School of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China.
School of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China.
Accid Anal Prev. 2024 Jan;194:107323. doi: 10.1016/j.aap.2023.107323. Epub 2023 Oct 19.
During rapid urbanization and increase in motorization, it becomes particularly important to understand the relationships between traffic safety and risk factors in order to provide targeted improvements and policy recommendations. Violations and police enforcement are key variables, but the endogenous relationship between crashes and violations has made these variables unreliable and has limited their use. To manage this problem, this study developed a systematic approach for the joint modeling of crashes and violations to identify crash and violation hotspots and examine the mechanisms underlying macro-level contributing factors. Socio-economic, road network, public facility, traffic enforcement, and land use intensity data from 115 towns in Suzhou, China, were collected as independent variables. A bivariate negative binomial spatial conditional autoregressive model (BNB-CAR) and the potential for safety improvement (PSI) method were adopted to identify crash-prone and violation-prone areas, and an interpretable machine learning framework was applied to explore the factors' effects by area. Results showed that the proposed framework was able to accurately identify problem areas and quantify the impact of key factors, which, in Suzhou, were the number of traffic police and their daily patrol time. Considering such enforcement-related information provided important insights into reducing crash and violation frequency; for example, keeping the number of traffic police and daily patrol time under certain thresholds (number of police lower than 11 and patrol time lower than 2.3 h in this sample) was as effective as increasing these numbers for reducing the probability of high-crash and high-violation areas. The proposed approach can help traffic administrators identify the key contributing factors, especially enforcement factors, in crash-prone and violation-prone areas and provide guidelines for improvement.
在快速城市化和机动车保有量增加的背景下，了解交通安全与风险因素之间的关系变得尤为重要，以便提供有针对性的改进和政策建议。违规行为和警察执法是关键变量，但事故和违规行为之间的内生关系使得这些变量变得不可靠，并限制了它们的使用。为了解决这个问题，本研究开发了一种系统的方法，用于对事故和违规行为进行联合建模，以识别事故和违规行为的热点，并研究宏观层面的促成因素的作用机制。本研究收集了来自中国苏州 115 个城镇的社会经济、道路网络、公共设施、交通执法和土地利用强度数据作为自变量。采用二元负二项空间条件自回归模型（BNB-CAR）和安全改进潜力（PSI）方法来识别易发生事故和易发生违规的区域，并应用可解释的机器学习框架按区域探索因素的影响。结果表明，所提出的框架能够准确识别问题区域，并量化关键因素的影响，在苏州，这些因素是交通警察的数量及其每日巡逻时间。考虑到这种与执法相关的信息，为降低事故和违规频率提供了重要的见解；例如，将交通警察的数量和每日巡逻时间保持在一定阈值以下（在这个样本中，警察数量低于 11 人，巡逻时间低于 2.3 小时）与增加这些数量一样有效，可以降低高事故和高违规区域的发生概率。所提出的方法可以帮助交通管理人员识别易发生事故和违规的区域中的关键促成因素，特别是执法因素，并为改进提供指导。
