Siting of Carsharing Stations Based on Spatial Multi-Criteria Evaluation: A Case Study of Shanghai EVCARD

Wenxiang Li, Ye Li, Jing Fan and Haopeng Deng
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Wenxiang Li: The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China
Ye Li: The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China
Jing Fan: The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China
Haopeng Deng: The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China

Sustainability, 2017, vol. 9, issue 1, 1-16

Abstract: Carsharing is one of the effective ways to relieve the problems of traffic jams, parking difficulties, and air pollution. In recent years, the numbers of carsharing services and their members have remarkably increased around the world. The project of electric carsharing in Shanghai, called EVCARD, has also developed rapidly with very large demand and supply. Aiming to determine the optimal locations of future stations of the EVCARD, this research employs a novel method combining the analytic hierarchy process (AHP) and geographical information system (GIS) with big data. Potential users, potential travel demand, potential travel purposes, and distance from existing stations are selected as the decision criteria. A siting decision system is established, consisting of 15 evaluation indicators which are calculated from multi-source data on mobile phones, taxi trajectory, point of interests (POI), and the EVCARD operation. The method of the AHP is used to determine the indicator weights, and the “Spatial Analyst” tool of ArcGIS is adopted to generate the indicator values for every 1 km × 1 km decision unit. Finally, synthetic scores are calculated to evaluate the candidate sites of EVCARD stations. The results of the case study verify the effectiveness of the proposed method, which can provide a more scientific and feasible method for carsharing operators to site stations, avoiding aimless and random decisions.

Keywords: carsharing; analytic hierarchy process; geographical information system; big data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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