大流行病中的交通行为和政府干预:针对道路安全的混合可解释机器学习

IF 8.3 1区 工程技术 Q1 ECONOMICS
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引用次数: 0

摘要

在大流行病期间,交通管理部门和决策者在识别和验证新的出行行为及其对交通事故模式的影响以制定有效的安全策略方面面临着巨大挑战。及时评估紧急事件的长期影响并制定适当的应对策略对于管理未来发生的紧急事件至关重要。本研究旨在回答以下研究问题:问题 1:在 COVID-19 大流行的不同阶段,各种时空风险因素是如何影响交通事故伤害严重程度的?问题 2:在大流行前、大流行初期、大流行第一波和第二波之间以及大流行后,影响车祸伤害严重程度的关键风险因素是什么?问题 3:大流行期间实施的政府政策和干预措施如何影响交通行为和道路安全?本研究提出了一种基于极梯度提升(XGBoost)和SHAPLE Additive exPlanation(SHAP)的混合可解释机器学习方法,以识别与交通事故相关的受伤严重程度的影响风险因素。此外,我们还提出了一种使用非线性多叉 logit 模型的统计学习方法,以联合分析按伤害严重程度划分的汽车交通事故数量,并评估 COVID-19 大流行病在不同阶段的影响。我们的研究结果包括对各特征组件的系统级分类法进行详细分析,以及使用 SHAP 总分将碰撞事故数据分类为大流行前、大流行中和大流行后阶段的高层次促成变量。预期成果包括:确定实施出行限制以减少交通事故的最佳时机、了解大流行病各阶段交通流模式的变化以及确定可减少交通事故和交通拥堵的有效公共卫生干预措施。此外,研究还显示,在大流行初期,交通流量和交通事故率显著下降。与此相反,在随后的大流行阶段和大流行后阶段,由于危险驾驶行为导致的严重交通事故有所增加,这强调了适应性安全措施的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Transport behavior and government interventions in pandemics: A hybrid explainable machine learning for road safety
During a pandemic, transportation authorities and policymakers face significant challenges in identifying and validating new travel behavior and how it affects traffic crash patterns to develop effective safety strategies. A timely assessment of an emergency incident’s long-term impact and the development of appropriate response strategies are critical for managing future occurrences. This study investigates to answer these research questions (RQs):
RQ1: How do various spatio-temporal risk factors influence traffic crash injury severity during the different phases of the COVID-19 pandemic?
RQ2: What are the key risk factors influencing injury severity in automobile crashes during the pre-pandemic, early pandemic, between the first and second waves of the pandemic, and the post-pandemic era?
RQ3: How do the implemented government policies and interventions during the pandemic affect transport behavior and road safety?
This study presents a hybrid explainable machine learning approach based on eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP) to identify influential traffic crash-related risk factors for injury severity. Additionally, we propose a statistical learning approach using a nonlinear multinomial logit model to jointly analyze the count of automobile traffic crashes by injury severity and assess the impact of the COVID-19 pandemic across different phases. Our findings include a detailed analysis of system-level taxonomies across feature components, as well as the use of aggregate SHAP scores to classify crash data into high-level contributing variables during the pre-pandemic, intra-pandemic, and post-pandemic phases. The expected outcomes include insights such as identifying the best times to implement travel restrictions to reduce traffic accidents, understanding shifts in traffic flow patterns across pandemic phases, and determining effective public health interventions that can reduce both traffic accidents and congestion. Furthermore, the study reveals that the initial pandemic phase saw a significant decrease in traffic volume and accident rates. In contrast, the subsequent pandemic and post-pandemic phases saw an increase in severe accidents due to risky driving behaviors, emphasizing the importance of adaptive safety measures.
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来源期刊
CiteScore
16.20
自引率
16.00%
发文量
285
审稿时长
62 days
期刊介绍: Transportation Research Part E: Logistics and Transportation Review is a reputable journal that publishes high-quality articles covering a wide range of topics in the field of logistics and transportation research. The journal welcomes submissions on various subjects, including transport economics, transport infrastructure and investment appraisal, evaluation of public policies related to transportation, empirical and analytical studies of logistics management practices and performance, logistics and operations models, and logistics and supply chain management. Part E aims to provide informative and well-researched articles that contribute to the understanding and advancement of the field. The content of the journal is complementary to other prestigious journals in transportation research, such as Transportation Research Part A: Policy and Practice, Part B: Methodological, Part C: Emerging Technologies, Part D: Transport and Environment, and Part F: Traffic Psychology and Behaviour. Together, these journals form a comprehensive and cohesive reference for current research in transportation science.
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