Citation

Ma Z, Shao C, Song Y, Chen J. Transp. Res. F Traffic Psychol. Behav. 2014; 26: 199-209.

Copyright

(Copyright © 2014, Elsevier Publishing)

DOI

10.1016/j.trf.2014.07.006

PMID

unavailable

Abstract

This paper presents an investigation of driver response attitudes to traffic information provided by variable message signs (VMS). In order to obtain the potential contributing factors affecting drivers' response under the information provided by VMS, stated preference (SP) method was used to collect data through the on-site survey. The survey includes four parts, which are personal socioeconomic characteristics, trip characteristics, driver's attitude and perception towards VMS message content, stated response to VMS displayed, covering 13 questions. A total of 9600 drivers were interviewed to examine the factors affecting drivers' compliance with VMS, and a total of 8477 drivers' questionnaires were valid. Drivers' response in this paper is a polytomous variable with three categories, namely not diverting, undecided and diverting, so the multinomial logit model is used to develop drivers' response model under the information provided by VMS. Correspondingly, twelve explanatory variables are considered in the developed drivers' response model, and three explanatory variables are continuous variables, and the others are categorical variables. The results show that most explanatory variables show significant association with the drivers' response under the information provided by VMS, except for age. Comparing with diverting behavior, some explanatory variables, including gender, driving vehicle, familiarity with alternative route, usefulness, understandability, accuracy and displaying mode of VMS, have positive correlation with not diverting behavior. In a similar way, comparing with diverting behavior, some explanatory variables, including gender, drivers' experience, driving vehicle, usefulness and understandability, have positive correlation with undecided diverting behavior. The findings show that the multinomial logit model developed in this paper provides a better understanding of potential factors related to drivers' diversion behavior.