Abstract
As a crucial travel mode, taxi plays a significant role in residents' daily travel. Uncovering taxi traffic demand has become a hotspot in transport studies. Previous researchers pay more attention to the statistical characteristics of taxi trips, while few studies focus on the dynamic features in different periods of a day. In this article, we study the taxi travel demand by constructing dynamic networks based on taxi trajectory data. In addition, relationship between travel intensity and point of interest (POI) in Xiamen, China is discussed. Firstly, the study area is divided by 1km × 1km uniform cells. The pick-up and drop-off activities of passengers are recorded for each cell. Secondly, the networks are constructed by regarding each cell as a node and regarding taxi trips from a cell to another cell as an edge. On this basis, we divide a day into 12 periods by two hours and construct the networks for different periods. Finally, correlation between travel intensity and POI intensity is detected with regression analysis. Results show that the taxi trip networks have large clustering coefficient and small shortest path length, which indicates they are `small world' networks. Moreover, the taxi trip networks are disassortative networks that hotspot areas tend to connect with the common areas. Furthermore, the taxi trip length in a day follows a lognormal distribution and the peak hour of taxi trip appears around midnight. Finally, a cubic polynomial curve could fit the relationship between travel intensity and POI intensity. Our findings provide a new insight for understanding the traffic demand of taxi.
Highlights
Traffic demand plays an important role in urban traffic planning, traffic management and city planning
Zhang et al studied the relationship between travel intensity and point of interest (POI) based on car-hailing data, and the results show that some types of POIs such as traffic facilities have great impacts on pick-up and drop-off [39]
We count the number of POI for each cell, and build the relationship between travel intensity and the POI intensity
Summary
Traffic demand plays an important role in urban traffic planning, traffic management and city planning. Three have been many studies focusing on spatiotemporal characteristics of urban travel demand by using taxi trajectory data. Liu et al proposed a complex network method to study the community structure of taxi trips [28] He et al partitioned studied area into grids with 100 m×100 m and constructed spatiotemporal traffic diagrams to extract traffic dynamics [29]. The fluctuation of traffic demand from network perspective during a day is not clear, which hinders the improvement of taxi service To fill this gap, this paper proposes a spatiotemporal analysis method by constructing taxi trip network to detect the spatiotemporal characteristic of taxi trips. This paper intends to provide a new insight to understand the traffic demand by constructing dynamic taxi trip networks.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
