A Study of the Factors Affecting Multimodal Ridesharing with Choice-Based Conjoint Analysis

Sunghi An, Daisik Nam, R. Jayakrishnan, Soongbong Lee and Michael G. McNally
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Sunghi An: Department of Civil and Environmental Engineering, Institute of Transportation Studies, University of California, Irvine, CA 92603, USA
Daisik Nam: Graduate School of Logistics, Inha University, Incheon 22212, Korea
R. Jayakrishnan: Department of Civil and Environmental Engineering, Institute of Transportation Studies, University of California, Irvine, CA 92603, USA
Soongbong Lee: Department of Big Data Platform and Data Economy, The Korea Transport Institute, Sejong 30147, Korea
Michael G. McNally: Department of Civil and Environmental Engineering, Institute of Transportation Studies, University of California, Irvine, CA 92603, USA

Sustainability, 2021, vol. 13, issue 20, 1-14

Abstract: As public perception about the shared economy evolves, peer-to-peer ridesharing has been gaining increased attention worldwide. Both private and public sector entities have launched mobile app-based ridesharing services, while a range of methodologies and system architectures have been proposed in academia. Whereas traditional ridesharing methods match drivers and riders when their origin and destination are similar, recently proposed algorithms often feature multi-hop and multimodal properties that allow riders to be connected by multiple modes. Such algorithms can reduce travel time and/or travel cost; however, they may also add other travel impedances, such as requiring multiple transfers. Understanding user behavior toward such new ridesharing systems is essential for successful service design. For policymakers and service planners, identifying factors that impact traveler choices can lead to better design and improved services. This research involved a web-based survey to capture traveler preferences using a conjoint analysis framework. A choice-based method was adopted to identify factors for the estimation model and to analyze traveler willingness to pay. Among the proposed factors, the number-of-transfers was shown to be the most important, as was expected. When a multimodal ridesharing system provides less travel time, low travel cost, and sufficient ridesharing incentive, people are more likely to pay for the service.

Keywords: multimodal ridesharing; conjoint survey design; choice-based conjoint analysis; multinomial logit model; factor estimation analysis; choice behavior (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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