Combined Revealed- and Stated-Preference Survey to Understand the Impact of Multi-Source Real-Time Information on Travel Mode Choice

Abstract

This study presents a combined revealed preference (RP) and stated preference (SP) survey to understand travelers’ mode choices under the influence of real-time information for different activity types and trip lengths. The D-efficient method is adopted to generate SP scenarios. The empirical data for this study came from a “Survey to understand the impact of ICT on transportation choices” (SUIT; ICT = information and communication technology), conducted in July 2023 in the Washington, DC metro area and the Charlotte metro area, North Carolina (NC), USA A combined RP–SP multinomial logit and mixed logit model (MxL) capturing the error components have been estimated based on the collected data. The model results reveal that daily parking costs significantly impact individuals’ mode choices and tend to discourage driving. Furthermore, real-time information such as the availability of parking spaces at workplaces and metro stations encourages people to prefer drive and park & ride modes. Conversely, information on flash flooding alerts, road closures, and road accidents discourages people from driving, riding as auto-passengers, or taking a transportation network company (TNC) (Uber/Lyft) for trip purposes. Lastly, information on reduced waiting time and disruption plays a significant role in selecting transit and park & ride modes. The results obtained from this study can be beneficial to policymakers when assessing or designing alternative sustainable modes in the presence of real-time information. As its policy finding, the study recommends that transit disruption should be handled carefully to retain loyal customers and achieve various sustainability goals. In the event of transit disruption, alternative sustainable transportation modes should be offered to transit riders.