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• We analyze the cycling behavior of over 2,400 New Jersey adults before, during, and after the pandemic. • Half of the cyclists report that they will cycle more post-COVID, compared to before the pandemic began. • Working from home was associated with purchasing a bicycle during the pandemic, and increased cycling during and expected after the pandemic. • Changes to the infrastructure (e.g., pop-up bike lanes, lane closures) are positively associated with cycling. Anecdotal evidence suggests that cycling boomed during the early stages of the COVID-19 pandemic. Many shops ran out of bicycles for sale. However, it is not known whether increased cycling activity has continued. Using evidence from two representative online panel surveys, comprising over 2,400 New Jersey residents, we analyze bicycle ownership, purchasing, usage during the pandemic and intention to use after the pandemic subsides. We estimate multivariate models and find that the odds that a respondent intends to cycle more in the future (compared to prior to the pandemic) are 4.5 times higher if they have noticed positive changes to their neighborhood during the pandemic. Moreover, the odds that a respondent intends to cycle more are two times higher if they worked from home, compared to working outside the home. Our findings suggest that infrastructure changes successfully induced cycling during the pandemic and increased the intention to cycle in the future. Additionally, because the uptake in cycling has been larger for people who work from home, we highlight the importance of focusing on bicycle infrastructure related to recreation and exercise, in addition to commuting. [ABSTRACT FROM AUTHOR]

• Non-motorist involved crashes occur disproportionately more in low-income and minority communities. • Non-motorist involved crashes are less likely to be geocoded in low-income and minority communities. • Proximity to sidewalks and crosswalks significantly reduces the likelihood that a pedestrian-involved crash is fatal. • Cyclist involved crashes in low-income neighborhoods are more likely to be fatal. Introduction: We analyze and compare the factors that influence the fatality of pedestrian and bicyclist involved crashes in New Jersey using available police-reported crash data between 2016 and 2020. Under three percent of crashes involve non-motorists statewide, but these account for about one third of all traffic fatalities in the state. Methods: Our analysis is broken down into five parts: we (1) analyze the relationship between minority and low-income communities and non-motorist involved crashes; (2) identify spatial differences between non-motorist involved crashes and non-motorist involved fatal crashes; (3) compare the factors affecting fatal pedestrian crashes in New Jersey and in four counties in southern New Jersey for which we have data on pedestrian infrastructure; (4) compare the factors affecting fatal pedestrian crashes and fatal cyclist crashes in New Jersey; and, (5) discuss priority areas for improving safety. Results: Crashes occur disproportionately more often in low-income communities. Moreover, we find that crashes are less likely to be geocoded if they take place in low-income and minority areas, a concerning finding considering that geocoded crashes are of paramount importance in identifying specific corridors for improvement. Light conditions, non-motorist age, posted speed, and vehicle type are significant factors influencing the fatality of non-motorist involved crashes. The proximity to a crosswalk or sidewalk is associated with decreased risk of a fatal crash for pedestrians. Cyclist crashes in low-income neighborhoods were more likely to be fatal – a finding that we attribute to lower access to bicycle facilities in low-income areas. Conclusions: We conclude with countermeasures, including a call for better data collection. [ABSTRACT FROM AUTHOR]

• Hourly number of trips and median duration of trips for Dockless Scooter Share (DSS) and Station Based Bikeshare (SBBS) are analyzed. • DSS users behave more similarly to non-member SBBS users than to member SBBS users. • Weather is less of a disutility for DSS users than for SBBS users, regardless of membership type. • All micromobility user types are positively impacted by changing gas prices, although DSS users are more sensitive both in terms of trip count and median duration. • DSS and non-member SBBS users appear to have a competing relationship while DSS and member SBBS users appear to have a complementary relationship. Dockless, or free-floating mobility has gained unprecedented popularity in the last year, from being virtually non-existent in 2017 to facilitating over 38.5 million trips in 2018. Hitherto, few studies have analyzed dockless micromobility, and scooter-share particularly using big data. This paper analyzes and compares the determinants of dockless scooters-share (DSS) and of station-based bike-share (SBBS) rides in D.C. It made use of API data from dockless vendors and historical trip data from Capital Bikeshare from December 2018 to June 2019. Two variables were estimated: hourly number of trips and hourly median duration of trips. A negative-binomial regression model was performed at the hourly scale controlling for environmental and economic variables including weather-related data, gasoline prices, local events or disturbances, day of week, and time of day. Four groups were analyzed: all of micromobility combined and weighed, SBBS members, SBBS non-members, and DSS. Three important findings emerged: (1) Temporal use differences between the three user groups were found, but DSS users behave most similarly to SBBS non-members. (2) Weather is less of a disutility for DSS users than for SBBS users. We attribute this to the physical ease of using a scooter and to the convenience of ending a trip at the actual destination rather than a nearby docking station. (3) All micromobility user types are sensitive to changing gas prices, although DSS users appear slightly more sensitive both in terms of trip count and duration. Additionally, an analysis of the interaction between modes found a possible competition between DSS and SBBS non-members and a complementary relationship between DSS and SBBS members. We conclude that significant differences exist between the two modes, and combined with its sudden and rising popularity, micromobility and DSS in particular could have a major role in promoting a shift towards low-carbon mobility. [ABSTRACT FROM AUTHOR]

Transportation disruptions offer opportunities to study how people adapt to using new modes of transportation and have important implications for transportation policy and planning. Bikeshare has emerged as a new popular mode of transportation in recent years as it offers a fast, easy, and reliable way to travel short distances, and for its convenience as a first- and last-mile mode to complement transit. It also offers many social, environmental, and health-related benefits and has the potential to promote low-carbon mobility. This study examines changes in bikeshare ridership due to rail transit closures in the Washington, D.C. area and investigates how promoting bikeshare systems in large metropolitan areas could be beneficial in cases of transit disruptions – regardless of the type, cause, and duration. We use disaggregate trip history data to analyze the impact of three different transit closures in 2016 lasting 7 to 25 days. The objective of this paper is to provide insight on how transit disruptions affect bikeshare use. An autoregressive Poisson time series model is used to estimate effects of transit closures on bikeshare activity. Kernel density estimation is applied to understand spatial changes in ridership from a week before, one year before, and after each closure. Results are compared both temporally and spatially and confirm that transit disruptions were associated with increased bikeshare ridership at the local level. Once the affected Metro stations reopened, bikeshare ridership returned to original levels. We conclude that when within 0.25 mile of a rail station and with a rail station spacing of <3 miles, bikeshare can be used as a mechanism for low-carbon mobility to complement transit. [ABSTRACT FROM AUTHOR]

• A dynamic model structure was designed to delineate spatiotemporal evolution of health disparities. • The paper analyzed racial disparities in human mobility and COVID-19 health outcomes jointly. • Confounding effects from built environment, socioeconomics, demographics, and partisanship on racial disparities have been quantified. • The study found a persistent and disproportionate burden of COVID-19 suffered by racial minorities. Social distancing has become a key countermeasure to contain the dissemination of COVID-19. This study examined county-level racial/ethnic disparities in human mobility and COVID-19 health outcomes during the year 2020 by leveraging geo-tracking data across the contiguous US. Sets of generalized additive models were fitted under cross-sectional and time-varying settings, with percentage of mobility change, percentage of staying home, COVID-19 infection rate, and case-fatality ratio as dependent variables, respectively. After adjusting for spatial effects, built environment, socioeconomics, demographics, and partisanship, we found counties with higher Asian populations decreased most in travel, counties with higher White and Asian populations experienced the least infection rate, and counties with higher African American populations presented the highest case-fatality ratio. Control variables, particularly partisanship and education attainment, significantly influenced modeling results. Time-varying analyses further suggested racial differences in human mobility varied dramatically at the beginning but remained stable during the pandemic, while racial differences in COVID-19 outcomes broadly decreased over time. All conclusions hold robust with different aggregation units or model specifications. Altogether, our analyses shine a spotlight on the entrenched racial segregation in the US as well as how it may influence the mobility patterns, urban forms, and health disparities during the COVID-19. [ABSTRACT FROM AUTHOR]

• A big-data-driven analytical framework assessing human mobility during COVID-19. • Location-based evidence from large-scale and privacy-protected samples in the US. • County-level human mobility metrics are made publicly available via an open platform. • Comprehensive assessment of human mobility using generalized additive mixed models. • The policy effect of "Stay-at-home" order on restricting travel is found limited. During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions became a widely adopted strategy to limit physical movements and interactions to mitigate virus transmissions. For situational awareness and decision-support, quickly available yet accurate big-data analytics about human mobility and social distancing is invaluable to agencies and decision-makers. This paper presents a big-data-driven analytical framework that ingests terabytes of data on a daily basis and quantitatively assesses the human mobility trend during COVID-19. Using mobile device location data of over 150 million monthly active samples in the United States (U.S.), the study successfully measures human mobility with three main metrics at the county level: daily average number of trips per person; daily average person-miles traveled; and daily percentage of residents staying home. A set of generalized additive mixed models is employed to disentangle the policy effect on human mobility from other confounding effects including virus effect, socio-demographic effect, weather effect, industry effect, and spatiotemporal autocorrelation. Results reveal the policy plays a limited, time-decreasing, and region-specific effect on human movement. The stay-at-home orders only contribute to a 3.5%-7.9% decrease in human mobility, while the reopening guidelines lead to a 1.6%-5.2% mobility increase. Results also indicate a reasonable spatial heterogeneity among the U.S. counties, wherein the number of confirmed COVID-19 cases, income levels, industry structure, age and racial distribution play important roles. The data informatics generated by the framework are made available to the public for a timely understanding of mobility trends and policy effects, as well as for time-sensitive decision support to further contain the spread of the virus. [ABSTRACT FROM AUTHOR]