This paper analysed automobile commuters' mode choice behaviour under the influence of simulated multimodal traveller information by developing two logit models. A combined revealed preference (RP)/stated preference (SP) travel behaviour survey was administered to drivers to gather individual commuters' travel decisions under integrated multimodal traveller information. Two SP scenarios were designed where the first scenario is to test the mode choice preference in a basic situation involving a congested work/school trip with information on several travel options, and the second scenario is to investigate the mode choice decision when certain incentives are given for public transport. Results showed that integrated multimodal traveller information can influence a traveller's mode choice decision. The influencing factors that significantly affect the mode choice decision include socio-economic characteristics – for example, gender, age, level of education and level of income – and multimodal traveller information attributes – for example, access mode to mass rapid transit (MRT) station, access time to MRT station and transit seat availability. The findings are useful to traffic management agencies for designing better operational policy and information publication strategies.
This paper is focused on calibration of an intelligent network simulation model (INSIM) with reallife transportation network to analyse the INSIM's feasibility in simulating commuters' travel choice behaviour under the influence of real-time integrated multimodal traveller information (IMTI). A transportation network model for the central and western areas of Singapore was simulated in PARAMICS and integrated with INSIM expert system by means of an application programming interface to form the INSIM. Upon calibration, INSIM was able to realistically present complicated scenarios in which real-time IMTI was provided to commuters and the network performance measures being recorded.
Aim:Real-time traveler information affects auto commuter's travel behavior.
Method:An ordered probit model is used to analyze auto commuter's mode switching propensity under influence of simulated real-time multimodal traveler information. A travel preference survey is administered to car drivers to gather individual commuter's travel decisions under integrated multimodal traveler information.
Result:It is shown that integrated multimodal traveler information can influence willingness of car drivers to switch mode of travel, while socio-economic characteristics also influence the mode choice decision.
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