ABSTRACT
The recent worldwide SARS-CoV-2 (COVID-19) pandemic has reshaped the way people live, how they access goods and services, and how they perform various activities. For public transit, there have been health concerns over the potential spread to transit users and transit service staff, which prompted transportation agencies to make decisions about the service, e.g., whether to reduce or completely shut down services. These decisions had substantial negative consequences, especially for transit-dependent residents, and prompted transit users to explore alternative transport modes, e.g., bikeshare. However, local governments and the public in general have limited information about whether and to what extent bikeshare provided adequate accessibility and mobility to those transit-dependent residents. To fill this gap, this study implemented spatial and visual analytics to identify how micro-mobility in the form of bikesharing has addressed travel needs and improved the resilience of transportation systems. Specifically, the study analyzed the case of San Francisco in California, USA, and selected three phases of the pandemic, i.e., initial confirmed cases, shelter-in-place, and initial changes in transit service. First, the authors implemented unsupervised machine learning clustering methods to identify different trip types. Moreover, through spatiotemporally matching bikeshare ridership data with transit service information (i.e., General Transit Feed Specification, GTFS) using the tool called OpenTripPlanner (OTP), the authors studied the travel behavior changes (e.g., the proportion of bikeshare trips that could be finished by transit) for different bikeshare trip types over the three specified phases. This study revealed that during the pandemic, more casual users joined bikeshare programs;recreation-related bikeshare trips increased in terms of the proportions;and, routine trips became more prevalent considering that docking-station-based bikeshare trips increased. More importantly, the analyses also provided insights about mode substitution, because the analyses identified an increase in dockless bikeshare trips in areas with no or limited transit coverage.
ABSTRACT
The recent worldwide SARS-CoV-2 (COVID-19) pandemic has reshaped the way people live, how they access goods and services, and how they perform various activities. For public transit, there have been health concerns over the potential spread to transit users and transit service staff, which prompted transportation agencies to make decisions about the service, e.g., whether to reduce or temporarily shut down services. These decisions had substantial negative consequences, especially for transit-dependent travelers, and prompted transit users to explore alternative transportation modes, e.g., bikeshare. However, local governments and the public in general have limited information about whether and to what extent bikeshare provides adequate accessibility and mobility to those transit-dependent residents. To fill this gap, this study implemented spatial and visual analytics to identify how micro-mobility in the form of bikesharing has addressed travel needs and improved the resilience of transportation systems. The study analyzed the case of San Francisco in California, USA, focusing on three phases of the pandemic, i.e., initial confirmed cases, shelter-in-place, and initial changes in transit service. First, the authors implemented unsupervised machine learning clustering methods to identify different bikesharing trip types. Moreover, through spatiotemporally matching bikeshare ridership data with transit service information (i.e., General Transit Feed Specification, GTFS) using the tool called OpenTripPlanner (OTP), the authors studied the travel behavior changes (e.g., the proportion of bikeshare trips that could be finished by transit) for different bikeshare trip types over the three specified phases. This study revealed that during the pandemic, more casual users joined bikeshare programs; the proportion of recreation-related bikeshare trips increased; and routine trips became more prevalent considering that docking-station-based bikeshare trips increased. More importantly, the analyses also provided insights about mode substitution, because the analyses identified an increase in dockless bikeshare trips in areas with no or limited transit coverage.
ABSTRACT
The COVID-19 pandemic led to a significant breakdown of the traditional retail sector resulting in an unprecedented surge in e-commerce demand for the delivery of essential goods. Consequently, the pandemic raised concerns pertaining to e-retailers' ability to maintain and efficiently restore level of service in the event of such low-probability high-severity market disruptions. Thus, considering e-retailers' role in the supply of essential goods, this study assesses the resilience of last-mile distribution operations under disruptions by integrating a Continuous Approximation (CA) based last-mile distribution model, the resilience triangle concept, and the Robustness, Redundancy, Resourcefulness, and Rapidity (R4) resilience framework. The proposed R4 Last Mile Distribution Resilience Triangle Framework is a novel performance-based qualitative-cum-quantitative domain-agnostic framework. Through a set of empirical analyses, this study highlights the opportunities and challenges of different distribution/outsourcing strategies to cope with disruption. In particular, the authors analyzed the use of an independent crowdsourced fleet (flexible service contingent on driver availability); the use of collection-point pickup (unconstrained downstream capacity contingent on customer willingness to self-collect); and integration with a logistics service provider (reliable service with high distribution costs). Overall, this work recommends the e-retailers to create a suitable platform to ensure reliable crowdsourced deliveries, position sufficient collection-points to ensure customer willingness to self-collect, and negotiate contracts with several logistics service providers to ensure adequate backup distribution.
ABSTRACT
This paper proposes a text analytics approach using dictionary-based clustering, word counting data, and discrete regression modeling to study the relationship between demand behaviors and supply issues affecting supply chain resilience during the first stages of the COVID-19 pandemic. The work used news and media articles gathered from the LexisNexis database covering 5 months between February and July 2020. The data analyses describe the general patterns in the news texts by using text mining techniques, and the methodology describes the relationship between consumer behavior, supply chain issues, and the reduced level of service shown during the study period. Demand behaviors include precautionary and opportunistic buying, which affected many countries and could be the result of a lack of perceived control and other factors;for example, near-empty shelves of certain products could have prompted consumers to increasingly look for comparable products, driving up demand. Additionally, the method explored the potential effect of strategies to mitigate impacts on the resilience of supply chains. The results confirmed that buying behaviors and a reduction in the capacity of the supply chain led to a lower level of service being perceived by consumers, however, resilience strategies were found to mitigate the impact of such capacity reductions. Empirical analyses showed that the proposed approach, using data extracted from the news, could identify and represent impacts consistent with expectations from the supply chain field under disruptions, and quantify the magnitude of the impacts as the pandemic evolved, providing more information for decision making.
ABSTRACT
Panic buying during pandemics and natural disasters are common and can lead to shortages of essential products and services. This project aims to characterize pre-cautionary and opportunistic shopping behaviors during the COVID-19 pandemic, and propose solutions in terms of new delivery business models (e.g. crowd shipping), delivery strategies (e.g., pick-up/drop-off delivery zones) and technology-driven delivery modes (e.g., AVs and robots).