Explainable macroscopic and microscopic influences of COVID-19 on naturalistic driver aggressiveness derived from telematics through SHAP values of SVM and XGBoost algorithms

IF 3.9 2区 工程技术 Q1 ERGONOMICS
Apostolos Ziakopoulos, Marios Sekadakis, Christos Katrakazas, Marianthi Kallidoni, Eva Michelaraki, George Yannis
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Abstract

Introduction: This study aims to quantify the impacts of the COVID-19 pandemic on driver behavior as expressed by harsh accelerations (HA) measured by smartphone telematics data. Method: Over 35,5000 naturalistic driving trips were analyzed, fused with additional data sources such as: (a) Apple driving requests; (b) Oxford government response metrics; and (c) Our World in Data metrics for the COVID-19 pandemic. Machine learning algorithms were implemented on two scales: (a) a macroscopic scale involving daily analysis of aggregate driver behavior across the network with an SVM algorithm; and (b) a microscopic scale, involving trip-based analysis of driver trips with an XGBoost algorithm. SHAP values interpret the outputs of both algorithms, quantifying the influence of pandemic indicators with driver behavior and aggressiveness. Results: Macroscopic results (i.e., daily analysis) indicated that high total average speed values reduce HA rates, while this trend reverses with high driving speed. High values of Reproduction Rate, Total Cases per million people were found to reduce HA rates, while Total Fatalities per million people have little contribution on HA rates. Microscopic results (i.e., trip-based analysis) indicated that high speeding, total trip distance, and trip duration are associated with increased HA counts. Drivers perform more HAs on speeds between 30–50 km/h, while after 50 km/h, the contributions of speed lead to fewer HAs. A mild HA reduction was observed as Apple driving requests increase. Mild HA reductions also manifest when COVID-19 new daily cases and total cases per million increase as well. Drivers performed more HAs when daily deaths from COVID-19 were either relatively low (around 0–20 fatalities) or relatively high (around 110–120 fatalities), while the Stringency Index has an unclear contribution, indicating that pandemic measurements were more influential on HA counts compared to policy measures taken by the state.
通过SVM和XGBoost算法的SHAP值,从远程信息处理中得出COVID-19对自然驾驶攻击性的宏观和微观影响
本研究旨在通过智能手机远程信息处理数据测量的剧烈加速度(HA)来量化COVID-19大流行对驾驶员行为的影响。方法:对超过35000次自然驾驶旅行进行分析,并融合其他数据源,如:(a)苹果驾驶请求;(b)牛津政府响应指标;(c) 2019冠状病毒病大流行的数据指标中的世界。机器学习算法在两个尺度上实现:(a)宏观尺度,涉及使用SVM算法对整个网络的总体驾驶员行为进行日常分析;(b)微观尺度,包括使用XGBoost算法对驾驶员行程进行基于行程的分析。SHAP值解释了两种算法的输出,量化了流行病指标对驾驶员行为和攻击性的影响。结果:宏观结果(即日常分析)表明,总平均车速越高,HA率越低,而车速越高,HA率越低。研究发现,较高的生殖率、每百万人总病例数可降低HA率,而每百万人总死亡人数对HA率的贡献不大。微观结果(即基于行程的分析)表明,高速、总行程距离和行程持续时间与HA计数增加有关。当车速在30-50公里/小时之间时,驾驶员表现出更多的HAs,而在50公里/小时之后,车速的贡献导致ha减少。随着苹果驱动请求的增加,观察到HA轻度降低。当COVID-19每日新增病例和每百万病例总数增加时,HA也会出现轻度减少。当COVID-19的每日死亡人数相对较低(约0-20人死亡)或相对较高(约110-120人死亡)时,驾驶员表现出更多的HA,而严格指数的贡献不明确,这表明与国家采取的政策措施相比,大流行措施对HA计数的影响更大。
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来源期刊
CiteScore
6.40
自引率
4.90%
发文量
174
审稿时长
61 days
期刊介绍: Journal of Safety Research is an interdisciplinary publication that provides for the exchange of ideas and scientific evidence capturing studies through research in all areas of safety and health, including traffic, workplace, home, and community. This forum invites research using rigorous methodologies, encourages translational research, and engages the global scientific community through various partnerships (e.g., this outreach includes highlighting some of the latest findings from the U.S. Centers for Disease Control and Prevention).
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