Abstract
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.
Highlights
Recent studies of human behavior reveal the predictability in the mobility of individuals [1, 2]
It brings to light that the shared mobility demand may take many aspects requiring different forms of transportation services to be optimally satisfied
We investigated whether groups of travelers that realize similar trips in a city exist
Summary
Recent studies of human behavior reveal the predictability in the mobility of individuals [1, 2]. A significant regularity appears at a larger scale, making it possible to identify recurrent trends in urban mobility even if variability can be observed in the trips at the scale of a particular individual [3] and to derive behavioral laws [4,5,6]. It appears that patterns repeating from one day to the can be identified either by observing the traffic conditions [7], the most used roads [8, 9], or in the choice of modes of transport [10].
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