Characteristics Research For Driver’s Physiology And Lane Changing In Congested State Of Urban Road

The traffic congestion will be in a long memory in big cities of China, due tothe contradiction between traffic supply and traffic demand. A tremendous amountof studies have conducted to investigate the effects on traffic operation and pollutionof traffic congestion. However, very little is known about the influences of trafficcongestion on driver’s physiological and psychological health. The objectives of thisstudy are therefore to examine the change pattern of driver’s physiological andbehavioral characteristics under different traffic congested conditions, and toquantify the negative impacts of traffic congestion on driver’s physiology andbehavior via uncovering the formation mechanism. The PSR (pressure-state-response) model of driver’s lang changing is proposed to realize a warningtechnology of risky lane changing in the congested condition.The concept of pressure coefficient has been introduced to describe the trafficcongestion level. Based on driver personal subjective perception, traffic congestionwas categorized into four levels: unblock, light congestion, medium congestion, andsevere congestion. The threshold value of the pressure coefficient at each level wasestimated by using the field observation data. This provides a theoretical basis forcarrying out the experiment on driver’s visual, physiological and behavioralcharacteristics under different traffic congestion states.Advanced equipments were utilized to conduct an experiment on driver’s visual,physiological and behavioral characteristics under four traffic congestion scenarios.The indices of driver vision (i.e., gaze, glance, and wink), physiologicalcharacteristics (i.e., heart rate, heart rate variability) and risky lane changingcomposition ratio were selected to investigate the effects of traffic congestion ondriver’s physiology and behavior.According to experimental data, the differences of driver’s indicators wereexamined using non-parametric test method under different traffic congested states.Results show that the increasingly traffic congestion level results in driver’s gazepoint distribution becoming disperse, while more attention being focused on thecentral primary visual area (close-up). As the traffic congestion level increases,driver’s gaze duration shortens, saccade velocity and acceleration increases, andsaccade, blink duration and blink frequency decreases. This can be interpreted interms of information amount. If the driver is presented with traffic congestion, s/heshould to handle more traffic and more complex environmental information, such aspedestrians, adjacent vehicles and variable message signs.Heart rate and heart rate variability were adopted to analyze the effects of traffic congestion pressure on driver’s ECG characteristics. Time and frequencydomain of the two indexes were studied. Traffic congestion level and driver’s meanheart rate were taken into consideration to examine the relationship between meanheart rate and pressure coefficient. According to the analysis results, trafficcongestion significantly affects the regulatory function of driver’s nerve and vagusnerve. An increase in the level of traffic congestion associate with a reduction invagus nerve, sympathetic nerve strengthening, and the balance of heart rateadjustment changing.Congestion pressure coefficient was selected to be the independent variable.The composition ratio of driving behavior was chosen to be the dependent variable.A regression model was proposed to quantify the relationship of congestion pressurecoefficient and the composition ratio of driving behavior. Based on the study in theECG characteristics and lane changing behavior under congested conditions, a BPneural network model was developed. The pressure(congestion pressure coefficient)and status (driver’s mean heart rate) were regarded as the input variables, theresponse (the driver’s lane changing composition proportion) was chosen to be theoutput variable. Thus, the driver’s PSR model of lane changing is established underdfferent traffic congestion states to uncover the impacts of congestion pressurecoefficient and driver’s physiological status on the lane changing behaviorcomposition ratios. Field observation data were used to validate the accuracy andeffectiveness of the proposed model.The goal of this article is to investigate the inherent relationship among trafficcongestion, driver’s anxiety, driver’s visual characteristics, driver’s physiologicalcharacteristics, and driver’s behavioral characteristics. This aids understanding thevariation of driver physiological and behavioral characteristics under differenttraffic congested states. This work provides a solid basis for future research onconceiving strategies to alleviate the driver’s anxiety under traffic congestioncondition and improve traffic safety.