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

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

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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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