School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran.
Centre for Urban Statistics and Observatory of Tehran, Tehran, Iran.
Int J Inj Contr Saf Promot. 2022 Dec;29(4):429-449. doi: 10.1080/17457300.2022.2075396. Epub 2022 Jul 20.
Traffic rules violations in urban areas, which can cause traffic crashes and unsafe situations, are a major issue nowadays. The present paper aims to analyze the frequency of traffic violations in Tehran city, Iran, over a five-year period (March 2016- March 2021). The data is obtained via road traffic violation monitoring system which can capture and process various traffic violations. This database, containing about 97 million violations committed by about 16 million drivers, is explored applying three statistical approaches. In the first approach, some multiplicative SARIMA and Bayesian Spatio-temporal models are fitted to the monthly violations. Also, in the second approach, the K-means clustering algorithm is applied to discover homogeneous districts of Tehran Municipality regarding their number of violations and their number of violations per camera towers meter during the study. Finally, in the third approach, a random-effect zero-truncated one-inflated Poisson model is proposed to study factors affecting driver's number of violations over time.
如今，城市地区的交通违规行为是造成交通事故和不安全状况的主要问题。本文旨在分析伊朗德黑兰市五年间（2016 年 3 月至 2021 年 3 月）的交通违规频率。该数据是通过道路交通违规监测系统获得的，该系统可以捕捉和处理各种交通违规行为。该数据库包含约 9700 万次违规行为，涉及约 1600 万司机，应用三种统计方法进行了探索。在第一种方法中，对每月的违规行为拟合了一些乘法 SARIMA 和贝叶斯时空模型。在第二种方法中，应用 K 均值聚类算法来发现德黑兰市同质地区，根据其违规次数和研究期间每台摄像机塔米的违规次数来进行分区。最后，在第三种方法中，提出了一个随机效应零截断一单位膨胀泊松模型，以研究随时间推移司机违规次数的影响因素。
