Urban freeway users' diversion response to variable message sign displaying the travel time of both freeway and local street

H. Gan, X. Ye

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

Most variable message sign (VMS) installed on the urban freeway only provide information about traffic conditions of urban freeway, but they do not provide traffic information about local streets. This study explores urban freeway users' diversion response to the D-VMS (VMS that display explicitly the travel time of both urban freeway and local streets) in the context of China. An on-site stated preference questionnaire survey was conducted to collect behavioural data. A cross-sectional binary probit model and a panel binary probit model are estimated to identify factors that influence drivers' diversion behaviour in response to D-VMS. The study showed that drivers' en-route decision on diverting from freeway to local streets can be significantly influenced by D-VMS and the extent of D-VMS impacts depends on driver characteristics, local street characteristics and D-VMS messages. Main findings regarding D-VMS impacts are, (a) travel time saving and drivers' years of driving experience serve as positive factors in diverting, (b) number of traffic lights on the local street, frequency of urban freeway use, being a mid-age driver, and being an employer-provided car driver, serve as negative factors in diverting. On the modelling aspect, it was shown that the panel model does not provide substantially different model coefficients but more robust statistical inferences for model coefficients as compared to the cross-sectional model, and the cross-sectional model tends to seriously overestimate t-test values for explanatory variables changing across drivers (e.g. demographic characteristics) but slightly underestimate t-test values for explanatory variables changing across scenarios (e.g. travel time savings). The findings have implications for better design and operation of advanced traveller information systems and for future effort on survey design, data collection and model estimation.

Original languageEnglish (US)
Pages (from-to)78-86
Number of pages9
JournalIET Intelligent Transport Systems
Volume6
Issue number1
DOIs
StatePublished - Mar 1 2012
Externally publishedYes

Fingerprint

Variable message signs
Highway systems
Travel time
motorway
travel time
travel
driver
traffic
Advanced Traveler Information System
Advanced traveler information systems
survey design
time
questionnaire survey
Telecommunication traffic
savings
Values
automobile
employer
information system
Railroad cars

ASJC Scopus subject areas

  • Transportation
  • Environmental Science(all)
  • Mechanical Engineering
  • Law

Cite this

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abstract = "Most variable message sign (VMS) installed on the urban freeway only provide information about traffic conditions of urban freeway, but they do not provide traffic information about local streets. This study explores urban freeway users' diversion response to the D-VMS (VMS that display explicitly the travel time of both urban freeway and local streets) in the context of China. An on-site stated preference questionnaire survey was conducted to collect behavioural data. A cross-sectional binary probit model and a panel binary probit model are estimated to identify factors that influence drivers' diversion behaviour in response to D-VMS. The study showed that drivers' en-route decision on diverting from freeway to local streets can be significantly influenced by D-VMS and the extent of D-VMS impacts depends on driver characteristics, local street characteristics and D-VMS messages. Main findings regarding D-VMS impacts are, (a) travel time saving and drivers' years of driving experience serve as positive factors in diverting, (b) number of traffic lights on the local street, frequency of urban freeway use, being a mid-age driver, and being an employer-provided car driver, serve as negative factors in diverting. On the modelling aspect, it was shown that the panel model does not provide substantially different model coefficients but more robust statistical inferences for model coefficients as compared to the cross-sectional model, and the cross-sectional model tends to seriously overestimate t-test values for explanatory variables changing across drivers (e.g. demographic characteristics) but slightly underestimate t-test values for explanatory variables changing across scenarios (e.g. travel time savings). The findings have implications for better design and operation of advanced traveller information systems and for future effort on survey design, data collection and model estimation.",
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