Exploring the Intermodal Relationship between Taxi and Subway in Beijing, China

Abstract

Taxi is an indispensable mode in the urban public transportation. Although many studies have explored the travel patterns of taxi trips, few have combined taxi and subway to reveal their intermodal relationship. To bridge the gap, this study utilized taxi's trajectory data to investigate its relationship with subway. Considering the multifaceted relationship between taxi and subway in operation, taxi trips are categorized into three types, namely, subway-competing, subway-extending, and subwaycomplementing taxi trips. The characteristics of each type of taxi trips reflect the specialties and their interactions with subway. The origin/destination distributions of taxi and subway trips are compared and analyzed. Furthermore, the supply and demand of taxi within the buffer zone of each subway station are analyzed to reflect the difficulty of hailing taxis. The negative binomial regression models are used to explore the relationship between taxi trips and subway ridership. The results show that there is a significantly positive correlation between taxi trips and subway ridership.

A Cooperative Data Mining Approach for Potential Urban Rail Transit Demand Using Probe Vehicle Trajectories

“…In recent years, research on public transportation using trajectory data has become a hot spot. Specifically, the taxi GPS data has been employed to study critical issues related to public transportation, such as flexible bus route planning [13], traffic hotline design [14], and the analysis of the relationship between taxi and subway travel modes [15], [16]. This research provided new perspectives on reexamining the issue of public transport development.…”

“…The transitcompeting part accounts for 58.54% taxi trips, indicating that there existed a certain amount of potential transit demand. Jiang et al [16] used Beijing taxi data to investigate the multifaceted relationships between taxi and subway modes; the results showed that the propostion of subway-competing taxi trips was 19.32%. Li et al [21] analyzed and predicted the potential passenger demand for the last train using the taxi GPS data and bus GPS and smart card data.…”

“…By investigating the inherent spatiotemporal characteristics of taxi trips, we try to explore which part of taxi trips has the potential to shift to the urban rail transit system. Based on the classification of the taxi trip demand proposed by [4], [16], the potential urban rail transit demand is defined and the data are extracted from the transit-competing trip dataset. Subsequently, we conduct an in-depth analysis of the spatiotemporal patterns of the potential transit demand to derive policy implications for improving the competitiveness of the public transportation system.…”

Research on urban public bicycle dispatching optimization method

“…This paper uses the pre-processing program to process the leased data, it turned into the lease-return relationship between the least sites [25]. It also generates a similarity matrix based on the rent-return relationship.…”

Peer Review #1 of "Urban public bicycle dispatching optimization method (v0.1)"

“…New York Public Bicycle [24] is a people-benefit project launched by the New York City Government. Figure 3 displays the spatial distribution of rental sites.…”