Snapcab: Urban Scale Context-Aware Smart Transport Using Adaptive Context Tries

Morris, Alistair; Patsakis, Constantinos; Cahill, Vinny; Bouroche, Mélanie

doi:10.1007/978-3-319-29236-6_4

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…The second approach used spatio-temporal constraints to cluster the requests (Cullen, Jarvis, and Ratliff, 1981;Gidofalvi et al, 2008;Ma, Zheng, and Wolfson, 2013;Martínez, Viegas, and Eiró, 2014;Morris et al, 2016). In the routing optimization phase, each cluster is treated as a single trip/request.…”

Section: Demand Clusteringmentioning

confidence: 99%

Real-world meeting points for shared demand-responsive transportation systems

et al. 2019

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While conventional shared demand-responsive transportation (SDRT) systems mostly operate on a door-to-door policy, the usage of meeting points for the pick-up and drop-off of user groups can offer several advantages, like fewer stops and less total travelled mileage. Moreover, it offers the possibility to select only feasible and well-defined locations where a safe (de-)boarding is possible. This paper presents a three-step workflow for solving the SDRT problem with meeting points (SDRT-MP). Firstly, the customers are clustered into similar groups, then meeting (and divergence) points are determined for each cluster. Finally, a parallel neighbourhood search algorithm is applied to create the vehicle routes. Further, a simulation with realistic pick-up and drop-off locations based on map data is performed in order to demonstrate the impact of using meeting points for SDRT systems in contrast to the door-to-door service. Although the average passenger travel time is higher due to enhanced walking and waiting times, the experiment highlights a reduction of operator resources required to serve all customers.

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