Even if shared mobility services are encouraged by transportation policies, they remain underused and inefficient transportation modes because they struggle to find their customer base. This paper aims to estimate the potential demand for such services by focusing on individual trips and determining the number of passengers who perform similar trips. Contrary to existing papers, this study focuses on the demand without assuming any specific shared mobility system. The experiment performed on data coming from New York City conducts to cluster more than 85% of the trips. Consequently, shared mobility services such as ride-sharing can find their customer base and, at a long time, to a significantly reduce the number of cars flowing in the city. After a detailed analysis, commonalities in the clusters are identified: regular patterns from one day to the next exist in shared mobility demand. This regularity makes it possible to anticipate the potential shared mobility demand to help transportation suppliers to optimize their operations.
Shared mobility services have been hailed as a game changer in transportation and a promising solution to reduce congestion and improve urban mobility. However, only a few studies have attempted to determine if significant demand for such services really exists. This article marks a first attempt toward this objective by employing ridesourcing trip data to identify twin travelers. Using the DiDi Chuxing dataset, a general methodology is defined to assess the similarity between trips and to cluster comparable travelers. The study reveals, among other things, that at least 18% of trips can be paired without introducing significant delays.
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