TY  - JOUR
PY  - 2021//
TI  - Road factor analysis of taxi speeding behavior considering spatial effect
JO  - China safety science journal (CSSJ)
A1  - Zhou, Yue
A1  - Fu, Chuanyun
A1  - Jiang, Xinguo
A1  - Mao, Chengyuan
A1  - Liu, Haiyue
SP  - 162
EP  - 170
VL  - 31
IS  - 3
N2  - 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.     ===    为充分利用道路特征干预出租车超速行为,搜集成都市区内出租车全球定位系统（GPS）轨迹数据,识别其超速行为,并采集道路特征数据,以各道路的出租车超速频数及平均超速严重度为超速特征,应用全局莫兰指数和4类空间回归模型,分别确定超速特征及道路因素的空间自相关性和显著影响出租车超速特征的道路因素。研究结果表明:出租车超速行为和道路特征均存在明显的空间自相关性;空间自相关模型（SAC）对超速频数的拟合效果最好,空间杜宾模型（SDM）对平均超速严重度的拟合效果最佳;路段的相接道路数、出入口数及车道数明显增加超速频数;道路长度和车道数显著增大平均超速严重度;施工区和单行道均与超速特征无关。<p />  <p>Language: en</p>
LA  - en
SN  - 1003-3033
UR  - http://dx.doi.org/10.16265/j.cnki.issn1003-3033.2021.03.023
ID  - ref1
ER  -