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后疫情时代阿拉巴马州的共享出行与主动出行:基于新冠疫情调查数据的机器学习分析

Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data.

作者信息

Xu Ningzhe, Nie Qifan, Liu Jun, Jones Steven

机构信息

Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, United States.

Alabama Transportation Institute, The University of Alabama, Tuscaloosa, AL 35487, United States.

出版信息

Travel Behav Soc. 2023 Jul;32:100584. doi: 10.1016/j.tbs.2023.100584. Epub 2023 Mar 27.

Abstract

The COVID-19 pandemic has had unprecedented impacts on the way we get around, which has increased the need for physical and social distancing while traveling. Shared mobility, as an emerging travel mode that allows travelers to share vehicles or rides has been confronted with social distancing measures during the pandemic. On the contrary, the interest in active travel (e.g., walking and cycling) has been renewed in the context of pandemic-driven social distancing. Although extensive efforts have been made to show the changes in travel behavior during the pandemic, people's post-pandemic attitudes toward shared mobility and active travel are under-explored. This study examined Alabamians' post-pandemic travel preferences regarding shared mobility and active travel. An online survey was conducted among residents in the State of Alabama to collect Alabamians' perspectives on post-pandemic travel behavior changes, e.g., whether they will avoid ride-hailing services and walk or cycle more after the pandemic. Machine learning algorithms were used to model the survey data (N = 481) to identify the contributing factors of post-pandemic travel preferences. To reduce the bias of any single model, this study explored multiple machine learning methods, including Random Forest, Adaptive Boosting, Support Vector Machine, K-Nearest Neighbors, and Artificial Neural Network. Marginal effects of variables from multiple models were combined to show the quantified relationships between contributing factors and future travel intentions due to the pandemic. Modeling results showed that the interest in shared mobility would decrease among people whose one-way commuting time by driving is 30-45 min. The interest in shared mobility would increase for households with an annual income of $100,000 or more and people who reduced their commuting trips by over 50% during the pandemic. In terms of active travel, people who want to work from home more seemed to be interested in increasing active travel. This study provides an understanding of future travel preferences among Alabamians due to COVID-19. The information can be incorporated into local transportation plans that consider the impacts of the pandemic on future travel intentions.

摘要

新冠疫情对我们的出行方式产生了前所未有的影响,这增加了出行时保持身体距离和社交距离的必要性。共享出行作为一种新兴的出行模式,允许出行者共享车辆或拼车,在疫情期间面临着社交距离措施的挑战。相反,在疫情导致社交距离的背景下,人们对主动出行(如步行和骑自行车)的兴趣再度兴起。尽管已经做出了大量努力来展示疫情期间出行行为的变化,但人们在疫情后对共享出行和主动出行的态度仍未得到充分探索。本研究调查了阿拉巴马州人在疫情后对共享出行和主动出行的出行偏好。在阿拉巴马州居民中进行了一项在线调查,以收集阿拉巴马州人对疫情后出行行为变化的看法,例如他们在疫情后是否会避免使用叫车服务,以及是否会更多地步行或骑自行车。使用机器学习算法对调查数据(N = 481)进行建模,以确定疫情后出行偏好的影响因素。为了减少任何单一模型的偏差,本研究探索了多种机器学习方法,包括随机森林、自适应提升、支持向量机、K近邻和人工神经网络。结合多个模型中变量的边际效应,以显示疫情导致的影响因素与未来出行意图之间的量化关系。建模结果表明,对于开车单程通勤时间为30 - 45分钟的人来说,他们对共享出行的兴趣会降低。对于年收入10万美元及以上的家庭以及在疫情期间通勤次数减少超过50%的人来说,他们对共享出行的兴趣会增加。在主动出行方面,那些希望更多在家工作的人似乎对增加主动出行感兴趣。本研究提供了对新冠疫情后阿拉巴马州人未来出行偏好的理解。这些信息可纳入考虑疫情对未来出行意图影响的当地交通规划中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15f7/10040369/912c821d5bc9/gr1_lrg.jpg

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