探索 COVID-19 对旅行行为的影响

Wenbin Yao, Youwei Hu, Congcong Bai, Sheng Jin, Chengcheng Yang
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引用次数: 0

摘要

自2019年12月疫情爆发以来,COVID-19在全球范围内迅速蔓延。为减缓疫情蔓延,各国实施了一系列政策措施。交通系统不仅是 COVID-19 的重要载体,也是防控疫情传播的重要手段。因此,大多数防疫措施都是以限制旅行为基础,从而减缓疫情的传播。因此,由于大流行病的影响和相应的控制措施,交通系统发生了巨大的变化。通过分析交通系统在 COVID-19 影响下的演变,可以更好地了解社会经济的变化和居民日常生活的变化。基于丰富的车牌识别数据,对 COVID-19 影响下的城市机动车出行特征进行了分析。根据疫情控制和恢复工作、生产的相关过程,将分析期分为四个阶段。每个阶段都分析了宏观交通状况指标的变化。三类典型机动车组(即非本地营运车辆、出租车和本地营运车辆)的交通流量、过境车辆数、平均出行强度、车辆日均出行时间、车辆日均出行距离以及出行起点和终点的时空分布等指标均由其贡献的交通流量、过境车辆数、平均出行强度、车辆日均出行时间、车辆日均出行距离以及出行起点和终点的时空分布来表征。这些数据阐明了大流行不同阶段人们出行行为的时空演变特征。数据分析结果表明,COVID-19 严重改变了城市居民的机动化出行行为。在恢复工作和生产的初期阶段,居民的机动化出行意愿与第一阶段相比明显下降。直到第三、第四阶段,这种意愿逐渐恢复,但仍未完全达到疫情发生前的水平。具体而言,早晚高峰的交通状况已基本恢复,甚至超过了疫情发生前的水平;但非高峰时段之间仍存在一定差距。不同类型的车辆受大流行影响的程度也存在明显差异。其中,出租车受大流行病的影响最大。在第四阶段(4 月底),车辆的日均行驶时间和日均行驶距离仍分别比第一阶段减少了 29.25% 和 22.63%。许多出租车的运营时间从晚上 22:00 缩短至晚上 19:00。车辆的时空特征表明,减少灵活的出行需求(如购物、餐饮和娱乐)是减少路网出行需求的关键。这项研究为未来严重公共卫生事件下实施交通管制措施提供了数据支持,并有助于制定后 COVID-19 时代的城市交通政策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Exploring Impact of COVID-19 on Travel Behavior

Exploring Impact of COVID-19 on Travel Behavior

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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