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原文传递 Driving Behavior and Its Correlation with COVID-19 Response Measures: A Neural Network Forecasting Analysis

题名：	Driving Behavior and Its Correlation with COVID-19 Response Measures: A Neural Network Forecasting Analysis
正文语种：	eng
作者：	Marios Sekadakis;Christos Katrakazas;Eva Michelaraki;George Yannis
作者单位：	Dept. of Transportation Planning and Engineering National Technical Univ. of Athens 5 Heroon Polytechniou St. Athens GR-15773 Greece;Dept. of Transportation Planning and Engineering National Technical Univ. of Athens 5 Heroon Polytechniou St. Athens GR-15773 Greece;Dept. of Transportation Planning and Engineering National Technical Univ. of Athens 5 Heroon Polytechniou St. Athens GR-15773 Greece;Dept. of Transportation Planning and Engineering National Technical Univ. of Athens 5 Heroon Polytechniou St. Athens GR-15773 Greece
关键词：	COVID-19; Stringency index; Time series; Neural network autoregressive forecasting
摘要：	The pandemic of COVID-19 has affected human patterns since December 2019. Since the very beginning, most countries imposed strict measures such as lockdowns and the suspension of all nonessential movements to reduce the spread of the pandemic. Therefore, mobility, road safety, and travel behavior were also significantly affected. At present, many studies tried to investigate travel or mobility behavior changes taking into account all possible transportation modes, but very few studies investigated driving behavior. This study aims to investigate driving behavior and its correlation with the strictness of COVID-19 response measures. Four neural network autoregression (NNAR) models with an external regressor were developed in order to forecast three different future stringency scenarios. NNAR models were employed as the forecasting performance was superior when comparing with statistical autoregressive integrated moving average (ARJMA) models. The NNAR models were developed using driving behavior-related variables (i.e., driving speed, speeding, speeding duration percentage, and mobile use percentage), derived from a smartphone application that has been developed by OSeven Telematics. The NNAR models were trained on 2020 data and three different scenarios were predicted for 2021 by providing three different constant stringency indices (i.e., 0, 55, 85). In particular, normal conditions without restrictions were simulated with zero stringency index, whereas moderate restrictions were simulated with 55 and finally, fully restrictions were simulated with 85. The NNAR modeling results showed that with higher stringency index, mobile use and driving speed tend to increase, whereas speeding duration demonstrates higher peaks. Interestingly, with stricter response measures, lower values were forecasted for speeding. Taking into account the modeling outcomes, there is a direct effect of the COVID-19 response measures on driving behavior. Nevertheless, a wider time frame for data collection as well as the use of more sophisticated techniques to control for the interrelationship between COVID-19 spread and driving behavior might be useful for future studies. Practical Applications: Interested stakeholders could exploit the study findings and the lessons learned during the pandemic in order to mitigate road safety implications. The COVID-19 pandemic demonstrated the vulnerability of mobility, travel behavior, road safety, and driving behavior patterns throughout health or societal crises. For example, driving speed, peaks of speeding duration percentage, as well as mobile use were found to be higher for an increased stringency index (i.e., during measures imposition) which lower traffic volumes imply. As a direct effect between response measures and driving behavior was observed, measures that promote safety and equity could be tested using the modeling approach followed in this paper. In this direction, road safety administrations have already proposed lowering speed limits inside cities to 20 or 30 km/h. Lower speeds could reduce the chance of road crashes, serious injuries, fatalities, and harsh events. Active traveling such as walking, cycling, or e-scooters could also be promoted by policymakers as a step towards safer and environmentally friendly mobility.
出版年：	2022
期刊名称：	Journal of Transportation Engineering
卷：	148
期：	10
页码：	04022083.1-04022083.13