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Today's urban transportation systems face increasing challenges such as greenhouse gas (GHG) emissions, urban air quality, and traffic congestion. In this context, various initiatives of mutualized mobility have emerged. However, notably lacking is assessing the environmental impacts of mutualized transportation modes from a life cycle perspective. Using the actual urban transportation big data and related product life cycle data, this study combined with the life cycle assessment methodology and a “bottom-up” approach, explores the effect of mutualized mobility on greenhouse gas emissions of urban transportation systems for both Beijing and Toronto. The results showed that mutualized mobility might positively affect the sustainability of urban transport systems, albeit in very different ways.
Using data derived from 597 face-to-face interviews with ride-hailing users in Chengdu (China), we examined the influence of ride-hailing on travel frequency and mode choice and further analyzed what the main determinants for these are.
The growth of app-based ridesharing, microtransit, and TNCs presents a unique opportunity to reduce congestion, energy use, and emissions through reduced personal vehicle ownership and increased vehicle occupancy, the latter of which is largely dependent on the decisions of individual travelers to pool or not to pool. This research provides key insights into the policy levers that could be employed to reduce vehicle miles traveled and emissions by incentivizing the use of pooled on-demand ride services and public transit. We employ a general population stated preference survey of four California metropolitan regions (Los Angeles, Sacramento, San Diego, and the San FranciscoBay Area) to examine the opportunities and challenges for drastically expanding the market for pooling, taking into account the nuances in emergent travel behavior and demand sensitivity across on-demand mobility options.
Ridesharing holds promise as a more efficient and sustainable version of emergent ride-hailing services. However, the adoption of pooled services in which individuals pay a reduced fare to share a portion of their ridehailing trip with other passengers has substantially lagged in popularity to the standard single-party services offered by Uber and Lyft in many American cities. To help guide policies and programs targeted at increasing pooling shares, this study analyzes data collected during fall 2017 from an in-vehicle intercept survey of 944 ride-hailing passengers in the Greater Boston region. These data, which describe the socioeconomic background, mobility options, and trip context of single-party and pooled ride-hailing survey respondents, were used to identify differences in the trip patterns and individual characteristics of passengers adopting the two service types and then estimate the individual-level social and trip-related predictors of ridesharing for different purposes.
On-demand ridesourcing services from transportation network companies (TNCs), such as Uber and Lyft, have reshaped urban travel and changed externality costs from vehicle emissions, congestion, crashes, and noise. To quantify these changes, we simulate replacing private vehicle travel with TNCs in six U.S. cities.
Transportation network companies (TNCs), such as Uber and Lyft, have been hypothesized to both complement and compete with public transit. Existing research on the topic is limited by a lack of detailed data on the timing and location of TNC trips. This study overcomes that limitation by using data scraped from the Application Programming Interfaces of two TNCs, combined with Automated Passenger Count data on transit use and other supporting data. Using a panel data model of the change in bus ridership in San Francisco between 2010 and 2015, and confirming the result with a separate time-series model, we find that TNCs are responsible for a net ridership decline of about 10%, offsetting net gains from other factors such as service increases and population growth. We do not find a statistically significant effect on light rail ridership. Cities and transit agencies should recognize the transit-competitive nature of TNCs as they plan, regulate and operate their transportation systems.
Microtransit—shared transportation that offers dynamic routing and scheduling to efficiently match demand—is emerging as an ally to fixed-route services. However, its positive impacts are too often constrained by the politics and economics imposed by existing transit infrastructure. This paper proposes a solution that ‘‘flips transit on its head.’’ By rapidly prototyping microtransit services across cities and analyzing supply-demand mismatches, it is possible to launch truly data-driven transit services. To illustrate the framework, a unique dataset generated from a year of Dallas Area Rapid Transit’s GoLink service, one of the largest ondemand microtransit services in North America, is used. Mapping and machine learning are combined to empower planners to ‘‘join the dots’’ when (re)designing fixed-route transit lines. It is shown that microtransit should not simply fill in the gaps left by inefficiently scheduled bus routes: by incorporating it fully into their planning processes, cities and transit agencies could dramatically reverse the fortunes of public transit.
The rapidly developing concept of carsharing is an essential and scalable part of sustainable, multimodal mobility in urban environments. There is a clear need for carsharing operators to understand their users and how they use different transportation modes to intensify the development of carsharing and its positive impacts on the environment and urban cohabitation. The researchers foster this understanding by analyzing usage data of carsharing in a medium-sized German city. They compare user groups based on individual characteristics and their carsharing usage behavior. They focus on a station-based two-way carsharing scheme and its relation to free-floating carsharing.
Bike enthusiasts argue that bikesharing programs can be an important element of sustainable mobility planning in the urban cores of large metropolitan areas. However, the objective longterm impact of bikesharing on reducing auto-dependence is not well-examined, as prior studies have tended to rely on self-reported subjective mode substitution effects. We use a unique longitudinal dataset containing millions of geo-referenced vehicle registrations and odometer readings in Massachusetts over a six-year period - the Massachusetts Vehicle Census - to examine the causal impact of bikesharing on various metrics of auto-dependence in the inner core of Metro Boston.
Transport accounts for 40 % of global emissions, 72 % of which comes from road transport, and private cars are responsible for 60 % of road transport emissions. In cities, self-service bike sharing systems are quickly developing and are intended to offer an alternative and cleaner mode of transport than the car. However, the sustainability of such schemes is often taken as a given, rather than thoroughly evaluated. To address this gap, in this paper we undertake a life cycle assessment (LCA) of a public self-service bike sharing system in the city of Edinburgh, UK, modelling the production, operation and disposal elements of the system, but discounting additional food intake by users.
Vehicle sharing services (bikeshare, carshare, and e-scooters) offer the potential to improve mobility and accessibility for disadvantaged populations. This article reviews research related to equity and vehicle sharing, focusing on race/ethnicity, income, gender, age, and disability. We find evidence of disparities in use of shared vehicles, which is only partly explained by lack of physical proximity. Some studies reveal additional barriers to use, particularly for bikesharing.
On-demand ridesourcing services from transportation network companies (TNCs), such as Uber and Lyft, have reshaped urban travel and changed externality costs from vehicle emissions, congestion, crashes, and noise. To quantify these changes, this study simulated replacing private vehicle travel with TNCs in six U.S. cities.
The Handbook provides methods to quantify GHG emission reductions from a specified list of measures, primarily focused on project-level actions. The Handbook also includes a method to assess potential benefits of different climate vulnerability reduction measures, as well as measures that can be implemented to improve health and equity, again at the project level.
In 2021, City Council directed PBOT to identify new revenue sources that reflect the City’s policy goals, address the bureau’s structural deficit, and provide maximum flexibility to invest in our transportation system, and to present its recommended revenue proposals to Council during the FY 2022-23 budget development process. POEM is an initiative to raise funds for implementing transportatin options by essentially taxing driving.
This paper synthesizes and reviews all literature regarding autonomous vehicles and their impact on GHG emissions. The paper aims to eliminate bias and provide insight by incorporating statistical analysis.
Researchers at the Harvard Kennedy School's Taubman Center for State and Local Government outline potential policy issues that will arise as autonomous vehicles become more popular. The authors recommend five policies cities can implement to get out in front of autonomous vehicle deployments to ensure that autonomous vehicles can support community goals.
AARP Public Policy Institute, RAND Corporation and Urbanism Next collaborated to better understand the ways in which shared mobility and AVs will be impacting older adults. Through a review of literature, interviews with public and private sector players in this arena, and a roundtable with over 25 experts from around the country, the project team developed a framework that identifies a range of factors around new mobility and AVs that will be affecting older adults’ mobility, independence and safety. The framework is a guide for governments and private sector companies to help them think broadly about impacts, understand barriers, and can serve as an internal checklist to guide future policy, research and development.
Re-allocating space on streets to accommodate new uses – particularly for walking, biking, and being – is not new. COVID-era needs have accelerated the process that many communities use to make such street transitions, however. Many communities quickly understood that the street is actually a public place and a public good that serves broader public needs more urgent than the free flow or the storage of private vehicles. This book captures some of these quick changes to city streets in response to societal needs during COVID, with two open questions: 1) what changes will endure post-COVID?; and 2) will communities be more open to street reconfigurations, including quick and inexpensive trials, going forward?
Using experience from working on the Knight AV Initiative, Urbanism Next created this white paper to provide a foundation for public sector agencies to approach autonomous vehicle deployment and policy with a focus on equity. This report outlines ways that public agencies can identify community needs and shape deployment to ensure that AVs will be accessible for all.
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