Poster session for AED10 that includes papers on human mobility behavior and responses to COVID-19, long distance travel and mileage based user fees.
Mobile Device Location Data Reveals Human Mobility Response to Stay-at-Home Orders during the COVID-19 Pandemic in the U.S.
Chenfeng Xiong (email@example.com)Show Abstract
Songhua Hu, University of Maryland
Hannah Younes, University of Maryland, College Park
Weiyu Luo, University of Maryland
Sepehr Ghader, University of Maryland, College Park
Lei Zhang, University of Maryland, College Park
One approach to delay the spread of the novel coronavirus (COVID-19) is to reduce human travel by imposing travel restriction policies. Understanding the actual human mobility response to such policies remains a challenge due to the lack of ground truth and large-scale dataset describing human mobility during the pandemic. This study utilizes an integrated dataset, consisting of anonymized and privacy-protected location data that covers over 150 million monthly active samples in the U.S., COVID-19 case data, and census population information, to uncover mobility changes during COVID-19 and under the “Stay-at-home” state orders in the U.S. The study successfully quantifies human mobility responses with three important metrics: daily average number of trips per person; daily average person-miles traveled; and daily percentage of residents staying home. The data analytics reveal a voluntary mobility reduction that occurred regardless of government actions, and a “floor” phenomenon that human mobility reached a lower bound and stopped decreasing soon after each state announced the “Stay-at-home” order. A set of longitudinal models is then developed and confirms empirically that about 5% of the reduction in human mobility is due to the effect of states’ “Stay-at-home” policy. Lessons learned from the data analytics and longitudinal models offer valuable insights for government actions in preparation for another COVID-19 or other virus outbreak in the future.
Quantifying Human Mobility Behavior Changes during the COVID-19 Outbreak in the United States
Yixuan Pan (firstname.lastname@example.org)Show Abstract
Aref Darzi, University of Maryland, College Park
Aliakbar Kabiri, University of Maryland, College Park
Guangchen Zhao, University of Maryland, College Park
Weiyu Luo, University of Maryland
Lei Zhang, University of Maryland, College Park
Since the first case of the novel coronavirus disease (COVID-19) was confirmed in Wuhan, China, social distancing has been promoted worldwide, including the United States, as one of the major community mitigation strategies. However, our understanding remains limited in how people react to such control measures, as well as how people would resume their normal behaviors when those orders were relaxed. We utilize an integrated dataset of real-time mobile device location data involving 100 million devices in the contiguous United States plus Alaska and Hawaii from February 2 to May 30, 2020. Built upon the common human mobility metrics, we construct a Social Distancing Index (SDI) to evaluate people’s mobility pattern changes along with the spread of COVID-19 at different geographic levels. We find that both government orders and local outbreak severity significantly contribute to the strength of social distancing. As people tend to practice less social distancing immediately after they observe a sign of local mitigation, we identify several states and counties with higher risks of continuous community transmission and a second outbreak. Our proposed index could help policymakers and researchers monitor people’s real-time mobility behaviors, understand the influence of government orders, and evaluate the risk of local outbreaks.
Leveraging Inspection Records for Vehicle Miles Traveled Estimates and Analysis of Mileage-Based User Fees in Pennsylvania
Chenyu Yuan, Carnegie Mellon UniversityShow Abstract
Zhufeng Fan, Carnegie Mellon University
Lin Lyu, Carnegie Mellon University
Prithvi Acharya, Carnegie Mellon University
H. Scott Matthews, Carnegie Mellon University
An increasing number of jurisdictions are considering mileage-based user fees (MBUFs) to replace fuel taxes, to fund transportation infrastructure. To support the design and evaluation of MBUF programs, and compare them to the existing fuel tax, we leverage over 119 million records across a fifteen-year period, from annual vehicle inspections in Pennsylvania, to develop high-resolution estimates of the annual cost to vehicle owners of fuel taxes, and of MBUF’s at various rates. Applying numerous data cleaning and analytical methods, we use odometer readings from subsequent vehicle inspection records to assess annual vehicle miles travelled (VMT) per vehicle aggregated at the state, county, and ZIP code level. Web-scraping was used to assess the fuel economy of each vehicle in the records and develop estimates for fleetwide fuel economy in each area. Based on these estimates, we find that fees would vary by county and ZIP code between ¢2.4 and ¢3.2 per mile, to cost vehicle-owners the same as the existing fuel tax. We also find that vehicles registered in urban areas travel 10-30% fewer miles per year and tend to consume about 10% less fuel per year than average. Our results show that a shift to MBUF’s will in general lead to drivers in urban areas, and drivers of hybrid electric vehicles, paying a higher amount than they currently do, while drivers in suburban and rural counties will spend less each year.
Modeling Long-Distance Travel Flexibility: Results From A National Survey In The Context of COVID-19
Fernando Cordero, Auburn UniversityShow Abstract
Mitchell Fisher, Auburn University
Jeffrey LaMondia, Auburn University
Long-distance travel represents an important travel market for intercity and statewide planning, sustainability, and equity. However, most research on travel demand modeling focuses only on intracity travel. Traveler’s behavior in long-distance travel is key to the development of enhanced forecast models. Likewise, the study of flexibility in the decision-making process is a valuable source of data to improve these models. This research applies two mixed multinomial logit models (MNL) to derive the influential factors affecting a) the decision to cancel or take a long-distance trip and b) the choice between six alternatives to reschedule a long-distance trip. The paper uses data collected in the context of the novel coronavirus pandemic using this crisis as a lens to understand flexibility in the decision-making process of long-distance travel. The results show that personal and travel-related characteristics are influential to the choice of alternatives.
Long-Distance Travel as an Extension of Everyday Life: Understanding Distinct Traveler Types
Miriam Magdolen, Karlsruhe Institute of Technology (KIT)Show Abstract
Lisa Boenisch, Karlsruhe Institute of Technology (KIT)
Bastian Chlond, Karlsruhe Institute of Technology
Peter Vortisch, Karlsruhe Institute of Technology (KIT)
With the growing relevance of long-distance travel and the resulting climate impacts, the understanding of long-distance travel next to everyday travel becomes relevant. In particular in urban areas, people often compensate short distances and the use of environmentally friendly means of transport in everyday life with a higher amount of long-distance travel. The question arises how to characterize the behavior of urban people considering both everyday and long-distance travel behavior. Of interest is, whether there are discrepancies or similarities between the both kinds of travel, especially regarding mode choice. The demand for a car may not only result from daily mobility needs but from the extension of everyday life with long-distance travel. With our paper, we present a typology of distinct travel types, that considers characteristics of everyday travel and long-distance travel as well as attitudes. By using data from a survey in Munich (Germany), we analyzed the relevance of long-distance journeys with short durations, such as weekend trips, as an extension of everyday life. For the segmentation, characteristics of everyday travel, long-distance travel and attitudes towards the car and public transit were simultaneously included in a cluster analysis. Seven traveler types were identified and compared to each other. The results show that traveler types exist that are very similar in everyday travel behavior but show completely different characteristics in terms of long-distance travel volumes and mode choice. Furthermore, we also see that for some traveler types, the car exclusively plays a role for trips that extend everyday life.
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