Spatial Analysis of Taxi Speeding Event Using GPS Trajectory Data

GPS-equipped taxis can generate large-scale trajectory data which enables scholars to uncover taxi abnormal behaviors, such as speeding. Hence, this study intends to disclose the characteristics of taxi speeding event (SE), identify the spatial factors impacting the frequency of taxi SE, using 5,757 taxis’ GPS trajectory data in a part of the central area of Chengdu city from November 1 to November 30, 2016, along with speed limit data, electronic map data, etc. By comparing the calculated travel speeds with speed limits, taxi SEs were identified and categorized into five groups: SE (<10%), SE (10-20%), SE (20-50%), SE (50-70%), and SE (≥70%). Firstly, spatial autocorrelations of five types of taxi SE were verified by employing global and local Moran’s I, respectively. Secondly, a spatial error model was developed to investigate the influences of spatial factors on the frequency of five types of taxi SE. The results showed that there existed a global and local spatial autocorrelation and hotspots for five types of taxi SE in the metropolis. The number, length of roads and average speed limit within a grid significantly impact the frequency of various taxi SEs. The findings of this study and corresponding implications were further discussed.