%0 Journal Article
%T Pattern Analysis of Driver¡¯s ¡°Pressure-State-Response¡± in Traffic Congestion
%A Weiwei Qi
%A Yulong Pei
%A Mo Song
%A Yiming Bie
%J Discrete Dynamics in Nature and Society
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/853845
%X Traffic congestion, which has a direct impact on the driver¡¯s mood and action, has become a serious problem in rush hours in most cities of China. Currently, the study about driver¡¯s mood and action in traffic congestion is scarce, so it is necessary to work on the relationship among driver¡¯s mood and action and traffic congestion. And the PSR (pressure-state-response) framework is established to describe that relationship. Here, PSR framework is composed of a three-level logical structure, which is composed of traffic congestion environment, drivers¡¯ physiology change, and drivers¡¯ behavior change. Based on the PSR framework, various styles of drivers have been chosen to drive on the congested roads, and then traffic stream state, drivers¡¯ physiology, and behavior characters have been measured via the appropriative equipment. Further, driver¡¯s visual characteristics and lane changing characteristics are analyzed to determine the parameters of PSR framework. According to the PSR framework, the changing law of drivers¡¯ characteristics in traffic congestion has been obtained to offer necessary logical space and systematic framework for traffic congestion management. 1. Introduction Traffic congestion has become a peculiar phenomenon in rush hour of big city, and the rapid increasing number of automobiles and comparable insufficiency of transportation facilities are the direct reason [1]. So, scholars usually research the causes, formation mechanism, and mitigation strategies of traffic congestion from the perspective of traffic supply and traffic demand. Arnott [2] established a bathtub model of downtown rush-hour traffic congestion to perfect the standard economic models of traffic congestion. Tsekeris and Geroliminis [3] analyzed the relationship between land use and traffic congestion by employing the macroscopic fundamental diagram, which constitutes robust second-best optimal strategies that can further reduce congestion externalities. Traffic congestion prediction plays an important role in route guidance and traffic management [4], and many traffic congestion prediction models have been proposed by scholars, such as the nearest neighbor method [5], the ARIMA (autoregressive integrated moving average) model [6] and the vector ARMA (autoregressive moving average) model [7]. Traffic congestion has brought huge economic losses and adverse impact on the driver¡¯s mood [8]. Traffic congestion increases the drivers¡¯ physiological pressures and the burdens of visual cognition [9], which leads to risky driving behavior [10]. So,
%U http://www.hindawi.com/journals/ddns/2013/853845/