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采用随机参数贝叶斯 LASSO 建模方法对激进出租车司机的操作因素进行分析。

Operational factor analysis of the aggressive taxi speeders using random parameters Bayesian LASSO modeling approach.

机构信息

School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China.

School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China; National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu 611756, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China.

出版信息

Accid Anal Prev. 2021 Jul;157:106183. doi: 10.1016/j.aap.2021.106183. Epub 2021 May 10.

Abstract

Partial taxi speeders are observed with both high speeding frequency and severity (range). They thereby can be viewed as aggressive speeders whose behaviors may result in more hazards than others. Among the factors contributing to taxi speeding, the operational factors are proven to be deterministic. However, previous studies mainly investigate the operational factors of taxi speeding frequency, which fail to comprehensively unveil the impact of factors on speeders, especially for aggressive speeders. This study intends to disclose the operational factors affecting the aggressive taxi speeders with the random parameters Bayesian least absolute shrinkage and selection operator (LASSO) modeling approach. Taxi speeding behaviors and several operational factors are extracted from taxi GPS trajectory data in Chengdu, China. Based on the hourly speeding frequency and average speeding severity of each speeder, the fuzzy C-means clustering algorithm is employed to categorize taxi speeders into three cohorts: restrained speeder (RS), moderate speeder (MS), and belligerent speeder (BS). Compared to RS, MS and BS are treated as the aggressive taxi speeders. Several binary logistic models are developed with RS as the reference category. The random parameters Bayesian binary logistic LASSO model that captures the unobserved heterogeneity and tackles the multicollinearity is found to be the best fit model to identify the significant operational factors. The results indicate that aggressive taxi speeders are linked to longer daily driving distance and cruise distance, shorter delivery time, higher hourly income, driving at night, and driving on low-speed limit roads. However, intensive lane-changes and sufficient daily naps do not contribute to aggressive taxi speeders. Moreover, BS is more sensitive to the operational factors than MS. This study stresses the necessity of implementing speeder classification in taxi driver management and conceiving countermeasures considering the operational factors which are significantly associated with the aggressive taxi speeders.

摘要

部分出租车司机超速行驶的频率和严重程度(范围)都很高。因此,可以将他们视为激进的超速者,其行为可能比其他超速者带来更多的危险。在导致出租车超速的因素中,运营因素已被证明是确定性的。然而,以前的研究主要调查了出租车超速频率的运营因素,未能全面揭示因素对超速者的影响,尤其是对激进超速者的影响。本研究旨在利用随机参数贝叶斯最小绝对收缩和选择算子(LASSO)建模方法,揭示影响激进出租车超速者的运营因素。从中国成都的出租车 GPS 轨迹数据中提取了出租车超速行为和几个运营因素。基于每个超速者的每小时超速频率和平均超速严重程度,采用模糊 C 均值聚类算法将出租车超速者分为三组:克制型超速者(RS)、适度型超速者(MS)和激进型超速者(BS)。与 RS 相比,MS 和 BS 被视为激进的出租车超速者。以 RS 为参考类别,建立了几个二元逻辑模型。结果表明,激进的出租车超速者与每日行驶距离和巡航距离较长、交货时间较短、每小时收入较高、夜间驾驶以及在低速限制道路上驾驶有关。然而,密集的变道和充足的每日小睡并不能导致激进的出租车超速者。此外,BS 比 MS 对运营因素更敏感。本研究强调了在出租车司机管理中实施超速者分类的必要性,并提出了考虑与激进出租车超速者显著相关的运营因素的对策。

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