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- Title
Sensitivity Analysis of Incentive Situations and Personal Attributes to Driving Anger Based on MNL Model: A Naturalistic Experimental Study.
- Authors
Ping WAN; Lixin YAN; Shan LU
- Abstract
Driving anger, called "road rage", has gradually become universal phenomenon nowadays, which has been a concern to traffic management authorities. It is necessary to figure out impacting degree of the influencing factors on driving anger for taking the corresponding intervening measures. Forty drivers were enrolled to conduct naturalistic experiments on a busy route in Wuhan, China, where drivers' anger can be induced by various incentive situations including jaywalking, weaving/cutting in line, traffic congestion and red light with extra paid if completing the experiment ahead of reference time. According to behavioral theory and disaggregation theory, the influencing factors including the incentive situations and personal attributes (i.e. gender, age, temperament) were determined for proposing driving anger prediction model based on MNL (multinomial logit). Then, the sensitivity of each influencing factor on driving anger was analyzed by elasticity theory on the basis of the proposed model. The result indicates that age, temperament and illegal behaviors from surrounding people are decisive influencing factors to driving anger sates with different intensity because their average elasticity values for none anger (neutral), low anger, medium anger, high anger are 1.254, 2.713, 2.914, respectively, which are all bigger than 1. Moreover, the accuracy of the proposed model is 78.30%. The results can provide theoretical support for developing key monitoring or targeted intervention to deal with the decisive influencing factors for traffic management authorities.
- Subjects
WUHAN (China); ANGER; SENSITIVITY analysis; ROAD rage; TRAFFIC congestion; GENDER; AGGRESSIVE driving
- Publication
Technical Gazette / Tehnički Vjesnik, 2021, Vol 28, Issue 5, p1654
- ISSN
1330-3651
- Publication type
Article
- DOI
10.17559/TV-20210601103911