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This paper discusses the dual role of the transport sector in the Covid-19 pandemic: spreading the virus around the world and being most negatively impacted by the pandemic. This paper describes and analyzes the following: (a) actions taken by the governments and international community in order to control the spreading and to alleviate negative economic impacts including massive fiscal and monetary stimulus funding; (b) detailed discussions on the impacts of the pandemic on air transport, rail and bus transport, and urban transit, and major countries' responses to reduce the negative effects; (c) discussions on the positive effects of the pandemic on the environment and climate change by suggesting policy measures in order to make it sustainable over the long term. Finally, the paper addresses social acceptance issue of the behavioral changes necessary in the post-pandemic world, in particular reflecting historical experience of the Spanish flu case. We end the paper with some observations and discussion of the normative issues for a sustainable development of the transport sector. • The impacts of three Covid-19 waves on the world economies have enforced many countries to start large stimulus packages. • The negative impacts on transportation activities are severe for aviation, rail and public transit. • Car travel is on the way of recovering while biking rides are increasing and parcel services are booming. • It will be difficult for mass transport modes to return to previous Covid growth paths because of reduced confidence of passengers. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID-19) crisis has influenced economies and societies across the globe and will thoroughly reshape our world as it continues to unfold. The pandemic is likely to trigger permanent long-term impacts on the transport sector in the post-COVID world. While a post-COVID "new normal" will be likely to incur negative consequences, it may provide an opportunity to move toward a more sustainable transport sector. This paper is aimed at developing an urban economic model with an energy focus to depict the dynamics of travel demand, energy consumption, and emissions in the post-COVID world. A set of scenarios was created according to model assumptions regarding lifestyle changes and policy interventions accompanied by the expected post-COVID new normal, to explore long-term pathways toward a deep decarbonization of the transport sector. Scenario simulations demonstrated that working from home, online shopping, and a bike-friendly infrastructure will contribute to a reduction in energy consumption and CO 2 emissions, whereas a significant shift from bus to car transport and the decreasing use of car-sharing services will adversely affect CO 2 emission reductions. The arrival of the post-COVID world may contribute to an 11% reduction in CO 2 emissions by 2060, while the maximum reduction potential could be as high as 44%. Supporting policies and strategies for encouraging remote work and online shopping as well as for promoting safe public transport, active transport, and carpooling services are needed to strongly decarbonize the transport sector in the post-COVID world. Moreover, population distribution and urban structure may also be influenced by the arrival of the post-COVID new normal, which warrant further attention for urban planning. • An urban economic model that accommodates detailed transport and energy technology representations is presented. • Several scenarios are analyzed to depict decarbonization pathways for the transport sector in the post-COVID world. • The arrival of the post-COVID new normal may contribute to reducing CO 2 emissions by a maximum of 44% by 2060. • The impacts of the oncoming new normal on urban structure deserve more attention. [ABSTRACT FROM AUTHOR]

To design effective policies against COVID-19, there is a need for more evidence-based research. However, associations between actual policies and temporal behavior changes have remained underexplored. To fill this important research gap, a nationwide retrospective life-oriented panel survey on individuals' behavior changes from April to September 2020 was implemented in Japan. Reliability of information sources, risk perceptions, and attitudes toward policymaking were also investigated. Valid data were collected from 2643 respondents residing in different parts of the country. Risks were reported about general infections and public transport use. Attitudes toward policymaking were mainly about policymaking capacity and PASS-LASTING based policy measures. A dynamic structural equation model (DSEM) was developed to quantify dynamic associations between individuals' behavior changes over time and subjective assessments (i.e., attitudes) of policymaking. Survey results revealed that behavior changes are mostly characterized by avoidance behaviors. Modeling estimation results showed a statistically-significant sequential cause-effect relationship between accumulated behavior changes in the past, subjective factors, and the most recent behavior changes. The most recent behavior changes are mostly affected by accumulated behavior changes in the past. Effects of subjective assessments of policymaking on the most recent behavior changes are significant but moderate. Among attitudes toward policymaking, attitudes toward policymaking capacity are more influential than willingness to follow PASS-LASTING based policy measures. High risks of using public transport are found to significantly influence the most recent behavior changes, together with other risk perception factors. Insights into effective COVID-19 policymaking are summarized. [Display omitted] • A nationwide life-oriented panel survey was implemented online in Japan in 2020. • Various behavior changes are mostly characterized by avoidance behaviors in Japan. • Impacts of risk perception during the use of public transport are revealed. • Impacts of policymaking capability on activity-travel behaviors are confirmed. • Impacts of PASS-LASTING policies on activity-travel behaviors are confirmed. [ABSTRACT FROM AUTHOR]

This study attempts to provide scientifically-sound evidence for designing more effective COVID-19 policies in the transport and public health sectors by comparing 418 policy measures (244 are transport measures) taken in different months of 2020 in Australia, Canada, Japan, New Zealand, the UK, and the US. The effectiveness of each policy is measured using nine indicators of infections and mobilities corresponding to three periods (i.e., one week, two weeks, and one month) before and after policy implementation. All policy measures are categorized based on the PASS approach (P: prepare-protect-provide; A: avoid-adjust; S: shift-share; S: substitute-stop). First, policy effectiveness is compared between policies, between countries, and over time. Second, a dynamic Bayesian multilevel generalized structural equation model is developed to represent dynamic cause-effect relationships between policymaking, its influencing factors and its consequences, within a unified research framework. Third, major policy measures in the six countries are compared. Finally, findings for policymakers are summarized and extensively discussed. [Display omitted] • COVID-19 transport-related policymaking in six developed countries are compared. • Effectiveness of 418 PASS-based policy measures are examined by using 27 indicators. • Policy effectiveness is compared between policies, between countries and over time. • A dynamic Bayesian multilevel generalized structural equation model is developed. • Dynamic relationships between policymaking, factors and consequences are revealed. [ABSTRACT FROM AUTHOR]

Impacts of coronavirus disease 2019 (COVID-19) on the transport sector and the corresponding policy measures are becoming widely investigated. Considering the various uncertainties and unknowns about this virus and its impacts (especially long-term impacts), it is critical to understand opinions and suggestions from experts within the transport sector and related planning fields. To date, however, there is no study that fills this gap in a comprehensive way. This paper is an executive summary of the findings of the WCTRS COVID-19 Taskforce expert survey conducted worldwide between the end of April and late May 2020, obtaining 284 valid answers. The experts include those in the field of transport and other relevant disciplines, keeping good balances between geographic regions, types of workplaces, and working durations. Based on extensive analyses of the survey results, this paper first reveals the realities of lockdowns, restrictions of out-of-home activities and other physical distancing requirements, as well as modal shifts. Experts' agreements and disagreements to the structural questions about changes in lifestyles and society are then discussed. Analysis results revealed that our human society was not well prepared for the current pandemic, reaffirming the importance of risk communication. Geographical differences of modal shifts are further identified, especially related to active transport and car dependence. Improved sustainability and resilience are expected in the future but should be supported by effective behavioral intervention measures. Finally, policy implications of the findings are discussed, together with important future research issues. • Findings are derived from a worldwide expert survey implemented in April–May 2020. • Guidelines and contingency plans were reported by only about 30% of experts. • Remarkable modal shifts away from public transport usage were reported. • Developing countries were equally active in taking measures as developed countries. • Various long-term changes in lifestyles and society were revealed. [ABSTRACT FROM AUTHOR]

This study evaluated the characteristics of individuals with voluntary behavioural changes (cancellation and postponement of bookings) during the early stages of the coronavirus disease 2019 (COVID-19) outbreak in Japan. In addition, the temporal trends of these changes were captured. A cross-sectional analysis and a time series analysis were conducted. A nation-wide retrospective panel survey was conducted at the end of March 2020 (n = 1052). Odds ratios for cancellations/postponements with respect to individual characteristics were calculated in the analysis. To determine the temporal trend, the incidence ratios were compared throughout the time series analysis for four time periods: period 1, before the announcement of the Public Health Emergency of International Concern (PHEIC) from the World Health Organisation (WHO) (January 1–31); period 2, after the announcement of PHEIC (February 1–26); period 3, after the announcement of school closures by the Japanese government (February 27 – March 11); and period 4, after the announcement of the pandemic by the WHO (March 12–31). In total, 72% of respondents cancelled or postponed their bookings at least once, and about half of the changes occurred in period 3. Elderly individuals' changes in gatherings were, on average, 5.9 times (95% confidence interval [CI] 1.9 – 17.9) higher than those of young individuals. The incidence rate of change in gatherings during period 3 was 7.11 times (95% CI: 5.16 – 9.81) higher than in period 2 and 3.15 times (95% CI: 2.25 – 4.43) higher than in period 4. Significant interaction terms were observed in age and residential city size, but not sex, of the respondents. A significant proportion of the Japanese population voluntarily changed their behaviour during the early stages of the COVID-19 outbreak, and the government's announcement of school closures was a key trigger during this time. [Display omitted] • Temporal behavioural changes for the coronavirus disease 2019 outbreak in Japan were evaluated. • A high percentage of the population had voluntarily initiated behavioural changes. • Differences in behavioural changes were observed across individual characteristics. • The government announcement for school closures was a key trigger for behavioural change across all ages. • Significant interaction effects between time periods and characteristics were observed. [ABSTRACT FROM AUTHOR]

The novel coronavirus 2019 (COVID-19) outbreak has had wide-reaching and unprecedented impacts on the transport sector worldwide. At present, there is no globally agreed timeframe for when this pandemic will end. The current and near-future potential impacts must be addressed in a relatively comprehensive and seamless way. The present study proposed a PASS (P: Prepare–Protect–Provide; A: Avoid–Adjust; S: Shift–Share; S: Substitute–Stop) approach for policymaking that accounts for COVID-19 and future public health threats. The PASS approach was illustrated conceptually, and then policy measures were recommended by referring to the past and ongoing best practices. Policymaking challenges and research issues were discussed. Image 1 • •Impacts of COVID-19 on the transport sector are targeted. • •A PASS approach was proposed for policymaking for addressing pandemics. • •Policy measures are recommended based on the PASS approach. [ABSTRACT FROM AUTHOR]

This study investigates urban recovery from the COVID-19 pandemic by focusing on three main types of working, commercial, and night-life activities and associating them with land use and inherent socio-economic patterns as well as points of interests (POIs). Massive multi-source and multi-scale data include mobile phone signaling data (500 m × 500 m), aerial images (0.49 m × 0.49 m), night light satellite data (500 m × 500 m), land use data (street-block), and POIs data. Methods of convolutional neural network, guided gradient-weighted class activation mapping, bivariate local indicator of spatial association, Elbow and K-means are jointly applied. It is found that the recovery in central areas was slower than in suburbs, especially in terms of working and night-life activities, showing a donut-shaped spatial pattern. Residential areas with mixed land uses seem more resilient to the pandemic shock. More than 60% of open spaces are highly associated with recovery in areas with high-level pre-pandemic social-economic activities. POIs of sports and recreation are crucial to the recovery in all areas, while POIs of transportation and science/culture are also important to the recovery in many areas. Policy implications are discussed from perspectives of open spaces, public facilities, neighborhood units, spatial structures, and anchoring roles of POIs. [ABSTRACT FROM AUTHOR]

The built environment can contribute to the spread of the novel coronavirus disease (COVID-19) by facilitating human mobility and social contacts between infected and uninfected individuals. However, mobility data capturing detailed interpersonal transmission at a large scale are not available. In this study, we aimed to objectively assess the influence of key built environment factors, which create spaces for activities—"inferred activity" rather than "actually observed activity"—on the spread of COVID-19 across townships in China at its initial stage through a random forest approach. Taking data for 2994 township-level administrative units, the spread is measured by two indicators: the ratio of cumulative infection cases (RCIC), and the coefficient of variation of infection cases (CVIC) that reflects the policy effect in the initial stage of the spread. Accordingly, we selected 19 explanatory variables covering built environment factors (urban facilities, land use, and transportation infrastructure), the level of nighttime activities, and the inter-city population flow (from Hubei Province). We investigated the spatial agglomerations based on an analysis of bivariate local indicators of spatial association between RCIC and CVIC. We found spatial agglomeration (or positive spatial autocorrelations) of RCIC and CVIC in about 20% of all townships under study. The density of convenience shops, supermarkets and shopping malls (DoCSS), and the inter-city population flow (from Hubei Province) are the two most important variables to explain RCIC, while the population flow is the most important factor in measuring policy effects (CVIC). When the DoCSS gets to 21/km2, the density of comprehensive hospitals to 0.7/km2, the density of road intersections to 72/km2, and the density of gyms and sports centers to 2/km2, their impacts on RCIC reach their maximum and remain constant with further increases in the density values. Stricter policy measures should be taken at townships with a density of colleges and universities higher than 0.5/km2 or a density of comprehensive hospitals higher than 0.25/km2 in order to effectively control the spread of COVID-19. [ABSTRACT FROM AUTHOR]