Affiliation:
1. School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China
2. Institute of Intelligence Science and Engineering, Shenzhen Polytechnic, Shenzhen 518055, China
3. Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
Abstract
As young novice drivers are inclined to getting involved in traffic accidents due to their improper emergency response under sorts of gender affective state, namely, mood, widely generated in fast-paced urban life, it is of great necessary to study the impact of mood state on responsive capacity for young novice drivers. Fourteen college students were recruited to take part in complex reaction experiments for this pilot study. Each subject’s mood was collected through a simplified POMS scale, while their complex reaction time (CRT) and response error rate (RER) were acquired during the experiments. The study results showed that young novice drivers’ RER was significantly positively correlated (pc=0.323
, “pc” omitted next) with their score of total mood disturbance (TMD), and a logarithmic regression model was feasible to describe the correlations with a good fitting effect. Further, their RER was also significantly positively correlated with score of negative mood state components such as nervousness (0.290
), anger (0.300
), fatigue (0.278
), depression (0.287
), and fluster (0.261
), and a quadratic or cubic regression model was suitable to describe the correlations. Additionally, the young novice drivers’ CRT was significantly positively correlated with score of nervousness (0.222
), vigorousness (0.227
), and fluster (0.273
), and a quadratic or exponential regression model was suitable to describe the correlations. The results can provide theoretical support for developing targeted intervention to improve young novice drivers’ emergency response capacity for driving training or traffic management authorities.
Funder
Jiangxi Provincial Major Science and Technology Project-5G Research Project
Subject
Strategy and Management,Computer Science Applications,Mechanical Engineering,Economics and Econometrics,Automotive Engineering