GPS data in taxi-sharing system: Analysis of potential demand and assessment of fuel consumption based on routing probability model

Qing Yu, Weifeng Li, Haoran Zhang and Jinyu Chen

Applied Energy, 2022, vol. 314, issue C, No S0306261922003452

Abstract: With the emergence of big geospatial data and the breakthrough of massive data processing, taxi-sharing offers the public a novel transportation mode with high comfort but low cost. However, designing a taxi-sharing system to effectively allocate taxi resources and provides high public acceptance services is an urgent problem to be solved. Furthermore, to what extent the taxi-sharing can be an eco-friendly service without bringing extra pressure to urban emission, fuel consumption, and transportation system is still an unanswered question. This paper proposes a methodology framework to design a taxi-sharing system with driver routing probability based matching and dispatching algorithms. The methodology is capable of matching multiple taxi trips into a sharing trip, with consideration of temporal and spatial feasibilities. The matching of sharing trips includes the determination of which trips to match and the sequence of the destinations. To examine the potential of operation efficiency improved and fuel consumption reduced in taxi-sharing, three scenarios are proposed with different constraints, representing different operation strategies. The sharing trips are then dispatched to the taxis. The potential of operating performance improvement and the potential of fuel consumption reduction are analyzed in the three scenarios. It is found that the delivery part of taxi-sharing may produce more travel distance because of the detour. The key factor for taxi-sharing service to reduce Vehicle Kilometres Travelled is the idle trips and the taxi resources saved. Considering both delivery trips and idle trips together, although taxi-sharing can reduce total fuel consumption in the city, it may increase traffic pressure in certain range area, especially in the key road sections or intersections in the urban road network and the area with high traffic demand.

Keywords: Taxi-sharing; Routing probability model; Spatiotemporal analysis; Fuel consumption (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.apenergy.2022.118923

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