Changes in car and bus usage amid the COVID-19 pandemic: Relationship with land use and land price

A study on the impact of housing prices on residents' travel frequency and transportation resilience in 35 Chinese cities

As urbanization proceeds, urban transportation resilience suffers greater challenges. Covering panel data for 35 Chinese cities from 2006 to 2019, after controlling for other variables that may affect urban residents' transportation volumes, this paper finds that rising houseing prices have a significant positive effect on urban residents' traffic frequency. As frequent traffic trips increase the probability of urban residents experiencing disruptions, rising housing purchase prices would have a negative impact on Chinese urban residents' transportation resilience. The regional comparison analysis finds that the regression coefficients of coastal and eastern cities with more expensive housing price are significantly higher than those of non-coastal and western cities with lower housing price, further validating the findings of this paper.

Causal effects of mobility intervention policies on intracity flows during the COVID-19 pandemic: The moderating role of zonal locations in the transportation networks

Many studies have investigated the impact of mobility restriction policies on the change of intercity flows during the outbreak of COVID-19, whereas only a few have highlighted intracity flows. By using the mobile phone trajectory data of approximately three months, we develop an interrupted time series quasi-experimental design to estimate the abrupt and gradual effects of mobility intervention policies during the pandemic on intracity flows of 491 neighborhoods in Shenzhen, China, with a focus on the role of urban transport networks. The results show that the highest level of public health emergency response caused an abrupt decline by 4567 trips and a gradually increasing effect by 34 trips per day. The effectiveness of the second return-to-work order (RtW2) was found to be clearly larger than that of the first return-to-work order (RtW1) as a mobility restoration strategy. The causal effects of mobility intervention policies are heterogenous across zonal locations in varying urban transport networks. The declining effect of health emergency response and rebounding effect of RtW2 are considerably large in better-connected neighborhoods with metro transit, as well as in those close to the airport. These findings provide new insights into the identification of pandemic-vulnerable hotspots in the transport network inside the city, as well as of crucial neighborhoods with increased adaptability to mobility interventions during the onset and decline of COVID-19.

Evaluation of indoor environmental quality, personal cumulative exposure dose, and aerosol transmission risk levels inside urban buses in Dalian, China

The transmission of pollutants in buses has an important impact on personal exposure to airborne particles and spread of the COVID-19 epidemic in enclosed spaces. We conducted the following real-time field measurements inside buses: CO2, airborne particle concentration, temperature, and relative humidity data during peak and off-peak hours in spring and autumn. Correlation analysis was adopted to evaluate the dominant factors influencing CO2 and particle mass concentrations in the vehicle. The cumulative personal exposure dose to particulate matter and reproduction number were calculated for passengers on a one-way trip. The results showed the in-cabin CO2 concentrations, with 22.11% and 21.27% of the total time exceeding 1000 ppm in spring and autumn respectively. In-cabin PM2.5 mass concentration exceeded 35 μm/m3 by 57.35% and 86.42% in spring and autumn, respectively. CO2 concentration and the cumulative number of passengers were approximately linearly correlated in both seasons, with R value up to 0.896. The cumulative number of passengers had the most impact on PM2.5 mass concentration among tested parameters. The cumulative personal exposure dose to PM2.5 during a one-way trip in autumn was up to 43.13 μg. The average reproductive number throughout the one-way trip was 0.26; it was 0.57 under the assumed extreme environment. The results of this study provide an important basic theoretical guidance for the optimization of ventilation system design and operation strategies aimed at reducing multi-pollutant integrated health exposure and airborne particle infection (such as SARS-CoV-2) risks.

Understanding individual-level travel behavior changes due to COVID-19: Trip frequency, trip regularity, and trip distance

Understanding different mechanisms in trip changes depending on transportation modes due to COVID-19 pandemic is the key to providing practical insights for healthy communities. This study aimed to investigate the impact of the COVID-19 pandemic on individual-level travel behavior in Daejeon Metropolitan City, South Korea. Using smart card and private vehicle records, we explored different travel behaviors exhibited while using buses and private vehicles. An individual's travel behavior was represented in trip frequency, trip regularity, and trip distance and was compared weekly for about three months, including the initial period of pandemic. A significant decrease in trip frequency during non-peak hours on weekdays and during weekends indicates that people reduced non-mandatory trips more than commuter trips. This was also verified in that, as the number of infection cases increased, trip regularity with 24-hour intervals intensified. People maintained the size of their activity boundaries but reduced their daily trip distances. The interesting point is that private vehicle usage increased for shorter trip distances while bus usage dropped regardless of the ranges of trip distances under the pandemic. The findings provide evidence of possible inequality issues in transportation during the pandemic and can help make precautionary policies for future pandemics.

Factors affecting public transport performance due to the COVID-19 outbreak: A worldwide analysis

In this paper we investigate the public transport trip frequency variations, as well as the reasons that led to the shift away from public transport means, due to the COVID-19 pandemic. We studied relevant data from the Moovit platform, and we compared operational and trip frequency characteristics of public transport systems before and after the outbreak of the pandemic in 87 cities worldwide. On average, waiting times at public transport stops/stations increased while trip distances decreased, apparently due to the mobility restriction and social distancing measures implemented in 2020. Most of the Moovit users who said that they abandoned public transport in 2020 were found in Italy and Greece. We developed linear regression analysis models to investigate (among the 35 variables examined in the study) the relationship between public transport abandonment rates and socioeconomic factors, quality of service characteristics, and indicators of pandemic's spread. Empirical findings show that public transport dropout rates are positively correlated with the COVID-19 death toll figures, the cleanliness of public transport vehicles and facilities, as well as with the income inequality (GINI) index of the population, and thus reconfirm previous research findings. In addition, the waiting time at stops/stations and the number of transfers required for commute trips appeared to be the most critical public transport trip segments, which significantly determine the discontinuation of public transport use under pandemic circumstances. Our research findings indicate specific aspects of public transport services, which require tailored adjustments in order to recover ridership in the post-pandemic period.

Understanding the perceived behavior of public utility bus passengers during the era of COVID-19 pandemic in the Philippines: Application of social exchange theory and theory of planned behavior

Over the years, passengers in the Philippines have relied on public utility bus (PUB) to reach a destination. However, PUB has been greatly affected by the adverse effects of the coronavirus disease (COVID-19) outbreak. Therefore, the study analyzed passengers' behavior using PUB during the COVID-19 pandemic by integrating social exchange theory (SET) and the theory of planned behavior (TPB). A total of 505 PUB users completed the online questionnaire containing sixty-seven (67) measures. Through confirmatory factor analysis - structural equation modeling (CFA-SEM), all the twelve (12) hypotheses were found significant. Accessibility, safety, economic benefit, and crisis management had a significant and positive influence on passengers' trust. Moreover, passengers' trust produced a significant positive influence on attitude, subjective norm, and perceived behavioral control. Additionally, attitude, subjective norm, and perceived behavioral control generated a significant positive influence on the intention to use PUB. Furthermore, perceived behavioral control had a significant positive influence on perceived passenger behavior. Lastly, the intention to use PUB developed a substantial and positive influence on perceived passenger behavior. PUB companies and policymakers can utilize these findings to develop additional PUB reforms for the benefit of passengers, drivers, operators, companies, and the government during the COVID-19 pandemic.

Unraveling the dynamic impacts of COVID-19 on metro ridership: An empirical analysis of Beijing and Shanghai, China

The outbreak of coronavirus disease 2019 (COVID-19) has had severely disruptive impacts on transportation, particularly public transit. To understand metro ridership changes due to the COVID-19 pandemic, this study conducts an in-depth analysis of two Chinese megacities from January 1, 2020, to August 31, 2021. Generalized linear models are used to explore the impact of the COVID-19 pandemic on metro ridership. The dependent variable is the relative change in metro ridership, and the independent variables include COVID-19, socio-economic, and weather variables. The results suggested the following: (1) The COVID-19 pandemic has a significantly negative effect on the relative change in metro ridership, and the number of cumulative confirmed COVID-19 cases within 14 days performs better in regression models, which reflects the existence of the time lag effect of the COVID-19 pandemic. (2) Emergency responses are negatively associated with metro system usage according to severity and duration. (3) The marginal effects of the COVID-19 variables and emergency responses are larger on weekdays than on weekends. (4) The number of imported confirmed COVID-19 cases only significantly affects metro ridership in the weekend and new-normal-phase models for Beijing. In addition, the daily gross domestic product and weather variables are significantly associated with metro ridership. These findings can aid in understanding the usage of metro systems in the outbreak and new-normal phases and provide transit operators with guidance to adjust services.

Multi-scale causality analysis between COVID-19 cases and mobility level using ensemble empirical mode decomposition and causal decomposition

The global spread of the coronavirus disease 2019 (COVID-19) pandemic has affected the world in many ways. Due to the communicable nature of the disease, it is difficult to investigate the causal reason for the epidemic's spread sufficiently. This study comprehensively investigates the causal relationship between the spread of COVID-19 and mobility level on a multi time-scale and its influencing factors, by using ensemble empirical mode decomposition (EEMD) and the causal decomposition approach. Linear regression analysis investigates the significance and importance of the influential factors on the intrastate and interstate causal strength. The results of an EEMD analysis indicate that the mid-term and long-term domain portrays the macroscopic component of the states' mobility level and COVID-19 cases, which represents overall intrinsic characteristics. In particular, the mobility level is highly associated with the long-term variations of COVID-19 cases rather than short-term variations. Intrastate causality analysis identifies the significant effects of median age and political orientation on the causal strength at a specific time-scale, and some of them cannot be identified from the existing method. Interstate causality results show a negative association with the interstate distance and the positive one with the airline traffic in the long-term domain. Clustering analysis confirms that the states with the higher the gross domestic product and the more politically democratic tend to more adhere to social distancing. The findings of this study can provide practical implications to the policymakers that whether the social distancing policies are effectively working or not should be monitored by long-term trends of COVID-19 cases rather than short-term.

Data analytics during pandemics: a transportation and location planning perspective

The recent COVID-19 pandemic once again showed the value of harnessing reliable and timely data in fighting the disease. Obtained from multiple sources via different collection streams, an immense amount of data is processed to understand and predict the future state of the disease. Apart from predicting the spatio-temporal dynamics, it is used to foresee the changes in human mobility patterns and travel behaviors and understand the mobility and spread speed relationship. During this period, data-driven analytic approaches and Operations Research tools are widely used by scholars to prescribe emerging transportation and location planning problems to guide policy-makers in making effective decisions. In this study, we provide a review of studies which tackle transportation and location problems during the COVID-19 pandemic with a focus on data analytics. We discuss the major data collecting streams utilized during the pandemic era, highlight the importance of rapid and reliable data sharing, and give an overview of the challenges and limitations on the use of data.

Analysis of the Relationship between the Characteristics of the Areas of Influence of Bus Stops and the Decrease in Ridership during COVID-19 Lockdowns

This study aimed to investigate the relationship between the characteristics of the areas of influence of bus stops and the decrease in ridership during COVID-19 lockdowns and subsequent initial reopening processes. A novel GIS methodology was developed to determine these characteristics from a large amount of data with high spatial detail and accurately assign them to individual bus stops. After processing the data, several multiple linear regression models were developed to determine the variables related to different activities and changes in mobility during lockdown that may explain the variation in demand owing to the COVID-19 pandemic. The characteristics related to population and land use were also studied. The proposed methodology can be used to improve transit planning during exceptional situations, by strengthening public transport in areas with a predictably higher transit demand, instead of uniformly decreasing the availability of public transport services, promoting sustainable mobility. The efficiency of the proposed methodology was shown by performing a case study that analysed the variation in bus demand in A Coruña, Spain. The areas with the highest sustained demand were those with low inhabitant incomes, a high population density, and significant proportions of land use dedicated to hospitals, offices, or supermarkets.