the system simulation model from the analysis of urban road flow characteristics, including vehicle intersection description model, vehicle production model, traffic rule model, vehicle model under the influence of traffic lights, intersection vehicle address tracking model, and evaluation index model. After analyzing the advantages and disadvantages of various existing vehicles, the robot motion planning model is selected for the micro-simulation system of urban road traffic signal control. The simulation system provides an interactive user interface that allows users to input signal timing parameters and traffic flow parameters into the system. The final results of the research show that the optimal path and optimization time of SRL-CACA+ are 16.79 and 8.41 s, respectively. SRL-CACA+ algorithm is better than SRL-CACA algorithm and SRL-ACA algorithm in terms of optimal path and optimization time achieved better performance.