Exploring Impact of COVID-19 on Travel Behavior

被引:0
作者
Wenbin Yao
Youwei Hu
Congcong Bai
Sheng Jin
Chengcheng Yang
机构
[1] Zhejiang University,Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture
[2] Zhejiang University,Center for Balance Architecture
[3] Zhejiang Lab,undefined
[4] Zhongyuan Institute,undefined
[5] Zhejiang University,undefined
来源
Networks and Spatial Economics | 2024年 / 24卷
关键词
Traffic engineering; COVID-19; Traffic status; Travel behavior; Spectral clustering;
D O I
暂无
中图分类号
学科分类号
摘要
Since its outbreak in December 2019, COVID-19 has spread rapidly across the world. To slow down the spread of the pandemic, various countries have implemented a series of policies and measures. The transportation system is not only an important carrier for COVID-19, but also a vital means for the prevention and control of the spread of the pandemic. Therefore, most anti-pandemic measures are based on travel restrictions, thereby slowing down the spread of the pandemic. As a result, because of the impact of the pandemic and corresponding control measures, the transportation system has undergone tremendous changes. By analyzing the evolution of the transportation system in response to the influence of COVID-19, it is possible to better understand socioeconomic changes and the changes in residents' daily life. Based on rich license plate recognition data, the characteristics of urban motorized travel under the influence of COVID-19 has been analyzed. According to the processes associated with the control of the pandemic and the resumption of work and production, the analysis period is divided into four stages. The changes in indicators of macroscopic traffic status are analyzed for each stage. The three types of typical motor vehicle groups (i.e., non-localized operating vehicles, taxis, and localized operating vehicles) are characterized by the traffic flow they contribute, the number of vehicles in transit, the average travel intensity, the average daily travel time of a vehicle, the average daily travel distance of a vehicle, and the spatiotemporal distributions of origins and destinations of trips. These data clarify the spatiotemporal evolution characteristics of peoples’ travel behavior at different stages of the pandemic. The results of data analysis show that COVID-19 has deeply changed the motorized travel behavior of urban residents. In the initial stage of resumption of work and production, the willingness to engage in motorized travel had decreased significantly compared with that in the first stage. This willingness gradually resumed until the third and fourth stages, but still did not fully reach the level before the onset of the pandemic. Specifically, the traffic status during morning and evening peaks has basically recovered, and has even increased beyond the level before the pandemic; however, a certain gap was still found between off-peak hours. There were also significant differences in the extent to which different types of vehicles were affected by the pandemic. Among these, taxis were impacted the most by the pandemic. In the fourth stage (at the end of April), the average daily travel time of a vehicle and the average daily travel distance of a vehicle still decreased by 29.25% and 22.63% compared with the first stage, respectively. The operating time of many taxis was shortened from 22:00 PM to 19:00 PM. The spatiotemporal characteristics of vehicles show that the reduction of flexible travel demand (e.g., shopping, catering, and entertainment) is key to the reduction of the travel demand of the road network. This research provides data support for the implementation of traffic control measures under future grave public health events and enables the formulation of urban traffic policies in the post COVID-19 era.
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页码:165 / 197
页数:32
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共 61 条
[1]  
Anzai A(2020)Assessing the impact of reduced travel on exportation dynamics of novel coronavirus infection (COVID-19) J Clin Med 9 601-721
[2]  
Kobayashi T(2010)Simultaneous and selective inference: Current successes and future challenges Biom J 52 708-87
[3]  
Linton N(2021)Mobility network models of COVID-19 explain inequities and inform reopening Nature 589 82-14
[4]  
Benjamini Y(2017)Clustering vehicle temporal and spatial travel behavior using license plate recognition data J Adv Transp 2017 1-407
[5]  
Chang S(2020)Estimating and projecting air passenger traffic during the COVID-19 coronavirus outbreak and its socio-economic impact Saf Sci 129 399-1000
[6]  
Pierson E(2021)Analysis of mobility data to build contact networks for COVID-19 PLoS ONE 16 967-495
[7]  
Koh PW(2020)The relationship between trends in COVID-19 prevalence and traffic levels in South Korea Int J Infect Dis 96 483-undefined
[8]  
Gerardin J(2020)Spatially explicit models for exploring COVID-19 lockdown strategies Trans GIS 24 undefined-undefined
[9]  
Redbird B(2020)COVID-19: epidemiology, evolution, and cross-disciplinary perspectives Trends Mol Med 26 undefined-undefined
[10]  
Grusky D(2020)When to lift the lockdown in Hubei province during COVID-19 epidemic? An insight from a patch model and multiple source data J Theor Biol 507 undefined-undefined