Road factor analysis of taxi speeding behavior considering spatial effect

doi:10.16265/j.cnki.issn1003-3033.2021.03.023

Abstract

Abstract: In order to prevent taxi speeding by utilizing road characteristics, GPS trajectory data of taxis in Chengdu city area were gathered to identify their speeding behavior, and road characteristics were extracted as well. Then, with speeding frequency and average speeding severity of each road as speeding characteristics, global Moran's I and four kinds of spatial regression models were adopted to analyze spatial autocorrelation of speeding characteristics and road factors and to explore significant influencing factors of the former. The results reveal that obvious spatial autocorrelation exists between taxi speeding and road characteristics. Spatial Autocorrelation Model (SAC) and Spatial Durbin Model (SDM) are the best for fitting of speeding frequency and average speeding severity estimation, respectively. Number of connected road, access number and lane number evidently increase taxi speeding frequency while road length and lane number significantly increase average speeding severity. Whereas, work zone and one-way roads are unrelated with speeding characteristics.

Key words: taxi speeding behavior, road characteristics, global position system (GPS) trajectory data, speeding frequency, speeding severity, spatial regression model, spatial autocorrelation

CLC Number:

X951

ZHOU Yue, FU Chuanyun, JIANG Xinguo, MAO Chengyuan, LIU Haiyue. Road factor analysis of taxi speeding behavior considering spatial effect[J]. China Safety Science Journal, 2021, 31(3): 162-170.

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