01657498

Component

This paper explores group travel behaviour (GTB), which is defined as two or more persons intentionally traveling together during (part of) a trip. Most travel behaviour studies are based on the assumption of isolated individuals and ignore interpersonal relationships between travellers. Here the authors propose a smart card data-based method to identify GTB, drawing on insights from the theory of proxemics, which is widely used to describe the role of interpersonal distance between individuals in Social Psychology. This method uses interpersonal time distance, namely the time interval between persons’ entrance/exit time, to distinguish GTB from persons traveling on their own. The authors apply the method using smart card data generated by the Shanghai metro system, and find that the value of interpersonal time distance strongly shapes the share of travel that is classified as GTB. However, when the interpersonal time distance is small enough (e.g., smaller than 10 seconds in the case of Shanghai), it is possible to reveal overall GTB patterns at a metropolitan level and at a fine temporal scale with a reasonable level of certainty. Results from the Shanghai study also show that the GTB pattern is distinctly different from the pattern of individual travel in terms of both time and space. For example, group trips tend to occur in weekends, in the afternoons and evenings. Stations close to the central city and serving more leisure-related destinations show higher GTB shares. While acknowledging the inherent uncertainties related to the approach, the authors argue that the proposed smart card data based method is a straightforward and workable solution to reveal GTB patterns at a large spatial scale and at a fine temporal resolution and a promising approach that can be applied in more situations.

This paper was sponsored by TRB committee ADB10 Standing Committee on Traveler Behavior and Values. Alternate title: An Explorative Analysis of Group Travel Behavior Patterns in the Public Transit Context

18-02503

English

25p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References; Tables

Groups; Public transit; Rapid transit; Smart cards; Spatial analysis; Time domain analysis; Time intervals; Transit riders; Travel behavior; Travel patterns

Spatiotemporal analysis

Shanghai (China)

Data and Information Technology; Planning and Forecasting; Public Transportation

Transportation Research Board Annual Meeting 2018 Paper #18-02503

TRIS, TRB, ATRI

Jan 8 2018 10:36AM