01785791

Component

https://doi.org/10.1177/03611981211049143

Many shared mobility solutions have been developed over recent decades. In the case of mobile technological innovations, new solutions that are more flexible to user demands have emerged. These dynamic solutions allow users to be served by optimizing different aspects such as the detour to pick up a passenger or the waiting time for users. Such methods make it possible to satisfy requests quickly and to match as closely as possible user expectations. However, these approaches usually use fleets composed of numerous small-capacity vehicles to serve each user. By contrast, microtransit aims to serve a more massive demand than conventional shared mobility methods. Our study falls within this context. It aims to identify recurrent patterns of mobility and to verify the possibility of implementing microtransit lines to serve them. In other words, the proposed method identifies spatial and temporal areas where the implementation of a flexible transport line would meet a potential mobility demand. The recurrence of trips in these specific areas provides a guarantee of the reliability of the designed lines.

Nicolas Chiabaut is now affiliated to Univ. Lyon, Univ. Gustave Eiffel, ENTPE, LICIT, Lyon, France. © National Academy of Sciences: Transportation Research Board 2021.

English

0000-0003-3336-1470

0000-0003-0450-4890

pp 58-70

2022-3

Transportation Research Record: Journal of the Transportation Research Board

Web

References (30)

Artificial intelligence; Data science; Mobility; Travel demand; Vehicle sharing

Highways; Vehicles and Equipment

TRIS, TRB, ATRI

Oct 20 2021 3:14PM