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By March of 2020, most cities worldwide had enacted stay-at-home public health orders to slow the spread of COVID-19. Restrictions on nonessential travel had extensive impacts across the transportation sector in the short term. This study explores the effects of COVID-19 on shared e-scooters by analyzing route trajectory data in the pre- and during-pandemic periods in Austin, TX, from a single provider. Although total shared e-scooter trips decreased during the pandemic, partially owing to vendors pulling out of the market, this study found average trip length increased, and temporal patterns of this mode did not meaningfully change. A count model of average daily trips by road segment found more trips on segments with sidewalks and bus stops during the pandemic than beforehand. More trips were observed on roads with lower vehicle miles traveled and fewer lanes, which might suggest more cautious travel behavior since there were fewer trips in residential neighborhoods. Stay-at-home orders and vendor e-scooter rebalancing operations inherently influence and can limit trip demand, but the unique trajectory data set and analysis provide cities with information on the road design preferences of vulnerable road users.

Bridge decks deteriorate faster compared to other bridge components, primarily influenced by traffic volume, while previous studies have examined the effect of bridge-wearing surfaces on deterioration, further understanding of the relationship between bridge performance and maintenance is needed for policy-making and planning purposes. In this study, we focus on nine influential variables to unravel the intricate connections among performance, deterioration, and maintenance of six distinct bridge-wearing surfaces: Monolithic Concrete, Gravel, Wood or Timber, Bituminous, Low Surface Concrete, and Other. Statistical analyses were employed to determine associations between variables and concepts, exploring similarities and differences across various wearing surface types. In particular, machine learning algorithms were utilized to model the maintenance considering the performance and deterioration of the six diverse wearing surfaces. This approach allowed for an examination of interactions between those variables and concepts. We further applied a well-performing prediction model (which achieved an accuracy of 0.86 and an AUC score of approximately 0.83) to obtain interpretable insights regarding bridge deck surfaces. Analysis with interpretable methods such as SHAP (Shapley additive explanation) and PDP (partial dependency plot) revealed that deterioration, deck age, deck area, and overall performance were the most influential variables among average daily traffic, average daily truck traffic, and the number of spans significantly influenced the maintenance of bridge deck condition with different wearing surfaces. Notably, a strong relationship between performance and maintenance was observed in specific wearing surface types, such as Monolithic Concrete and Wood or Timber, while Other surface types exhibited different patterns. These findings highlight the need for tailored approaches when assessing bridge health, considering the distinct characteristics of different bridge deck types. [ABSTRACT FROM AUTHOR]

Bridge decks deteriorate faster compared to other bridge components, primarily influenced by traffic volume, while previous studies have examined the effect of bridge-wearing surfaces on deterioration, further understanding of the relationship between bridge performance and maintenance is needed for policy-making and planning purposes. In this study, we focus on nine influential variables to unravel the intricate connections among performance, deterioration, and maintenance of six distinct bridge-wearing surfaces: Monolithic Concrete, Gravel, Wood or Timber, Bituminous, Low Surface Concrete, and Other. Statistical analyses were employed to determine associations between variables and concepts, exploring similarities and differences across various wearing surface types. In particular, machine learning algorithms were utilized to model the maintenance considering the performance and deterioration of the six diverse wearing surfaces. This approach allowed for an examination of interactions between those variables and concepts. We further applied a well-performing prediction model (which achieved an accuracy of 0.86 and an AUC score of approximately 0.83) to obtain interpretable insights regarding bridge deck surfaces. Analysis with interpretable methods such as SHAP (Shapley additive explanation) and PDP (partial dependency plot) revealed that deterioration, deck age, deck area, and overall performance were the most influential variables among average daily traffic, average daily truck traffic, and the number of spans significantly influenced the maintenance of bridge deck condition with different wearing surfaces. Notably, a strong relationship between performance and maintenance was observed in specific wearing surface types, such as Monolithic Concrete and Wood or Timber, while Other surface types exhibited different patterns. These findings highlight the need for tailored approaches when assessing bridge health, considering the distinct characteristics of different bridge deck types.

COVID-19 has radically transformed urban travel behavior throughout the world. Agencies have had to provide adequate service while navigating a rapidly changing environment with reduced revenue. As COVID-19-related restrictions are lifted, transit agencies are concerned about their ability to adapt to changes in ridership behavior and public transit usage. To aid their becoming more adaptive to sudden or persistent shifts in ridership, we addressed three questions: To what degree has COVID-19 affected fixed-line public transit ridership and what is the relationship between reduced demand and -vehicle trips? How has COVID-19 changed ridership patterns and are they expected to persist after restrictions are lifted? Are there disparities in ridership changes across socioeconomic groups and mobility-impaired riders? Focusing on Nashville and Chattanooga, TN, ridership demand and vehicle trips were compared with anonymized mobile location data to study the relationship between mobility patterns and transit usage. Correlation analysis and multiple linear regression were used to investigate the relationship between socioeconomic indicators and changes in transit ridership, and an analysis of changes in paratransit demand before and during COVID-19. Ridership initially dropped by 66% and 65% over the first month of the pandemic for Nashville and Chattanooga, respectively. Cellular mobility patterns in Chattanooga indicated that foot traffic recovered to a greater degree than transit ridership between mid-April and the last week in June, 2020. Education-level had a statistically significant impact on changes in fixed-line bus transit, and the distribution of changes in demand for paratransit services were similar to those of fixed-line bus transit., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© National Academy of Sciences: Transportation Research Board 2023.)

In this study, we proposed a GIS-based approach to analyzing hospital visitors from January to June 2019 and January to June 2020 with the goal of revealing significant changes in the visitor demographics. The target dates were chosen to observe the effect of the first wave of COVID-19 on the visitor count in hospitals. The results indicated that American Indian and Pacific Islander groups were the only ones that sometimes showed no shift in visitor levels between the studied years. For 19 of the 28 hospitals in Austin, TX, the average distance traveled to those hospitals from home increased in 2020 compared with 2019. A hospital desert index was devised to identify the areas in which the demand for hospitals is greater than the current hospital supply. The hospital desert index considers the travel time, location, bed supply, and population. The cities located along the outskirts of metropolitan regions and rural towns showed more hospital deserts than dense city centers., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© National Academy of Sciences: Transportation Research Board 2022.)

The continued spread of COVID-19 poses significant threats to the safety of the community. Since it is still uncertain when the pandemic will end, it is vital to understand the factors contributing to new cases of COVID-19, especially from the transportation perspective. This paper examines the effect of the United States residents' daily trips by distances on the spread of COVID-19 in the community. The artificial neural network method is used to construct and test the predictive model using data collected from two sources: Bureau of Transportation Statistics and the COVID-19 Tracking Project. The dataset uses ten daily travel variables by distances and new tests from March to September 2020, with a sample size of 10,914. The results indicate the importance of daily trips at different distances in predicting the spread of COVID-19. More specifically, trips shorter than 3 mi and trips between 250 and 500 mi contribute most to predicting daily new cases of COVID-19. Additionally, daily new tests and trips between 10 and 25 mi are among the variables with the lowest effects. This study's findings can help governmental authorities evaluate the risk of COVID-19 infection based on residents' daily travel behaviors and form necessary strategies to mitigate the risks. The developed neural network can be used to predict the infection rate and construct various scenarios for risk assessment and control., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© National Academy of Sciences: Transportation Research Board 2022.)

The COVID-19 pandemic in 2020 has caused sudden shocks in transportation systems, specifically the subway ridership patterns in New York City (NYC), U.S. Understanding the temporal pattern of subway ridership through statistical models is crucial during such shocks. However, many existing statistical frameworks may not be a good fit to analyze the ridership data sets during the pandemic, since some of the modeling assumptions might be violated during this time. In this paper, utilizing change point detection procedures, a piecewise stationary time series model is proposed to capture the nonstationary structure of subway ridership. Specifically, the model consists of several independent station based autoregressive integrated moving average (ARIMA) models concatenated together at certain time points. Further, data-driven algorithms are utilized to detect the changes of ridership patterns as well as to estimate the model parameters before and during the COVID-19 pandemic. The data sets of focus are daily ridership of subway stations in NYC for randomly selected stations. Fitting the proposed model to these data sets enhances understanding of ridership changes during external shocks, both in relation to mean (average) changes and the temporal correlations., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© National Academy of Sciences: Transportation Research Board 2022.)

COVID-19 has shocked every system in the U.S., including transportation. In the first months of the pandemic, driving and transit use fell far below normal levels. Yet people still need to travel for essential purposes like medical appointments, buying groceries, and-for those who cannot work from home-to work. For some, the pandemic may exacerbate extant travel challenges as transit agencies reduce service hours and frequency. As travelers reevaluate modal options, it remains unclear how one mode-ride-hailing-fits into the transportation landscape during COVID-19. In particular, how does the number of ride-hail trips vary across neighborhood characteristics before versus during the pandemic? And how do patterns of essential trips pre-pandemic compare with those during COVID-19? To answer these questions, we analyzed aggregated Uber trip data before and during the first two months of the COVID-19 pandemic across four regions in California. We find that during these first months, ride-hail trips fell at levels commensurate with transit (82%), while trips serving identified essential destinations fell by less (62%). Changes in ride-hail use were unevenly distributed across neighborhoods, with higher-income areas and those with more transit commuters and higher shares of zero-car households showing steeper declines in the number of trips made during the pandemic. Conversely, neighborhoods with more older (aged 45+) residents, and a greater proportion of Black, Hispanic/Latinx, and Asian residents still appear to rely more on ride-hail during the pandemic compared with other neighborhoods. These findings further underscore the need for cities to invest in robust and redundant transportation systems to create a resilient mobility network., (© National Academy of Sciences: Transportation Research Board 2021.)

Transport plays a major role in spreading contagious diseases such as COVID-19 by facilitating social contacts. The standard response to fighting COVID-19 in most countries has been imposing a lockdown-including on the transport sector-to slow down the spread. Though the Government of Bangladesh also imposed a lockdown quite early, it was forced to relax the lockdown for economic reasons. This motivates this study to assess the interaction between various non-pharmaceutical intervention (NPI) policies and transport sector outcomes, such as mobility and accidents, in Bangladesh. The study explores the effect of NPIs on both intra- and inter-regional mobility. Intra-regional mobility is captured using Google mobility reports which provide information about the number of visitors at different activity locations. Inter-regional, or long-distance, mobility is captured using vehicle count information from toll booths on a major bridge. Modeling shows that, in most cases, the policy interventions had the desired impact on people's mobility patterns. Closure of education institutes, offices, public transport, and shopping malls reduced mobility at most locations. The closure of garment factories reduced mobility for work and at transit stations only. Mobility was increased at all places except at residential locations, after the wearing of masks was made mandatory. Reduced traffic because of policy interventions resulted in a lower number of accidents (crashes) and related fatalities. However, mobility-normalized crashes and fatalities increased nationally. The outcomes of the study are especially useful in understanding the differential impacts of various policy measures on transport, and thus would help future evidence-based decision-making., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© National Academy of Sciences: Transportation Research Board 2022.)

The year 2020 has marked the spread of a global pandemic, COVID-19, challenging many aspects of our daily lives. Different organizations have been involved in controlling this outbreak. The social distancing intervention is deemed to be the most effective policy in reducing face-to-face contact and slowing down the rate of infections. Stay-at-home and shelter-in-place orders have been implemented in different states and cities, affecting daily traffic patterns. Social distancing interventions and fear of the disease resulted in a traffic decline in cities and counties. However, after stay-at-home orders ended and some public places reopened, traffic gradually started to revert to pre-pandemic levels. It can be shown that counties have diverse patterns in the decline and recovery phases. This study analyzes county-level mobility change after the pandemic, explores the contributing factors, and identifies possible spatial heterogeneity. To this end, 95 counties in Tennessee have been selected as the study area to perform geographically weighted regressions (GWR) models. The results show that density on non-freeway roads, median household income, percent of unemployment, population density, percent of people over age 65, percent of people under age 18, percent of work from home, and mean time to work are significantly correlated with vehicle miles traveled change magnitude in both decline and recovery phases. Also, the GWR estimation captures the spatial heterogeneity and local variation in coefficients among counties. Finally, the results imply that the recovery phase could be estimated depending on the identified spatial attributes. The proposed model can help agencies and researchers estimate and manage decline and recovery based on spatial factors in similar events in the future., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© National Academy of Sciences: Transportation Research Board 2021.)

Highway fatalities are a leading cause of death in the U.S. and other industrialized countries. Using highly detailed crash, speed, and flow data, we show highway travel and motor vehicle crashes fell substantially in California during the response to the COVID-19 pandemic. However, we also show the frequency of severe crashes increased owing to lower traffic congestion and higher highway speeds. This "speed effect" is largest in counties with high pre-existing levels of congestion, and we show it partially or completely offsets the "VMT effect" of reduced vehicle miles traveled on total fatalities. During the first eleven weeks of the COVID-19 response, highway driving decreased by approximately 22% and total crashes decreased by 49%. While average speeds increased by a modest 2 to 3 mph across the state, they increased between 10 and 15 mph in several counties. The proportion of severe crashes increased nearly 5 percentage points, or 25%. While fatalities decreased initially following restrictions, increased speeds mitigated the effect of lower vehicle miles traveled on fatalities, yielding little to no reduction in fatalities later in the COVID period., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© National Academy of Sciences: Transportation Research Board 2022.)

U.S. container ports have experienced unpresented congestion since mid-2020. The congestion is generally attributed to import surges triggered by heavy spending on consumer goods during the COVID-19 pandemic. Port congestion has been compounded by the inability of importers to retrieve, receive, and process all the inbound goods they have ordered, resulting in supply chain shortfalls and economic disruption. How can the shipping industry and government organizations predict the end of the current surge and anticipate future surges? Expected seasonal variations in import volume are associated with peak holiday shopping periods; nonseasonal import surges are signaled by other factors. The research goes beyond transportation data sources to examine broader connections between import volume and indicators of economic and retail industry conditions. The strongest and most useful relationship appears to be between retail inventory indicators and containerized import growth. From January 2018 through July 2021, there was a relatively strong negative correlation between retail inventory- and import TEU indices with a 4-month lag (corresponding roughly to the time between import orders and -arrival). In the 2020 to 2021 pandemic period the negative correlation was stronger, again with a 4-month lag. These findings suggest that observers might anticipate import surges after marked, nonseasonal drops in retail inventories, and that import surges are likely to last until target inventory levels are restored. In a broader sense, an awareness of the linkages between consumer demand, retail chain responses, and containerized import volumes could better inform port, freight transportation, and government planning and policy choices., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© National Academy of Sciences: Transportation Research Board 2022.)