Literature DB >> 32334153

Influence of traffic congestion on driver behavior in post-congestion driving.

Guofa Li1, Weijian Lai2, Xiaoxuan Sui3, Xiaohang Li4, Xingda Qu5, Tingru Zhang6, Yuezhi Li7.   

Abstract

Traffic congestion is more likely to lead to aggressive driving behavior that is associated with increased crash risks. Previous studies mainly focus on driving behavior during congestion when studying congestion effects. However, the negative effects of congestion on driving behavior may also affect drivers' post-congestion driving. To fill this research gap, this study examined the influence of traffic congestion on driver behavior on the post-congestion roads (i.e., the roads travelled right after congestion). Twenty-five subjects participated in a driving simulation study. They were asked to complete two trials corresponding to post-congestion and non-congestion conditions, respectively. Driver behavior quantified by driving performance measures, eye movement measures, and electroencephalogram (EEG) measures was compared between the two conditions. Ten features were selected from the measures with statistical significance. The selected features were integrated to characterize drivers' response patterns using a hierarchical clustering method. The results showed that driver behavior in post-congestion situations became more aggressive, more focused in the forward area but less focused in the dashboard area, and was associated with lower power of the β-band in the temporal brain region. The clustering results showed more aggressive and lack-of-aware response patterns while driving in post-congestion situations. This study revealed that traffic congestion negatively affected driver behavior on the post-congestion roads. Practical implications for driving safety education was discussed based on the findings from the present study.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Keywords:  Advanced driver assistance systems; Driver behavior; Driving safety; Hierarchical clustering; Traffic congestion

Year:  2020        PMID: 32334153     DOI: 10.1016/j.aap.2020.105508

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  3 in total

1.  Traffic Crash Characteristics in Shenzhen, China from 2014 to 2016.

Authors:  Guofa Li; Yuan Liao; Qiangqiang Guo; Caixiong Shen; Weijian Lai
Journal:  Int J Environ Res Public Health       Date:  2021-01-28       Impact factor: 3.390

2.  Association between Crash Attributes and Drivers' Crash Involvement: A Study Based on Police-Reported Crash Data.

Authors:  Guofa Li; Weijian Lai; Xingda Qu
Journal:  Int J Environ Res Public Health       Date:  2020-12-03       Impact factor: 3.390

3.  NLP-Based Approach for Predicting HMI State Sequences Towards Monitoring Operator Situational Awareness.

Authors:  Harsh V P Singh; Qusay H Mahmoud
Journal:  Sensors (Basel)       Date:  2020-06-05       Impact factor: 3.576

  3 in total

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