Abstract：
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.
摘要：
在快速城市化和机动化进程中，了解交通安全与危险因素之间的关系，以便提供有针对性的改进和政策建议，变得尤为重要。违规和警察执法是关键变量，但是崩溃和违规之间的内生关系使得这些变量不可靠，限制了它们的使用。为了解决这个问题，这项研究开发了一种系统的方法，用于对碰撞和违规进行联合建模，以识别碰撞和违规热点，并检查宏观层面影响因素的潜在机制。社会经济,公路网,公共设施,交通执法,和苏州115个城镇的土地利用强度数据，中国,被收集为自变量。采用双变量负二项空间条件自回归模型（BNB-CAR）和潜在安全改进（PSI）方法来识别易发生碰撞和易发生违规的区域，并应用了一个可解释的机器学习框架来探索各地区的影响因素。结果表明，所提出的框架能够准确地识别问题区域并量化关键因素的影响，which,在苏州,是交警的数量和他们每天的巡逻时间。考虑到这些与执法相关的信息，为减少崩溃和违规频率提供了重要的见解；例如，将交警人数和每日巡逻时间保持在一定的阈值(在该样本中，警察人数低于11人，巡逻时间低于2.3小时)与增加这些数字对降低高撞车和高违规区域的可能性同样有效。所提出的方法可以帮助流量管理员识别关键的影响因素，尤其是执法因素，在容易发生碰撞和违规的地区，并提供改进指南。
