Short-Duration Crash Modeling to Understand the Impact of Operating Speed on Freeway Crashes During COVID-19

Gaining an understanding of speed–crash relationships is a critical issue in highway safety research. Because of the ongoing pandemic (COVID-19) there has been a reduction in traffic volume, and some early studies explain that speeding in an environment with less traffic is associated with a high number of crashes, especially fatal and serious injury crashes. This study aims to quantify the impact of operating speed on traffic crash occurrences. The study conflated several databases (speed data, roadway inventory data, and crash data) that contain data from Dallas, Texas, spanning from 2018 to 2020, to examine the speed–crash association. Using the negative binomial Lindley regression model, this study showed that the trends of crash prediction models vary over the years (2018, 2019, and 2020) by different injury severity levels (i.e., fatal crashes, fatal and incapacitating injury crashes). The 2020 models show that operating speed measures (i.e., average operating speed) have a significant impact on crash frequencies. The magnitudes of the speed measures show variations across the models at different injury severity levels.

Metrics of Mobility: Assessing the Impact of COVID-19 on Travel Behavior

The COVID-19 pandemic disrupted typical travel behavior worldwide. In the United States (U.S.), government entities took action to limit its spread through public health messaging to encourage reduced mobility and thus reduce the spread of the virus. Within statewide responses to COVID-19, however, there were different responses locally. Likely some of these variations were a result of individual attitudes toward the government and health messaging, but there is also likely a portion of the effects that were because of the character of the communities. In this research, we summarize county-level characteristics that are known to affect travel behavior for 404 counties in the U.S., and we investigate correlates of mobility between April and September (2020). We do this through application of three metrics that are derived via changepoint analysis—initial post-disruption mobility index, changepoint on restoration of a ‘‘new normal,'' and recovered mobility index. We find that variables for employment sectors are significantly correlated and had large effects on mobility during the pandemic. The state dummy variables are significant, suggesting that counties within the same state behaved more similarly to one another than to counties in different states. Our findings indicate that few travel characteristics that typically correlate with travel behavior are related to pandemic mobility, and that the number of COVID-19 cases may not be correlated with mobility outcomes. © National Academy of Sciences: Transportation Research Board 2021.

Impacts of COVID-19 on Future Preferences Toward Telework

This paper presents a study in capturing the impacts of the mandatory pandemic-induced telework practice on workers' perceptions of the benefits, challenges, and difficulties associated with telecommuting and how those might influence their preference for telework in the future. Data was collected through an online survey conducted in South Florida in May 2020. Survey data showed that telework indices (either measured through actual behavior or stated preference) before, during, and after the pandemic were heterogeneous across socio-economic, demographic, and attitudinal segments. Before the outbreak, males, full-time students, those with PhD degrees, and high-income people showed higher percentages of involvement in jobs with a telework option. They also had higher pro-technology, pro-online education, workaholic, and pro-telework attitudes. During the pandemic, professional/managerial/technical jobs as well as jobs with lower physical-proximity measures showed the highest telework frequency. In view of future telework preferences, our analysis showed that those who were more pro-telework, pro-technology, and showed less dislike of telework dislike preferred higher telework frequency. A structural equation model was developed to assess the impacts of different predictors on telework behavior before the pandemic and preferences after the pandemic. While telework frequency before the pandemic was highly affected by the pro-telework attitude, the after-pandemic preferences were influenced by several other attitudes such as dislike telework, enjoy interaction, workaholic, as well as productivity factors. This might confirm the assumption that the mandatory practice through the pandemic has provided employees more experiences with work-from-home arrangements, which could reshape decisions and expectations around telework adoption in the future. © National Academy of Sciences: Transportation Research Board 2021.

Quantitative Framework for Establishing Low-Risk Inter-District Travel Corridors During COVID-19.

Aspirations to slow down the spread of novel Coronavirus (COVID-19) resulted in unprecedented restrictions on personal and work-related travels in various nations across the globe in 2020. As a consequence, economic activities within and across the countries were almost halted. As restrictions loosen and cities start to resume public and private transport to revamp the economy, it becomes critical to assess the commuters' travel-related risk in light of the ongoing pandemic. The paper develops a generalizable quantitative framework to evaluate the commute-related risk arising from inter-district and intra-district travel by combining nonparametric data envelopment analysis for vulnerability assessment with transportation network analysis. It demonstrates the application of the proposed model for establishing travel corridors within and across Gujarat and Maharashtra, two Indian states that have reported many COVID-19 cases since early April 2020. The findings suggest that establishing travel corridors between a pair of districts solely based on the health vulnerability indices of the origin and destination discards the en-route travel risks from the prevalent pandemic, underestimating the threat. For example, while the resultant of social and health vulnerabilities of Narmada and Vadodara districts is relatively moderate, the en-route travel risk exacerbates the overall travel risk of travel between them. The study provides a quantitative framework to identify the alternate path with the least risk and hence establish low-risk travel corridors within and across states while accounting for social and health vulnerabilities in addition to transit-time related risks.

Mitigating Increased Driving after the COVID-19 Pandemic: An Analysis on Mode Share, Travel Demand, and Public Transport Capacity.

Reduced transit capacity to accommodate social distancing during the COVID-19 pandemic was a sudden constraint that along with a large reduction in total travel volume and a shift in activity patterns contributed to abrupt changes in transportation mode shares across cities worldwide. There are major concerns that as the total travel demand rises back toward prepandemic levels, the overall transport system capacity with transit constraints will be insufficient for the increasing demand. This paper uses city-level scenario analysis to examine the potential increase in post-COVID-19 car use and the feasibility of shifting to active transportation, based on prepandemic mode shares and varying levels of reduction in transit capacity. An application of the analysis to a sample of cities in Europe and North America is presented. Mitigating an increase in driving requires a substantial increase in active transportation mode share, particularly in cities with high pre-COVID-19 transit ridership; however, such a shift may be possible based on the high percentage of short-distance motorized trips. The results highlight the importance of making active transportation attractive and reinforce the value of multimodal transportation systems as a strategy for urban resilience. This paper provides a strategic planning tool for policy makers facing challenging transportation system decisions in the aftermath of the COVID-19 pandemic.

Health Care Logistics Network Design and Analysis on Pandemic Outbreaks: Insights From COVID-19.

Health care systems throughout the world are under pressure as a result of COVID-19. It is over two years since the first case was announced in China and health care providers are continuing to struggle with this fatal infectious disease in intensive care units and inpatient wards. Meanwhile, the burden of postponed routine medical procedures has become greater as the pandemic has progressed. We believe that establishing separate health care institutions for infected and non-infected patients would provide safer and better quality health care services. The aim of this study is to find the appropriate number and location of dedicated health care institutions which would only treat individuals infected by a pandemic during an outbreak. For this purpose, a decision-making framework including two multi-objective mixed-integer programming models is developed. At the strategic level, the locations of designated pandemic hospitals are optimized. At the tactical level, we determine the locations and operation durations of temporary isolation centers which treat mildly and moderately symptomatic patients. The developed framework provides assessments of the distance that infected patients travel, the routine medical services expected to be disrupted, two-way distances between new facilities (designated pandemic hospitals and isolation centers), and the infection risk in the population. To demonstrate the applicability of the suggested models, we perform a case study for the European side of Istanbul. In the base case, seven designated pandemic hospitals and four isolation centers are established. In sensitivity analyses, 23 cases are analyzed and compared to provide support to decision makers.

Equity Implications of Ride-Hail Travel during COVID-19 in California.

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.

Exploring Public Perception Toward Travel and COVID-19 Preventive Measures: Insights From the Early Stages of Lockdown in India.

The paper reports evidence related to peoples' perceptions and attitude during the early stages of COVID-19 related lockdown in India in four major aspects: strategies and preventive measures, long-distance travel, essential services, and post-lockdown travel. A five-stage survey instrument was designed and circulated through various online modes to make it convenient to the respondents and also to achieve a greater geographical coverage within a short period of time. The survey responses were analyzed using statistical tools and the results are translated into potential policy recommendations which may be useful to implement effective interventions during future pandemics of similar nature. The findings highlight a high rate of awareness among the people about the COVID-19, lack of supply of protective equipment such as masks, gloves, and personal protective equipment kits during the early stage of lockdown in India. However, several heterogeneities were also observed across a few socio-economic groups which emphasize the need for targeted campaigns in a diverse country such as India. The findings also suggest the need for arranging safe and hygienic long-distance trips for a section of the society when such lockdown measures are extended for long periods. The observations related to mode choice preferences during the post-lockdown recovery period indicate a potential shift of public transport patronage to the personal modes.

Effects of COVID-19 on Telework and Commuting Behavior: Evidence from 3 Years of Panel Data.

The COVID-19 pandemic has forced employers and employees to re-evaluate their attitudes toward telecommuting. This induced a change in the sheer number of people who have started to work from home (WFH). While previous studies highlight differences between telecommuters based on their level of telecommuting experience, these effects have not been studied in detail. This may limit the evaluation of implications for post-pandemic times and the transferability of models and predictions based on data collected during the COVID-19 pandemic. This study expands on previous findings by comparing the characteristics and behavior of those who have started to telecommute during the pandemic and those who had already telecommuted before. Furthermore, this study addresses the uncertainty that exists about whether the findings of studies conducted before the pandemic-for example about sociodemographic characteristics of telecommuters-still hold true, or if the pandemic induced a shift in telecommuters' profiles. Telecommuters show differences when considering their previous experience in WFH. The results of this study suggest that the transition induced by the pandemic was more drastic for new telecommuters compared with experienced telecommuters. The COVID-19 pandemic had an effect on how household configurations are considered in the choice to WFH. With decreased access to child care resulting from school closings, people with children in the household were more likely to choose to telecommute during the pandemic. Also, while people living alone are generally less likely to choose to WFH, this effect was reduced as a result of the pandemic.

Pedestrians and the Built Environment during the COVID-19 Pandemic: Changing Relationships by the Pandemic Phases in Salt Lake County, Utah, U.S.A.

The COVID-19 pandemic has dramatically altered people's travel behavior, in particular outdoor activities, including walking. Their behavior changes may have prolonged effects after the pandemic, and such changes vary by the context and are related to the characteristics of the built environment. But empirical studies about the relationships between pedestrians and the built environment during the pandemic are lacking. This study explores how COVID-19 and related travel restrictions have affected the relationship between pedestrian traffic volume and the built environment. We estimate daily pedestrian volumes for all signalized intersections in Salt Lake County, Utah, U.S.A., from pedestrian push-button log data between January 2019 and October 2020. Multilevel spatial filtering models show that the COVID-19 pandemic has altered the relationship between pedestrian traffic volume and the built environment. During the pandemic, the higher the number of COVID-19 cases, the less (or more negative) the effects of density, street connectivity, and destination accessibility on pedestrian volume being observed. The exception is access to urban parks, as it became more significant in increasing pedestrian activities during the pandemic. The models also highlight the negative impacts of the pandemic in economically disadvantaged areas. Our findings could help urban and transportation planners find effective interventions to promote active transportation and physical activity amid the global pandemic.

Analysis of Road Transport Response to COVID-19 Pandemic in Nigeria and its Policy Implications.

The COVID-19 pandemic presents a serious global health challenge to humanity in recent times. It has caused fundamental disruptions to the global transportation system, supply chains, and trade. The impact on the transport sector resulting from lockdowns has led to huge losses in revenue. At the moment there are limited studies of the road transport sector response to the COVID-19 pandemic. This paper fills this gap using Nigeria as a case study area. A mixed method involving both qualitative and quantitative research was employed. Principal Component Analysis and Multiple Criteria Analysis were used to analyze the data. The results suggest that road transport operators strongly (90.7%) believe that 51 adopted new technologies/innovations, processes, and procedures will keep them and passengers safe from the COVID-19 pandemic in Nigeria. A breakdown shows that observing the lockdown directive is perceived by road transport operators as the most effective response to the pandemic. The breakdown continues in descending order thus: COVID-19 safety protocols, environmental sanitation, and promotion of hygiene, information technology, facemask, and social distancing. Others are public enlightenment, palliative, inclusion, and mass media. This indicates that non-pharmaceutical measures are very effective in the fight against the pandemic. This finding leverages support for the application of non-pharmaceutical guidelines in containing the COVID-19 pandemic in Nigeria.

Longitudinal Analysis of COVID-19 Impacts on Mobility: An Early Snapshot of the Emerging Changes in Travel Behavior.

The COVID-19 pandemic has caused a huge disruption worldwide with direct and indirect effects on travel behavior. In response to extensive community spread and potential risk of infection, during the early stage of the pandemic many state and local governments implemented non-pharmaceutical interventions that restricted non-essential travel for residents. This study evaluates the impacts of the pandemic on mobility by analyzing micro panel data (N = 1,274) collected in the United States via online surveys in two periods, before and during the early phase of the pandemic. The panel makes it possible to observe initial trends in travel behavior change, adoption of online shopping, active travel, and use of shared mobility services. This analysis intends to document a high-level overview of the initial impacts to spur future research to dive deeper into these topics. With the analysis of the panel data, substantial shifts are found from physical commutes to teleworking, more adoption of e-shopping and home delivery services, more frequent trips by walking and biking for leisure purposes, and changes in ridehailing use with substantial variations across socioeconomic groups. The social and environmental implications of these findings are discussed and suggestions for effective policy and directions for future research are made in the conclusion.

COVID-19: Modeling Out-of-Home and In-Home Activity Participation during the Pandemic.

Understanding the interaction between in-home and out-of-home activity participation decisions is important, particularly at a time when opportunities for out-of-home activities such as shopping, entertainment, and so forth are limited because of the COVID-19 pandemic. The travel restrictions imposed as a result of the pandemic have had a massive impact on out-of-home activities and have changed in-home activities as well. This study investigates in-home and out-of-home activity participation during the COVID-19 pandemic. Data comes from the COVID-19 Survey for assessing Travel impact (COST), conducted from March to May in 2020. This study uses data for the Okanagan region of British Columbia, Canada to develop the following two models: a random parameter multinomial logit (RPMNL) model for out-of-home activity participation and a hazard-based random parameter duration (HRPD) model for in-home activity participation. The model results suggest that significant interactions exist between out-of-home and in-home activities. For example, a higher frequency of out-of-home work-related travel is more likely to result in a shorter duration of in-home work activities. Similarly, a longer duration of in-home leisure activities might yield a lower likelihood for recreational travel. Health care workers are more likely to engage in work-related travel and less likely to participate in personal and household maintenance activities at home. The model confirms heterogeneity among the individuals. For instance, a shorter duration of in-home online shopping yields a higher probability for participation in out-of-home shopping activity. This variable shows significant heterogeneity with a large standard deviation, which reveals that sizable variation exists for this variable.

Modelling the Long-Term Expected Impact of the Covid-19 Crisis on Commute and Telecommute

The Covid-19 pandemic crisis has forced many people to work from home rather than at their regular workplace. This paper explores the expected long-term changes caused by the pandemic crisis in Israel on work-related travel patterns, that is, the shifts in commuting and telecommuting post- versus pre-pandemic. Methodologically, the analysis is based on two consecutive surveys (of the same respondents) that were distributed during the pandemic (April and June 2020) to evaluate the trends in commuting and telecommuting from pre- to post-pandemic, addressing revealed preferences on work habits before and during the pandemic and stated intentions about work patterns after the pandemic. Four models were estimated based on these data: two multinomial logit models analyzing the trends in commuting and teleworking from before to after the pandemic, and two ordered logit models addressing the frequency of the intended teleworking and commuting trips in the post-pandemic era. The results showed that the Covid-19 crisis is expected to have some long-term implications, specifically, based on our sample, a 5%-6% expected reduction in commuting trips, alongside an expected increase in teleworking. While several socio-demographic, work-related, and personality traits were found to significantly influence commuting/telecommuting trends and frequency, it is interesting to note that working solely from home during the lockdown was found to have a prominent impact on increasing teleworking while decreasing commuting. Quantitative consistency evaluation of behavioral-shift statements across the consecutive surveys revealed moderate consistency, which is very reasonable given the instability associated with the Covid-19 crisis and the inherent changes in human perceptions.

Probit-Based Discrete-Continuous Choice Model to Explore the Relationship Between Car Ownership and Commuters' Non-Work Activity Durations in Xiaoshan District of Hangzhou, China

In China, a developing country, the car ownership level is much lower than that in developed countries, but transportation policies have been implemented to discourage car ownership and mitigate traffic congestion. However, car ownership (considered as car availability in this paper, meaning that an individual has access to a household private car) may influence travelers' well-being. To highlight the interrelation between car ownership and travelers' well-being, this paper develops a probit-based discrete-continuous model to analyze the relationship between car ownership and the duration of commuters' three major non-work outdoor activities (Act1: shopping and dining;Act2: leisure and entertainment;and Act3: visiting relatives or friends) in Xiaoshan District, Hangzhou, China. Empirical results indicate strong effects of individual and household socio-demographics, built environment attributes, and work-related characteristics on the car ownership decision and the duration of three non-work activities. The analysis shows positive correlations in unobserved factors between the car ownership decision and the duration of Acts1-3, indicating a mutually promotive relationship. Similarly, negative correlations among the duration of Acts1-3 show that non-work activities' duration is mutually substitutive. These findings will help to better understand commuters' car ownership decisions and non-work outdoor activity behavior restricted by fixed work schedules in developing countries, which can, in turn, better evaluate the impact of transportation policies (such as car ownership restriction) on travel demand as well as well-being, and provide decision support for the formulation of transportation policies.

User Segmentation Based on Travel Regularity in E-Scooter Sharing Service

The dockless e-scooter sharing service is rapidly spreading, replacing existing transportation, and improving last-mile accessibility. User segmentation with travel regularity and segment-level behavior analysis, which are already conducted in public transit, also benefits e-scooter sharing service to enhance service quality and increase usage. In this work, we group e-scooter users according to their travel regularity and identify each group's usage characteristics. Through the dockless e-scooter usage data, as operated in six cities in South Korea, travel regularity measured by users' repetitive departure time and destination is discovered and spatiotemporal usage patterns are identified. We divide e-scooter users into three groups by type of travel regularity: irregular user, spatially regular user, and regular user. Regular users more frequently use e-scooters, travel shorter distances, and walk longer distances to find an e-scooter than other groups. It is also revealed that the use in morning peak hours only occurs in the regular user group. By decomposing the temporal patterns of spatially regular and regular users, we discover that spatially regular users are composed of daytime, evening peak, and nighttime users. In contrast, regular users are composed of morning peak, evening peak, and lockdown (restriction in response to COVID-19 pandemic) peak users. This research suggests user segmentation based on travel regularity in e-scooter sharing services, enabling multiple strategies to be drawn to retain users with high regularity and convert users with low regularity to regular users.

Co-Evolutionary Location-Routing Model of Medical Isolation Areas for Major Public Health Emergencies Considering the Uncertainty of Future Traffic Environment

With the continuous development of the COVID-19 pandemic, the selection of locations for medical isolation areas has not always been optimal for the timely transportation of infected people, or those suspected of being infected. This has resulted in failure to control the rate of spread of infection cases in time. To address this problem, this paper proposes a co-evolutionary location-routing optimization (CELRO) model of medical isolation areas for use in major public health emergencies to develop a rapid location-routing scheme for epidemic isolation, including the selection of locations of medical isolation facilities per area and the optimal route per vehicle to each infected person. Specifically, this paper solves the following two sub-problems: (i) calculate the shortest transportation times and corresponding routes from any medical isolation area to any person infected or suspected of being infected, and (ii) calculate the location scheme for distribution of isolation areas. Different from previous studies, the vehicle operating characteristics and the interference of uncertainty of the traffic environment are considered in the proposed model. To find an appropriate scheme for location of medical isolation areas with the shortest travel times, a co-evolutionary clustering algorithm (CECA), which is a combination of some separated evolutionary programming operations, is proposed to solve the model. Various network sizes and uncertainty combinations are used to design some comparative tests, which aim to verify the effectiveness of the proposed model. In the experiment section, CELRO reduced travel time by at least 14% compared with other methods. This finding can provide an effective theoretical basis for optimizing the spatial layout of medical isolation areas or the location planning of new medical facilities. © National Academy of Sciences.

Strangers On This Road We Are On: A Literature Review of Pooling in On-Demand Mobility Services

Ridepooling service options introduced by transportation network companies (TNCs) and microtransit companies provide opportunities to increase shared-ride trips in vehicles, thereby improving congestion and environmental factors. This paper reviews the existing literature available on ridepooling and related services, specifically focusing on pooling options available from on-demand transportation companies. The paper summarizes the existing knowledge on the use of pooled-ride services, factors in travel mode service options for customers, available policy and planning strategies to incentivize sharing vehicles, and effects of the COVID-19 pandemic on shared-ride travel. Overall, research shows that ridepooling options are more likely to be considered by public transit users who have lower household incomes, while ridesourcing users of upperclass backgrounds are less likely to consider moving to a shared-ride service. Travel time and trip cost are the most important factors for travelers determining whether to use a ridesplitting or microtransit service rather than a ride-alone ridesourced trip. Existing policy and planning tools targeting pooled travel or TNCs can be expanded on and specified for on-demand ridepooling services, such as offering better incentives to use shared vehicles and increased access to curb areas or travel lanes, but the most effective strategies will include increasing the user costs for parking or riding alone. © National Academy of Sciences.

Multiperspective Analysis of Pandemic Impacts on U.S. Import Trade: What Happened, and Why?

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.

Metro Network Operational Solutions for Connectivity Control based on Percolation Theory

Recent studies have applied the percolation theory to analyze the connectivity of networks in the transportation field. However, research was conducted in a manner that completely removed the function of nodes or links. There was a limit in that applying public transportation was difficult to guarantee the right to move the captive rider. In this study, penalties were imposed on public transportation nodes in the form of wait times to remove the function of node partially. Accordingly, the travel time of a network was calculated by optimal strategy assignment to reflect passenger behavior. When nodes were randomly penalized without transfer distinctions, there was a critical point of travel-time increase between cases with penalties of 50 and 60 nodes, respectively, and percolation was observed indirectly. A large and global effect of increased travel time was observed when the penalties were issued only to transfer stations. The application of a trip frequency weight increases the effect of penalties on medium- or short-timed trips. The results of this study can be used to establish quarantine policies for controlling public transportation networks. Furthermore, it is the first attempt at observing percolation by partially limiting its function in the form of node penalties in a public transportation network.