Electrification of Transit Buses in the United States Reduces Greenhouse Gas Emissions.

The transportation sector is the largest emitter of greenhouse gas emissions (GHGs) in the United States. Increased use of public transit and electrification of public transit could help reduce these emissions. The electrification of public transit systems could also reduce air pollutant emissions in densely populated areas, where air pollution disproportionally burdens vulnerable communities with high health impacts and associated social costs. We analyze the life cycle emissions of transit buses powered by electricity, diesel, gasoline, and compressed natural gas and model GHGs and air pollutants mitigated for a transition to a fully electric U.S. public transit bus fleet using transit agency-level data. The electrification of the U.S. bus fleet would reduce several conventional air pollutants and has the potential to reduce transit bus GHGs by 33-65% within the next 14 years depending on how quickly the transition is made and how quickly the electricity grid decarbonizes. A levelized cost of driving analysis shows that with falling capital costs and an increase in annual passenger-kilometers of battery electric buses, the technology could reach levelized cost parity with diesel buses when electric bus capital costs fall below about $670 000 per bus.

Public transit stop density is associated with walking for exercise among a national sample of older adults.

BACKGROUND: Walking is the primary and preferred mode of exercise for older adults. Walking to and from public transit stops may support older adults in achieving exercise goals. This study examined whether density of neighborhood public transit stops was associated with walking for exercise among older adults. METHODS: 2018 National Health and Aging Trends Study (NHATS) data were linked with the 2018 National Neighborhood Data Archive, which reported density of public transit stops (stops/mile2) within participants' neighborhood, defined using census tract boundaries. Walking for exercise in the last month was self-reported. The extent to which self-reported public transit use mediated the relationship between density of neighborhood public transit stops and walking for exercise was examined. Covariates included sociodemographic characteristics, economic status, disability status, and neighborhood attributes. National estimates were calculated using NHATS analytic survey weights. RESULTS: Among 4,836 respondents with complete data, 39.7% lived in a census tract with at least one neighborhood public transit stop and 8.5% were public transit users. The odds of walking for exercise were 32% higher (OR = 1.32; 95% confidence interval: 1.08, 1.61) among respondents living in a neighborhood with > 10 transit stops per mile compared to living in a neighborhood without any public transit stops documented. Self-reported public transit use mediated 24% of the association between density of neighborhood public transit stops and walking for exercise. CONCLUSIONS: Density of neighborhood public transit stops was associated with walking for exercise, with a substantial portion of the association mediated by self-reported public transit use. Increasing public transit stop availability within neighborhoods may contribute to active aging among older adults.

Experimental studies of particle removal and probability of COVID-19 infection in passenger railcars.

A series of experiments in stationary and moving passenger railcars was conducted to measure the removal rates of particles in the size ranges of SARS-CoV-2 viral aerosols, and the air changes per hour provided by the existing and modified air handling systems. The effect of ventilation and air filtration systems on removal rates and their effects on estimated probability (i.e., risk) of infection was evaluated in a range of representative conditions: (1) for two different ratios of recirculated air (RA) to outdoor air (OA) (90:10 RA:OA and 67:33 RA:OA); (2) using minimum efficiency reporting value (MERV) filters with standard (MERV-8) and increased (MERV-13) filtration ratings; and (3) in the presence and absence of a portable high-efficiency particulate-air (HEPA) room air purifier system operated at clean air delivery rate (CADR) of 150 and 550 cfm. The higher-efficiency MERV-13 filters significantly increased particle removal rates on average by 3.8 to 8.4 hr-1 across particle sizes ranging from 0.3 to 10 µm (p < 0.01) compared to MERV-8 filters. The different RA:OA ratios and the use of a portable HEPA air purifier system had little effect on particle removal rates. MERV-13 filters reduced the estimated probability of infection by 42% compared to the MERV-8 filter. The use of a HEPA-air purifier with a MERV-13 filter causes a 50% reduction in the estimated probability of infection. Upgrading the efficiency of HVAC filters from MERV-8 to MERV-13 in public transit vehicles is the most effective exposure control method resulting in a clear reduction in the removal rates of aerosol particles and the estimated probability of infection.

Associations Between COVID-19 Vaccine Hesitancy and Socio-Spatial Factors in NYC Transit Workers 50 Years and Older.

This analysis investigates how age, race/ethnicity, and geographic location contributed to vaccine hesitancy in a sample of 645 New York City (NYC) Transport Workers Union (TWU), Local 100 members surveyed in August 2020. Union members ages 50+ were 46% less likely to be vaccine hesitant than their younger counterparts (OR 0.64; 95% CI 0.42, 0.97). Non-Whites (OR 3.95; 95% 2.44, 6.39) and those who did not report their race (OR 3.10; 95% CI 1.87, 5.12) were significantly more likely to be vaccine hesitant than Whites. Those who were not concerned about contracting COVID-19 in the community had 1.83 greater odds (95% CI 1.12, 2.98) of being vaccine hesitant than those who were concerned. Older respondents tended to reside in Queens while vaccine hesitant and non-White respondents were clustered in Brooklyn. General trends observed in COVID-19 vaccine hesitancy persist in a population of high risk, non-healthcare essential workers.

Inclusive growth, public transit infrastructure investments and neighbourhood trajectories of inequality in Montreal.

Investing in accessible, affordable and sustainable modes of transportation is increasingly seen as an important policy tool for fostering the development of more inclusive cities and combating the rise in inequality. In this article, we review how the concept of inclusive growth has gained traction at the local level framed within a discourse of building more equitable and sustainable cities with a particular emphasis on transportation infrastructure projects as a way of operationalizing the concept as a policy tool. Using Montreal as a case study, we then proceed to evaluate two competing proposals for major public transit infrastructure projects (the Pink line and the REM Phase II) to see if one may potentially offer more inclusive outcomes in terms of transit access and mobility. We do so by first examining changes in the spatial configurations of neighbourhood income disparities in the city between 1981 and 2016. After identifying a pattern of growing spatial polarization between higher- and lower-income neighbourhoods, we use a buffer analysis of transit stations to assess which of the two proposed transit infrastructure projects is best positioned to curb the growth of neighbourhood disparities. Our results suggest the proposed Pink line project provides more coverage in terms of accessibility and connecting economically disadvantaged neighbourhoods from Montreal Nord to Lachine with the downtown core.

Public transit infrastructure and heat perceptions in hot and dry climates.

Many cities aim to progress toward their sustainability and public health goals by increasing use of their public transit systems. However, without adequate protective infrastructure that provides thermally comfortable conditions for public transit riders, it can be challenging to reach these goals in hot climates. We took micrometeorological measurements and surveyed riders about their perceptions of heat and heat-coping behaviors at bus stops with a variety of design attributes in Phoenix, AZ, USA, during the summer of 2018. We identified the design attributes and coping behaviors that made riders feel cooler. We observed that current infrastructure standards and material choices for bus stops in Phoenix are insufficient to provide thermal comfort, and can even expose riders to health risks. Almost half of the study participants felt hot or very hot at the time they were surveyed, and more than half reported feeling thermally uncomfortable. On average, shade reduced the physiological equivalent temperature (PET) by 19 °C. Moreover, we found significant diurnal differences in PET reductions from the shade provided by various design attributes. For instance, all design attributes were effective in reducing PET in the morning; however, a vegetated awning did not provide statistically significant shade reductions in the afternoon. Temperatures of sun-exposed surfaces of man-made materials exceeded skin burn thresholds in the afternoon, but shade was effective in bringing the same surfaces to safe levels. Aesthetically pleasing stops were rated as cooler than stops rated as less beautiful. We conclude that cities striving to increase public transit use should prioritize thermal comfort when designing public transit stops in hot climates.

Covid-19 pandemic impacts on essential transit riders: Findings from a U.S. Survey.

The Covid-19 pandemic has decimated public transit service across the United States and caused significant decreases in ridership. Little is known about the reasons for unevenness in pandemic-era mode shifts and the impacts of pandemic-related transit reductions on riders' day-to-day lives. Using a national survey of U.S. transit riders (n = 500) conducted in fall 2020, this study examines changes in transit use since the pandemic began, the reasons for transit reductions, and the effects of reduced transit use and transit service on transit riders' ability to meet their travel needs. The Covid-19 pandemic has exacerbated existing transportation burdens for those who have limited mobility options, those facing socioeconomic challenges, Hispanic or Latinx riders, and female, non-binary or genderqueer people. We close with recommendations for strengthening transit service for these groups in the long term as we recover from the pandemic.

Managing public transit during a pandemic: The trade-off between safety and mobility.

During a pandemic such as COVID-19, managing public transit effectively becomes a critical policy decision. On the one hand, efficient transportation plays a pivotal role in enabling the movement of essential workers and keeping the economy moving. On the other hand, public transit can be a vector for disease propagation due to travelers' proximity within shared and enclosed spaces. Without strategic preparedness, mass transit facilities are potential hotbeds for spreading infectious diseases. Thus, transportation agencies face a complex trade-off when developing context-specific operating strategies for public transit. This work provides a network-based analysis framework for understanding this trade-off, as well as tools for calculating targeted commute restrictions under different policy constraints, e.g., regarding public health considerations (limiting infection levels) and economic activity (limiting the reduction in travel). The resulting plans ensure that the traffic flow restrictions imposed on each route are adaptive to the time-varying epidemic dynamics. A case study based on the COVID-19 pandemic reveals that a well-planned subway system in New York City can sustain 88% of transit flow while reducing the risk of disease transmission by 50% relative to fully-loaded public transit systems. Transport policy-makers can exploit this optimization-based framework to address safety-and-mobility trade-offs and make proactive transit management plans during an epidemic outbreak.

Examining the causal relationship between bike-share and public transit in response to the COVID-19 pandemic.

As urban transportation systems often face disruptive events, including natural and man-made disasters, the importance of resilience in the transportation sector has recently been on the rise. In particular, the worldwide spread of the COVID-19 pandemic resulted in a significant decrease in citizens' public transit use to avoid unnecessary physical contact with others. Accordingly, bike-share has been highlighted as one of the sustainable modes that can replace public transit and, thus, improve the overall resilience of the urban transportation systems in response to COVID-19. This study aims to examine the changes in causal relationships between bike-share and public transit throughout the COVID-19 pandemic in Seoul, Korea. We analyzed bike-share and public transit ridership from Jan 2018 to Dec 2020. We developed a weekly panel vector autoregressive (PVAR) model to identify the bike-transit relationships before and after the pandemic. Our results showed that COVID-19 weakens the competitive relationships between bike-share and bus transit and modal integration between bike-share and subway transit. This study also found that bus and subway transit were more competitive with each other after the outbreak of COVID-19. The study's findings suggest that bike-share can increase the overall resilience of the urban transportation system during the pandemic situation, particularly for those who rely on public transit for their mobility.

The lockdown, mobility, and spatial health disparities in COVID-19 pandemic: A case study of New York City.

The world has adopted unprecedented lockdown as the key method to mitigate COVID-19; yet its effect on pandemic outcomes and health disparities remains largely unknown. Adopting a multilevel conceptual framework, this research investigates how city-level lockdown policy and public transit system shape mobility and thus intra-city health disparities, using New York City as a case study. With a spatial method and multiple sources of data, this research demonstrates the uneven impact of the lockdown policy and public transit system in shaping local pandemic outcomes. Census tracts with people spending more time at home have lower infection and death rates, while those with a higher density of transit stations have higher infection and death rates. Residential profile matters and census tracts with a higher concentration of disadvantaged population, such as Blacks, Hispanics, poor and elderly people, and people with no health insurance, have higher infection and death rates. Spatial analyses identify clusters where the lockdown policy was not effective and census tracts that share similar pandemic characteristics. Through the lens of mobility, this research advances knowledge of health disparities by focusing on institutional causes for health disparities and the role of the government through intervention policy and public transit system.

Greenhouse Gas Emission Mitigation Pathways for Urban Passenger Land Transport under Ambitious Climate Targets.

Urban passenger land transport is an important source of greenhouse gas (GHG) emissions globally, but it is challenging to mitigate these emissions as this sector interacts with many other economic sectors. We develop the Climate change constrained Urban passenger Transport Integrated Life cycle assessment (CURTAIL) model to outline mitigation pathways of urban passenger land transport that are consistent with ambitious climate targets. CURTAIL uses the transport activity of exogenously defined modal shares to simulate the associated annual vehicle stocks, sales, and life cycle GHG emissions. It estimates GHG emission budgets that are consistent with global warming below 2 and 1.5 °C above preindustrial levels and seeks mitigation strategies to remain within the budgets. We apply it to a case study of Singapore, a city-state. Meeting a 1.5 °C target requires strong commitments in the transport and electricity sectors, such as reducing the motorized passenger activity, accelerating the deployment of public transit and of electrification, and decarbonizing the power generation system. Focusing on one mitigation technology or one mode of transport alone will not be sufficient to meet the target. Our novel model could be applied to any city to provide insights relevant to the design of urban climate change mitigation targets and policies.

Perceived risk of using shared mobility services during the COVID-19 pandemic.

The COVID-19 pandemic has caused our daily routines to change quickly. The pandemic provokes public fear, resulting in changes in what modes of transport people use to perform their daily activities. It is imperative for transportation authorities to properly identify the different degrees of behavioral change among various social groups. A major factor that can substantially explain individuals' behavioral changes is the personal risk perceptions toward using shared mobility solutions. Thus, this study explores the risk that individuals perceive while using public transit and ridesharing services (as the most widespread forms of shared mobility) during the COVID-19 pandemic. To do so, we designed and implemented a multidimensional travel-behavior survey in the Chicago metropolitan area that comprises socio-demographic information and retrospective questions related to attitudes and travel behavior before and during the pandemic. Utilizing a bivariate ordered probit modeling approach to better account for the potential correlation between unobserved factors, we simultaneously modeled the perceived risk of exposure to the novel coronavirus in case of riding transit and using ridesharing services. A wide range of factors is found to be influential on the perceived risk of using shared mobility services, including the socio-demographic attributes, built environment settings, and the virus spread. Further, our results indicate that the mitigation strategies to increase the ridership of shared mobility services should be adaptive considering the spatial variations.

Mobility in post-pandemic economic reopening under social distancing guidelines: Congestion, emissions, and contact exposure in public transit.

COVID-19 has raised new challenges for transportation in the post-pandemic era. The social distancing requirement, with the aim of reducing contact risk in public transit, could exacerbate traffic congestion and emissions. We propose a simulation tool to evaluate the trade-offs between traffic congestion, emissions, and policies impacting travel behavior to mitigate the spread of COVID-19 including social distancing and working from home. Open-source agent-based simulation models are used to evaluate the transportation system usage for the case study of New York City. A Post Processing Software for Air Quality (PPS-AQ) estimation is used to evaluate the air quality impacts. Finally, system-wide contact exposure on the subway is estimated from the traffic simulation output. The social distancing requirement in public transit is found to be effective in reducing contact exposure, but it has negative congestion and emission impacts on Manhattan and neighborhoods at transit and commercial hubs. While telework can reduce congestion and emissions citywide, in Manhattan the negative impacts are higher due to behavioral inertia and social distancing. The findings suggest that contact exposure to COVID-19 on subways is relatively low, especially if social distancing practices are followed. The proposed integrated traffic simulation models and air quality estimation model can help policymakers evaluate the impact of policies on traffic congestion and emissions as well as identifying hot spots, both temporally and spatially.

On bus ridership and frequency.

Even before the start of the COVID-19 pandemic, bus ridership in the United States had attained its lowest level since 1973. If transit agencies hope to reverse this trend, they must understand how their service allocation policies affect ridership. This paper is among the first to model ridership trends on a hyper-local level over time. A Poisson fixed-effects model is developed to evaluate the ridership elasticity to frequency on weekdays using passenger count data from Portland, Miami, Minneapolis/St-Paul, and Atlanta between 2012 and 2018. In every agency, ridership is found to be elastic to frequency when observing the variation between individual route-segments at one point in time. In other words, the most frequent routes are already the most productive in terms of passengers per vehicle-trip. When observing the variation within each route-segment over time, however, ridership is inelastic; each additional vehicle-trip is expected to generate less ridership than the average bus already on the route. In three of the four agencies, the elasticity is a decreasing function of prior frequency, meaning that low-frequency routes are the most sensitive to changes in frequency. This paper can help transit agencies anticipate the marginal effect of shifting service throughout the network. As the quality and availability of passenger count data improve, this paper can serve as the methodological basis to explore the dynamics of bus ridership.

Resiliency of on-demand multimodal transit systems during a pandemic.

During the COVID-19 pandemic, the collapse of the public transit ridership led to significant budget deficits due to dramatic decreases in fare revenues. Additionally, public transit agencies are facing challenges of reduced vehicle capacity due to social distancing requirements, additional costs of cleaning and protective equipment, and increased downtime for vehicle cleaning. Due to these constraints on resources and budgets, many transit agencies have adopted essential service plans with reduced service hours, number of routes, or frequencies. This paper studies the resiliency during a pandemic of On-Demand Multimodal Transit Systems (ODMTS), a new generation of transit systems that combine a network of high-frequency trains and buses with on-demand shuttles to serve the first and last miles and act as feeders to the fixed network. It presents a case study for the city of Atlanta and evaluates ODMTS for multiple scenarios of depressed demand and social distancing representing various stages of the pandemic. The case study relies on an optimization pipeline that provides an end-to-end ODMTS solution by bringing together methods for demand estimation, network design, fleet sizing, and real-time dispatching. These methods are adapted to work in a multimodal setting and to satisfy practical constraints. In particular, a limit is imposed on the number of passenger transfers, and a new network design model is introduced to avoid the computational burden stemming from this constraint. Real data from the Metropolitan Atlanta Rapid Transit Authority (MARTA) is used to conduct the case study, and the results are evaluated with a high-fidelity simulation. The case study demonstrates how ODMTS provide a resilient solution in terms of cost, convenience, and accessibility for this wide range of scenarios.

Modeling epidemic spreading through public transit using time-varying encounter network.

Passenger contact in public transit (PT) networks can be a key mediate in the spreading of infectious diseases. This paper proposes a time-varying weighted PT encounter network to model the spreading of infectious diseases through the PT systems. Social activity contacts at both local and global levels are also considered. We select the epidemiological characteristics of coronavirus disease 2019 (COVID-19) as a case study along with smart card data from Singapore to illustrate the model at the metropolitan level. A scalable and lightweight theoretical framework is derived to capture the time-varying and heterogeneous network structures, which enables to solve the problem at the whole population level with low computational costs. Different control policies from both the public health side and the transportation side are evaluated. We find that people's preventative behavior is one of the most effective measures to control the spreading of epidemics. From the transportation side, partial closure of bus routes helps to slow down but cannot fully contain the spreading of epidemics. Identifying "influential passengers" using the smart card data and isolating them at an early stage can also effectively reduce the epidemic spreading.

Public transit use in the United States in the era of COVID-19: Transit riders' travel behavior in the COVID-19 impact and recovery period.

COVID-19 has upended travel across the world, disrupting commute patterns, mode choices, and public transit systems. In the United States, changes to transit service and reductions in passenger volume due to COVID-19 are lasting longer than originally anticipated. In this paper we examine the impacts of the COVID-19 pandemic on individual travel behavior across the United States. We analyze mobility data from Janurary to December 2020 from a sample drawn from a nationwide smartphone-based panel curated by a private firm, Embee Mobile. We combine this with a survey that we administered to that sample in August 2020. Our analysis provides insight into travel patterns and the immediate impacts of the COVID-19 pandemic on transit riders. We investigate three questions. First, how do transit riders differ socio-demographically from non-riders? Second, how has the travel behavior of transit riders changed due to the pandemic in comparison to non-riders, controlling for other factors? And third, how has this travel behavior varied across different types of transit riders? The travel patterns of transit riders were more significantly disrupted by the pandemic than the travel of non-riders, as measured by the average weekly number of trips and distance traveled before and after the onset of the pandemic. This was calculated using GPS traces from panel member smartphones. Our survey of the panel revealed that of transit riders, 75% reported taking transit less since the pandemic, likely due to a combination of being affected by transit service changes, concerns about infection risk on transit, and trip reductions due to shelter-in-place rules. Less than 10 percent of transit riders in our sample reported that they were comfortable using transit despite COVID-19 infection risk, and were not affected by transit service reductions. Transit riders were also more likely to have changed their travel behavior in other ways, including reporting an increase in walking. However, lower-income transit riders were different from higher-income riders in that they had a significantly smaller reduction in the number of trips and distance traveled, suggesting that these lower-income households had less discretion over the amount of travel they carried out during the pandemic. These results have significant implications for understanding the way welfare has been affected for transportation-disadvantaged populations during the course of the pandemic, and insight into the recovery of U.S. transit systems. The evidence from this unique dataset helps us understand the future effects of the pandemic on transit riders in the United States, either in further recovery from the pandemic with the anticipated effects of mass vaccination, or in response to additional waves of COVID-19 and other pandemics.

Marginal emission factors for public transit: Effects of urban scale and density.

The objective of this study is to determine the relationship between fundamental urban scale characteristics (population, area, density) and marginal emission factors (MEF) for public transit. Emissions intensity of travel is typically examined using average emission factors (AEF), but MEF (how emissions change with travel volume) are more important for understanding the effects of interventions. MEF and AEF are estimated and compared for transit systems across the U.S. using panel data from 376 urban areas over 27 years. Results show that both MEF and AEF vary substantially across cities and decrease with urban population, area, density, and transit system extent - but AEF are around 50% more sensitive to urban scale. The distinction between MEF and AEF is especially important for bus transit in smaller, less dense cities. Marginal analysis shows that mode shift from private vehicles to transit should be encouraged, even where average emissions from transit are higher.

Effect of Passenger Encumbrance and Mobility Aid Use on Dwell Time Variability in Low-floor Transit Vehicles.

Public transit serves users with a broad range of physical capabilities and design needs. However information about the operational effects of diverse users interacting with the transit system is scarce. This paper examined the occurrence and effects of boarding and alighting passengers with mobility aids (wheelchairs, scooters, walkers and canes), or with large items (carts, strollers, bicycles, or carrying an infant) on bus stop dwell time in a fixed-route bus service. On-board video data from low-floor public transit buses serving Ann Arbor, Michigan were used from 199 bus stops with at least one passenger boarding or alighting with a mobility aid or encumbered with a large item, and an additional 1642 bus stops without any mobility aids or encumbrances. A sequence of linear regression models examined the relationship between dwell time and the addition of variables representing passengers with mobility aids and encumbrances, and use of the on-vehicle access ramp, beyond explanatory variables typically used in dwell time analysis. Accounting for passengers boarding/alighting with mobility aids and encumbrances (p < 0.001) and use of the access ramp (p < 0.001) increased the variance explanation of a dwell time model based on boarding passengers by fare payment, alighting passengers by door use, and passenger load from 46% to 56%. Results indicate distinct patterns in the durations for boarding and alighting by passengers with vs. without mobility aids and encumbrances, and when a ramp is used by wheeled mobility users vs. ambulatory passengers with walking aids. The findings suggest that accounting for the presence of passengers with mobility aids or encumbrances and ramp use in dwell time analyses could help transit operators make their service operationally more efficient and inclusive for all passengers and encourage more use of fixed-route transit among individuals with disabilities.

Estimation of left behind subway passengers through archived data and video image processing.

Crowding is one of the most common problems for public transportation systems worldwide, and extreme crowding can lead to passengers being left behind when they are unable to board the first arriving bus or train. This paper combines existing data sources with an emerging technology for object detection to estimate the number of passengers that are left behind on subway platforms. The methodology proposed in this study has been developed and applied to the subway in Boston, Massachusetts. Trains are not currently equipped with automated passenger counters, and farecard data is only collected on entry to the system. An analysis of crowding from inferred origin-destination data was used to identify stations with high likelihood of passengers being left behind during peak hours. Results from North Station during afternoon peak hours are presented here. Image processing and object detection software was used to count the number of passengers that were left behind on station platforms from surveillance video feeds. Automatically counted passengers and train operations data were used to develop logistic regression models that were calibrated to manual counts of left behind passengers on a typical weekday with normal operating conditions. The models were validated against manual counts of left behind passengers on a separate day with normal operations. The results show that by fusing passenger counts from video with train operations data, the number of passengers left behind during a day's rush period can be estimated within 10 % of their actual number.