A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Unravel the impact of COVID-19 on the spatio-temporal mobility patterns of microtransit
Abstract Shared mobility is an essential component of the larger sharing economy. Ride-hailing, bike-sharing, e-scooters, and other types of shared mobility continue to grow worldwide. Among these services is microtransit, a new transport mode that extends transit coverage within a region. Mobile devices enable microtransit services, aggregating riders and using real-time routing algorithms to group customers traveling in similar directions. Meanwhile, the newly emerged coronavirus, COVID-19, has radically reshaped the ridership behavior of all transit services, including microtransit. While existing research evaluates the performance of microtransit pilot programs before the pandemic, there is no information concerning the spatio-temporal pattern of microtransit activities under the impact of COVID-19. The purpose of this paper is to apply eigendecomposition and k-clique percolation methods to uncover the spatio-temporal patterns of microtransit trips. Further, we used these approaches to identify underlying communities using data from a pilot program in Salt Lake City, Utah. The resulting research offers insight into how COVID-19 altered travel behavior. Specifically, eigendecomposition delineated the homogeneity and heterogeneity of travel patterns across temporal dimensions. We identified first mile/last mile trips as a major source of variance in both pre- and post-COVID periods and that transit-dependent users prove to be inelastic despite the threat of COVID-19. The k-clique percolation method detected possible community formations and tracked how these communities evolved during the pandemic. In addition, we systematically analyzed overlapping communities and the network structure around shared nodes by using a clustering coefficient. The workflow developed in this research broadly is generalizable and valuable for understanding the unique spatio-temporal patterns of microtransit. The framework can also help transit agencies with performance evaluation, regional transport strategies, and optimal vehicle dispatching.
Unravel the impact of COVID-19 on the spatio-temporal mobility patterns of microtransit
Abstract Shared mobility is an essential component of the larger sharing economy. Ride-hailing, bike-sharing, e-scooters, and other types of shared mobility continue to grow worldwide. Among these services is microtransit, a new transport mode that extends transit coverage within a region. Mobile devices enable microtransit services, aggregating riders and using real-time routing algorithms to group customers traveling in similar directions. Meanwhile, the newly emerged coronavirus, COVID-19, has radically reshaped the ridership behavior of all transit services, including microtransit. While existing research evaluates the performance of microtransit pilot programs before the pandemic, there is no information concerning the spatio-temporal pattern of microtransit activities under the impact of COVID-19. The purpose of this paper is to apply eigendecomposition and k-clique percolation methods to uncover the spatio-temporal patterns of microtransit trips. Further, we used these approaches to identify underlying communities using data from a pilot program in Salt Lake City, Utah. The resulting research offers insight into how COVID-19 altered travel behavior. Specifically, eigendecomposition delineated the homogeneity and heterogeneity of travel patterns across temporal dimensions. We identified first mile/last mile trips as a major source of variance in both pre- and post-COVID periods and that transit-dependent users prove to be inelastic despite the threat of COVID-19. The k-clique percolation method detected possible community formations and tracked how these communities evolved during the pandemic. In addition, we systematically analyzed overlapping communities and the network structure around shared nodes by using a clustering coefficient. The workflow developed in this research broadly is generalizable and valuable for understanding the unique spatio-temporal patterns of microtransit. The framework can also help transit agencies with performance evaluation, regional transport strategies, and optimal vehicle dispatching.
Unravel the impact of COVID-19 on the spatio-temporal mobility patterns of microtransit
Zhou, Yirong (author) / Liu, Xiaoyue Cathy (author) / Grubesic, Tony (author)
2021-11-03
Article (Journal)
Electronic Resource
English