01627690

Component

Car sharing as a means to provide individuals with a short-term access to automobiles to complete a personal trip has been well received in Europe and North America but is still in its infancy in China. Currently, there are few case studies based on operation data to support the importance of car sharing system in urban comprehensive transportation system. In this study, vehicle GPS data of a round-trip car-sharing system in Hangzhou was used to derive trip information and classify the trip chain pattern. The rental behaviors, of which time is within 24 h, are chosen as study samples. Through data preprocessing, 26,085 valid behavior samples are obtained. Several trip chain characteristic indices are selected to describe the characteristics. The Clustering LARge Applications algorithm was used to analyze the clustering of such sized samples. Next, 26,085 samples were classified into five clusters as nonactivity point, multiactivity point, high stop-rate, long-distance, and common patterns; each cluster has significant differences in one or two trip chain characteristics indices. The cluster result reflected that different use patterns exist, and at least in three typical use patterns, shared vehicles are used as private cars to satisfy continuous daily trip demands or even commute demands. The car-sharing system in Hangzhou still presents a multipattern condition. Therefore, the proposed method could facilitate companies in formulating a flexible price strategy and determining valued customers. Moreover, the traffic administration could more deeply understand the position and function of car-sharing mode in an urban comprehensive transportation system, and plan scientific policies.

This paper was sponsored by TRB committee ABJ40 Standing Committee on Travel Survey Methods.

01618707

17-03059

English

19p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References; Tables

Algorithms; Cluster analysis; Travel patterns; Trip chaining; Vehicle sharing

Hangzhou (China)

Data and Information Technology; Highways; Planning and Forecasting

Transportation Research Board Annual Meeting 2017 Paper #17-03059

PRP, TRIS, TRB, ATRI

Dec 8 2016 11:08AM