Revealing Urban Traffic Demand by Constructing Dynamic Networks With Taxi Trajectory Data

• Published in: IEEE access Vol. 8; pp. 147673 - 147681
• Main Authors: Zhang, Hui, Zhang, Lele, Che, Fa, Jia, Jianmin, Shi, Baiying
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Clustering; dynamic spatial-interaction network; Global Positioning System; point of interest; Polynomials; Public transportation; Regression analysis; spatiotemporal characteristics of taxi trips; Spatiotemporal phenomena; Statistical analysis; taxi GPS trajectory data; Trajectories; Trajectory; Transportation networks; Travel; Travel demand; Travel modes; Urban areas; Urban traffic demand
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3015752

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 <inline-formula> <tex-math notation="LaTeX">\times1 </tex-math></inline-formula>km 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.