Carsharing Trip Characteristic Analysis: Do Users Choose Carsharing Rather Than Taxi to Economize?. Issue 42 (December 2018)

• Record Type: Journal Article
• Authors: Hui, Ying; Liu, Cuiyi; Ding, Mengtao
• Is Part Of: Transportation research record. Volume 2672:Issue 42(2018)
• Page Start: 115
• Page End: 126
• Publication Date: 2018-12
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/0361198118774232
• Languages: English
• ISSNs: 0361-1981

Abstract

Carsharing and taxi are both shared or public car services in urban transportation systems, which means they have a lot in common. In particular, the narrow price gap between carsharing and taxi or public transit in China makes them similar. Trip characteristics analysis and market segmentation are needed, to investigate why and when travelers choose carsharing rather than taxi. In this study, vehicle GPS data and operation order data of a round-trip carsharing system in Hangzhou, China, is used to obtain information on 13, 338 valid trips. The trips are divided into three groups based on the travel cost comparison with taxi. Then, an artificial neural network model is developed to analyze group characteristics. The trip characteristics concluded from the model, and analysis on typical service stations, reveal that carsharing has its price advantage on simple long-distance trips during off-peak hours, which is called the regular market. Carsharing's extended market, in which travel cost is higher than by taxi, covers two types of trips: One involves short driving distance (around 20 km) and long stopping time, and tends to occur in areas in which it is difficult to hail a taxi during peak hours; the other involves very short driving distance (around 10 km), more activity spots and very low travel cost (around 25 CNY). The results of this study can help carsharing operators to extend and adjust their business. Also, these results can contribute to help city administrators to reach better decisions.