Vehicle Transportation Network Congestion Control Model Research Based On Brownian Agent Approach

Intelligent transportation system is a fundamental part of The Smart City.Vehicle transportation network congestion control in the field of intelligent transportation is an important research topic which could solve the urban road congestion,improve road capacity,and ensure the entire transport system operation efficiently and smoothly.Current research works about congestion control focus on the aspects of distributed control and intelligent vehicle guidance,etc.And other researchers already made a lot of accomplishments in traffic signal control and real-time navigation through statistical physical methods and intelligent agent modeling.However,rarely studies consider the dynamic complexity feedback mechanism existing in the transportation system,or quantitative description of different drivers routing selection preference;on the other hand,how to build a dynamic quantitative indicators of vehicle transportation network congestion is also a current congestion control problems that need be solved.This paper studies the issue of road network congestion in the field of ITS and the related quantitative indicators construction.First,in order to analyze the formation mechanism for network congestion,using a bottom-up agent modeling approach,paper proposed a multi-objective routing decision agent mobile model.In the model,vehicle agents consider two optimization goals:shortest path and congestion avoidance,to dynamically optimize routing decisions and achieve congested road traffic diversion control.Model provide a analyze approach for the complex dynamic traffic congestion feedback mechanism.Secondly,based on the multi-objective routing decision model,paper analyzes the common characteristics of congested roads and intersections under the different network topology,which could provide a theoretical basis for network congestion controlling.Finally,this paper presents an adaptive intersection node weight sequence model.The node weight could adaptive iterative modification based on the road congestion status nearby.Simulation results proved the validity and correctness of proposed model and indicators.Vehicle transportation network congestion control model based on Brownian agent approach presented in this paper,studied the internal congestion feedback mechanism in the road network,provided a new research approach for traffic diversion;mining the common features of congested roads and intersections,also provided a theoretical support for transportation network infrastructure and control.This adaptive intersection node weight model provides network congestion control a new quantitative evaluation indicator and a real-time vehicle diversion idea.The quantitative evaluation indicators of network congestion filled the research gaps in transportation system congestion description,which have a practical significance.