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,
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