Effects of COVID-19 on Docked Bikeshare Trips
Theresa Firestine, OST-R/Bureau of Transportation StatisticsShow Abstract
This paper explores the effects of COVID-19 on docked bikeshare trips in: (1) in January and February 2020—the months prior to the declaration of a national emergency on March 1, 2020, (2) the days from the declaration of the national emergency up to the stay-at-home order date in states issuing an order, (3) during the state issued stay-at-home period, and (4) from the beginning of phased reopening (in the states that issued stay-at-home or shelter-in-place orders) through the end of June. We examine docked bikeshare trips using the dates specific to the state or territory in which the system can be found. We additionally compare these four phases to local public transportation trips and overall travel behavior where available as a first step in exploring the effects of COVID-19 on mode choice and traditional travel patterns. For the 10 systems examined, docked bikeshare increased from January to February 2020 from 2019 levels (the period prior to COVID-19 responses), declined during statewide stay-at-home and shelter-in-place periods, and began to increase during phased reopening. Docked bikeshare trips have not yet returned to 2019 levels, but data through June 2019 show a faster increase compared to the same period in 2019 and a faster increase during phased reopening than local public transportation trips. This may be an early indicator of a modal shift from public transportation to docked bikeshare for some trips and riders. Further research is needed to fully understand the effects of COVID-19 on mode choice and travel behavior.
Impact of COVID-19 lockdown on the Behavior Change of Cyclists in Lisbon, using multinomial logit regression analysis
Miguel Costa (email@example.com), Instituto Superior Técnico Universidade de LisboaShow Abstract
Rosa Félix, Universidade de Lisboa
Mannuel Marques, Universidade de Lisboa
Filipe Moura, Universidade de Lisboa
COVID-19 outbreak hit the majority of countries and cities in the world, causing dramatic impacts in the way people live during the lockdown periods. Socioeconomic activities changed, in a compulsory way, while long-lasting and structural changes might have remained after the end of lockdowns. As activities changed, the corresponding mobility habits and patterns also changed, during and after the lockdown. Many cities have implemented fast and temporary solutions to avoid a massive transfer back to private vehicles, due to the fear of increased exposure to and potential contamination by the COVID-19 virus in public transportation. The main objective of this paper is to present and analyze the impact of COVID-19 lockdown on the behavior change of cyclists in the case of Lisbon, Portugal, where at the time of the survey, neither temporary solutions were implemented on the cycling network, nor were pro-bike campaigns in progress . Our analyses are based on a sample of 493 valid on-site intercept surveys, collected in 5 locations in existing cycling lanes. The 1-minute questionnaire aimed mainly to collect effectively revealed preferences on whether cycling frequency increased (or began), remained stable or decreased, before and after the COVID-19 lockdown. We calibrated successfully a multinomial logit model to analyze the probability of respondents changing their cycling habits, based on the change of cycling frequency, sociodemographic, and mobility attributes. Overall, we notice an increase in cycling frequency, especially for those that never or rarely cycled before the pandemic.
Insights into the Impact of COVID-19 on Bicycle Usage in Colorado Counties
Abdullah Kurkcu, UlteigShow Abstract
Ilgin Gokasar, Bogazici Universitesi
Onur Kalan, New York University
Alperen Timurogullari, Bogazici Universitesi
Burak Altin, Bogazici Universitesi
Coronavirus, which emerged in China towards the end of 2019 and subsequently influenced the whole world, has changed the daily lives of people to a great extent. In many parts of the world, in both cities and rural areas, people have been forced to stay home weeks. They have only been allowed to leave home for fundamental needs such as food and health needs, and most started to work from home. In this period, very few people, including essential workers, had to leave their homes. Avoiding social contact is proven to be the best method to reduce the spread of the novel Coronavirus. Because of the COVID-19 pandemic, people are adapting their behavior to this new reality, and it may change the type of public events people perform and how people go to these activities. Consumer behaviors have been altered during the pandemic. While people try to avoid gatherings, they also stayed away from mass transport modes and turned to private modes of transportation more -- private cars, private taxis and bike-sharing systems; even walking became more popular. In this study, we attempt to analyze how the use of bicycling has changed -- pre- and post-pandemic -- using open data sources and investigating how socio-economics characteristics affect this change. The results showed that average income, average education level, and total population are the most crucial variables for the Pandemic to Transition period and the Transition to the Normalization period.
Montreal’s Response to COVID-19: An Equity Analysis of New Active Transport Infrastructure
Kevin Manaugh (firstname.lastname@example.org), McGill UniversityShow Abstract
Linnea Soli, McGill University
Samuel Kohn, McGill University
Robin Baselaev-Binder, McGill University
Ty Tuff, McGill University
David Wachsmuth, McGill University
Starting in mid-March 2020, as a result of the COVID-19 pandemic, residents of most cities in Canada were instructed to ‘shelter in place’ and make only necessary trips and to practice physical distancing by keeping a two meter buffer from other people while in public. In order to accommodate the resulting increased demand on local street networks, many cities made rapid changes in the provision of active transport infrastructure such as closing of streets to cars, installing bike lanes, and extending sidewalks. Reductions in public transport services, along with fears of being in enclosed spaces with strangers, also increased the amount that residents were walking in their neighborhood, thereby putting additional demands on the pedestrian realm. Using the case of Montreal, this study examines the provision of walkable urban space before the lockdown and analyses the impact of the City’s response in terms of location of interventions. We find clear discrepancies in the provision of walkable space across income and racial categories: visible minority, Black, and low-income households are disproportionately located in areas with a low capacity to allow residents to make daily trips while practicing physical distancing. While the City’s plans to increase walkable urban space made some improvements to these discrepancies, there is room for improvement. In addition, the city has greatly reduced its originally proposed plan and, in some cases, street closures have been cancelled due to backlash from local merchants who feared the loss of car parking, further reducing the impacts that these interventions could have.
Simulation-based Infection Risk Study on Bike Sharing Systems Amid COVID-19 Pandemic
Yang Liu, Old Dominion UniversityShow Abstract
Qingyu Ma, Old Dominion University
Hong Yang (email@example.com), Old Dominion University
Suzana Duran Bernardes, New York University
Jingqin Gao, New York University
Kaan Ozbay, New York University
The fast-evolving COVID-19 pandemic has dramatically reshaped urban mobility patterns. In particular, micromobility systems including bike sharing systems become a critical option for those who attempted to avoid public transportation systems for their essential travel need. Clearly, the use of shared bikes provides much flexibility to personalized trips and reduces the risk of being exposed to infectious environments with crowd. Yet, the use of shared bikes is also not necessarily risk free due to touching bikes with the potential coronavirus living on surfaces (e.g., handlebars) for days. Thus, it is quite important to understand how such shared mobility systems may become a spreading channel for the virus. This paper aims to investigate the potential spread of infection risk among bike sharing systems. A simplified simulation-based approach was developed to achieve the goal. The impacts of different levels of initial infection carriers and the non-uniformed spatial distributions of high-risk riders were tested with customized simulation scenarios. Three-month data collected from the Citi Bike system in New York City amid COVID-19 pandemic were used as the testbed. The simulation results show that the use of the shared bikes can lead to additional infections among many riders. The estimated infections will be affected by the initial proportion of infection carriers as well as their spatial distributions. It is concluded that the use of shared bikes contaminated with the COVID-19 virus can facilitate the transmission of it to riders widely distributed in the entire system.
The Impact of Bike Share Services in Disease Transmission: Numerical Exploration of the COVID 19 Spread in Washington DC Through Modeling and Sensitivity Analysis
Mohaiminul Haque, George Washington UniversityShow Abstract
Samer Hamdar, George Washington University
Covid-19 is a respiratory disease cause by the virus SARS-CoV-2. This corovirus first emerged in the city of Wuhan, China, in December of 2019. Due to its ability to stay alive/active in air and on different surfaces for a given period of time, this virus was widely spread with a high contagion rate impacting considerable portions of different populations with different socio-economic characteristics. The World Health Organization declared Covid-19 as a pandemic in March of 2020. Because of the nature of the Covid-19 transmission, public transport is considered as a driver for human to human transmission. In this study, we focus mainly on cyclist’s behavior as a function of the Covid-19 transmission evolution in an urban environment; the impact of the Covid-19 on Washington DC’s Capital Bikeshare system is analyzed. Moreover, the possible relationship between the bikeshare usage and the Covid-19 transmission is explored. In order to investigate the transmission via the use of bikeshare system, a probabilistic contagion model is adopted. From the prediction outcome of the contagion model it, is found that 1 infected people can infect a maximum of 5 people and 3 infected people can infect a maximum of 27 people during a 3-day interval. The peak value of the 7-day moving average of infected people is 181: the maximum impact of Covid-19 transmission due to the usage of bikeshare services remain significant. However, if compared to other modes of public transport such as light rails and buses, bike sharing is safer for its direct users.
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