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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.
How Uber affects public transit ridership is a relevant policy question facing cities worldwide. Theoretically, Uber’s effect on transit is ambiguous: while Uber is an alternative mode of travel, it can also increase the reach and flexibility of public transit’s fixed-route, fixed-schedule service. We estimate the effect of Uber on public transit ridership using a difference-in-differences design that exploits variation across U.S. metropolitan areas in both the intensity of Uber penetration and the timing of Uber entry. We find that Uber is a complement for the average transit agency, increasing ridership by five percent after two years. This average effect masks considerable heterogeneity, with Uber increasing ridership more in larger cities and for smaller transit agencies.
On May 7, 2016, residents of Austin, Texas, voted against Proposition 1, which would have allowed ridesourcing/transportation networking companies (TNCs) to continue using their own background check systems. The defeat of the proposition prompted Uber and Lyft to suspend services in Austin indefinitely. The disruption provided for a natural experiment to evaluate the impact of Uber and Lyft on users’ travel demand and the supply side implications of the entry of new players. Our paper focuses solely on the demand side user response to the disruption. In examining the impact, we conducted an online survey that combines stated and revealed preference questions (N=1,840) of former Uber and/or Lyft users in Austin to explore the effect of the disruption on travel behavior. In order to test our hypothesis of the impact of the service suspension on changes in travel mode choice and trip frequency we used regression analyses to model both the before and after travel behavioral pattern.
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.
This paper aims to examine the associations between ride-hailing and their spatial distribution in relation to key socioeconomic and built environment characteristics both at the trip origin and destination. To do so the study uses official data provided by Transportation Network Companies operating in the city of Chicago, with 32 million trips logged between November 1st, 2018 to June 28th, 2019. Among the built environment attributes we focus on the relationship between walkability levels and demand for ridehailing. Study findings indicate an association between ride-hailing and income levels, car-availability and raceethnicity.
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.
Even as ride-hailing has become ubiquitous in most urban areas, its impacts on individual travel are still unclear. This includes limited knowledge of demand characteristics (especially for pooled rides), travel modes being substituted, types of activities being accessed, as well as possible trip induction effects. The current study contributes to this knowledge gap by investigating ridehailing experience, frequency, and trip characteristics through two multi-dimensional models estimated using data from the Dallas-Fort Worth Metropolitan Area. Ride-hailing adoption and usage are modeled as functions of unobserved lifestyle stochastic latent constructs, observed transportation-related choices, and sociodemographic variables. The results point to low residential location density and people’s privacy concerns as the main deterrents to pooled ridehailing adoption, with non-Hispanic Whites being more privacy sensitive than individuals of other ethnicities.
This report analyzes EV use in TNC fleets from 2016 through 2018. Data sets from TNCs and charging service providers are used to analyze charging and use patterns of EVs within TNC fleets. The emissions benefits of EV use withing TNC fleets is quanitfied, assessments of EVs to perform TNC services are made, and the effects of EV use within TNC fleets on charging behavior of non-TNC EVs is understood.
In this study, newly available Chicago transportation network provider data were explored to identify the extent to which different socioeconomic, spatiotemporal, and trip characteristics affect willingness to pool (WTP) in ridehailing trips. Multivariate linear regression and machine-learning models were employed to understand and predict WTP based on location, time, and trip factors. The results show intuitive trends, with income level at drop-off and pickup locations and airport trips as the most important predictors of WTP. Results from this study can help TNCs and cities devise strategies that increase pooled ride-hailing, thereby reducing adverse transportation and energy impacts from ride-hailing modes.
Transportation network companies (TNCs) such as Uber and Lyft have grown tremendously over the last decade, particularly in the San Francisco Bay Area. Nonetheless, relatively little publicly available data exist about the users of these services, their travel behaviors, volume of use, the times and locations of TNC trips, and how TNC services are affecting transportation system performance overall. This paper describes the methods and descriptive results of the first large-scale smartphone-based TNC user survey conducted in the California Bay Area in the fall 2018 and spring of 2019.
This paper identifies major aspects of ridesourcing services provided by Transportation Network Companies (TNCs) which influence vehicles miles traveled (VMT) and energy use. Using detailed data on approximately 1.5 million individual rides provided by RideAustin in Austin Texas, we quantify the additional miles TNC drivers travel: before beginning and after ending their shifts, to reach a passenger once a ride has been requested, and between consecutive rides (all of which is referred to as deadheading); and the relative fuel efficiency of the vehicles that RideAustin drivers use compared to the average vehicle registered in Austin.
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.
The impacts of ride-hailing services on the transportation system have been immediate and major. Yet, public agencies are only beginning to understand their magnitude because the private ride-hailing industry has provided limited amounts of meaningful data. Consequently, public agencies responsible for managing congestion and providing transit services are unable to clearly determine who uses ride-hailing services and how their adoption influences established travel modes, or forecast the potential growth of this emergent mode in the future. To address these pressing questions, an intercept survey of ride-hailing passengers was conducted in the Greater Boston region in fall 2017. The responses, which enabled a robust description of ride-hailing passengers for the region, were used to analyze how new on-demand mobility services such as Uber and Lyft may be substituting travel by other modes.
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.
Ride-hailing such as Uber and Lyft are changing the ways people travel. Despite widespread claims that these services help reduce driving, there is little research on this topic. This research paper uses a quasi-natural experiment in the Denver, Colorado, region to analyze basic impacts of ride-hailing on transportation efficiency in terms of deadheading, vehicle occupancy, mode replacement, and vehicle miles traveled (VMT).
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.
Many cities are rolling out bike share programs. However, few studies have evaluated how bike share systems (BSS) are used to quantify their sustainability impacts. This study proposes a Bike Share Emission Reduction Estimation Model (BS-EREM) to quantify the environmental benefits from bike share trips and compare the greenhouse gas (GHG) emission reductions from BSS in eight cities in the United States, including New York, Chicago, Boston, Philadelphia, Washington D.C., Los Angeles, San Francisco, and Seattle. The BS-EREM model stochastically estimates the transportation modes substituted by bike share trips, considering factors such as trip distance, trip purpose, trip start time, the accessibility of public transits, and historical distributions of transportation mode choices.
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.
Moving toward sustainable mobility, the sharing economy business model emerges as a prominent practice that can contribute to the transition to sustainability. Using a system dynamics modeling approach, this paper investigates the impacts of an e-carsharing scheme in carbon emissions and in electric vehicle adoption. They study the VAMO scheme located in Fortaleza, Brazil, as the first e-carsharing scheme in the country. They study two policies combined: a VAMO planned growth policy and a retirement policy for conventional vehicles.
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.
This report builds on an on-going research effort that investigates emerging mobility patterns and the adoption of new mobility services. In this report, the authors focus on the environmental impacts of various modality styles and the frequency of ridehailing use among a sample of millennials (i.e., born from 1981 to 1997) and members of the preceding Generation X (i.e., born from 1965 to 1980). The total sample for the analysis included in this report includes 1,785 individuals who participated in a survey administered in Fall 2015 in California. In this study, the researchers focus on the vehicle miles traveled, the energy consumption and greenhouse gas (GHG) emissions for transportation purposes of various groups of travelers.
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.
This study analyzes the relation between shared mobility services and greenhouse gases (GHGs) emissions by using a nationally representative sample of US young adults. We conduct a comprehensive analysis based on the data collected in the 2017 National Household Travel Survey (NHTS).
Many studies have noted that denser and more accessible environments with higher level-of-service (LOS) tend to encourage higher levels of walking and bicycling activity. As streets are increasingly designed to facilitate safe cycling through built environment interventions, little has been done to evaluate perceptions of safety on different typologies, particularly one vs. two-way corridors. Theory would suggest that many individuals frame their commutes based in-part on the perceived safety of the environment, yet little research looks at varying street design and this perception. This study uses a moving camera approach to evaluate the perceived cycling comfort for drivers and cyclists on different roadway designs (multi-lane, one way; two-way, bidirectional street; single-lane, one-way).
Shared micro-mobility services are rapidly expanding yet little is known about travel behaviour. Understanding mode choice, in particular, is quintessential for incorporating micro-mobility into transport simulations in order to enable effective transport planning. We contribute by collecting a large dataset with matching GPS tracks, booking data and survey data for more than 500 travellers, and by estimating a first choice model between eight transport modes, including shared e-scooters, shared e-bikes, personal e-scooters and personal e-bikes.
Bike share systems are expanding efforts to be more equitable and accessible to everyone by offering adaptive bicycle options to people who might otherwise be unable to ride. These systems tend to range from the inclusion of electric bikes and standard trikes into the existing systems to offer a more full range of adaptive bicycle options for use at rental locations. Surveys of residents living in several low-income communities of color (n = 1,885) are used to explore the potential need for adaptive bike share options in urban locations. A national survey of cities and bike share operators (n = 70) is used to document the prevalence and basic models of adaptive bike share programming currently in place. Interviews conducted with bike share representatives in select cities with adaptive bike share programs provide context and details on how specific programs operate. Finally, interviews with adaptive bike share participants (n = 5) in Portland, Oregon, help to illuminate users’ experiences, including the perceived value and potential improvements for adaptive bike share.
This study aims to quantitatively estimate the environmental benefits of bike sharing. Using big data techniques, we estimate the impacts of bike sharing on energy use and carbon dioxide (CO2) and nitrogen oxide (NOX) emissions in Shanghai from a spatiotemporal perspective.
Technology-enhanced bikeshare features a dockless system with GPS-tracked electric bikes and a mobile app. As an additional transportation mode, it offers users greater accessibility and more flexibility compared to traditional bikeshare. This paper examines the causal impact of a tech-enhanced bikeshare program on public transit ridership, using evidence from a mid-sized metropolitan area in the Midwest of the United States.
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.
"In this paper we put together a list of the basic instincts that drive and contain travelers' behavior, showing how they mesh with technological progress and economic constraints."
This study explores the full life cycle impacts of connected and automated vehicles beyond just operational impacts to understand net energy and environmental performance.
This article details a study done in the neighborhood of Rosslyn in Arlington, Virginia to understand the relationship between e-scooter riders and non-riders in terms of e-scooter parking and pedestrian safety.
This article intends to inform policymakers of the potential effects of autonomous vehicles on road traffic congestion.
This paper explores the impacts of AVs on car trips using a case study of Victoria, Australia, specifically studying the potential increase in new trips and trip diversions from other modes such as public transport.
This paper examines the relationship between ride-hailing and parking demand by looking at ride-hailing trips that otherwise would have needed parking.
The introduction of shared autonomous vehicles (SAVs) in cities could potentially increase the number of vehicle miles traveled (VMT). The implementation of dynamic ride-sharing (DRS) systems could limit this increase and potentially result in a net reduction in VMT.
This article questions the effects of parking minimums and policies on residential renters in the U.S. by analyzing a sample of rental housing units from the 2011 American Housing Survey.
This article studies the relationship between gasoline consumption and urban design patterns by comparing 32 principal cities from around the world. The purpose is to evaluate physical planning policies for conserving transportation energy in urban areas.
This article examines the potential effects of driving on health and well-being.
This article examines the relationship between urban form and vehicle miles travelled, especially as it relates to last mile goods delivery and greenhouse gas emissions.
This resource examines how new technologies in AVs will affect traffic behavior, and how these new behaviors will impact congestion, capacity, and efficiency of road networks.
This resource studies whether mobility as a service (MaaS) can be used to promote shared modes. Initial results from surveys showed that MaaS bundles can be used as a tool to introduce more travelers to shared modes.
This article breaks down the varying types of ride sharing services in China and details their differing business models and levels of success.
This article examines bicyclists’ travel behavior for transportation and for recreational purposes based on preferences, physical and social environmental factors, and perceived safety.
There is a growing market for pedestrian- and transit-oreinted development. This article outlines existing literature in order to assess how increased demand for these types of developments is impacting land value. It also includes suggestions for further research on the topic.
This article examines the theoretical heat-energy demand of different types of urban form at a scale of 500 m × 500 m.
This article outlines a case study of Los Angeles parking requirements, studying whether parking requirements impact the amount and type of housing that is developed, particularly in housing developed in old vacant and commercial buildings.
This article discusses regulations around land use and why careful implementation is important. The author studied how two areas of Los Angeles near rail stations developed housing under baseline land use regulations and found that developers were most sensitive to density restrictions and parking requirements.
TNCs provide on-demand mobility service that either complements or competes with transit services. This article studies how TNCs influence changes in urban travel patterns as well as energy and environmental implications.
Recent research on autonomous vehicles (AV) has shown a substantive dive into the technical aspects of AVs, but our understanding of the secondary effects of AVs is minimal in comparison (Glancy, 2015; Mitteregger, Soteropoulos, Bröthaler, & Dorner, 2019; Terry & Bachmann, 2019). This article offers a look at how automation of one of the cornerstones of many municipal government—solid waste collection—could be altered with the advent of AVs.
This article discusses an experiment conducted to investigate the factors contributing to travel mode choice. The experiment found that subjects were more inclined to chose cars over other forms of transportation, even when another form of transportation might have been more ideal based on cost or travel time. This demonstrates the concept of car stickiness, where travelers are heavily biased towards traveling in cars over other forms of transportation.
“This research examines office parking at a series of case study sites in suburban Southern California, identifying its impact on travel behavior, development density, development cost, and urban design.”
This paper surveys emerging mobility services in order to highlight the key points of the concept of “mobility as a service” and to develop an index that evaluates the level of mobility integration of each service.
This article studies how emerging “smart mobility” systems will affect equity issues in Portland, Oregon. It suggests that affordable and improved public transit, ridesharing and active transportation could address many transportation challenges.
This article is a literature review of the definition and effects of urban sprawl for the purpose of implementing planning policies that discourage sprawl.
“This study investigates neighborhood scale net migration of young adults in the top 20 urbanized areas (UAs) in the United States between 1980 and 2010.”
"This research explored how these new options could be synergistic with public transit models and detailed the experiences of two transit operators that entered into service delivery partnerships with a transportation network company and a micro- transit operator. Based on a series of interviews and the experiences of these two public agencies, this research provides a set of key takeaways and recommendations for transit operators exploring the potential of partnering with new mobility services such as transportation network companies (e.g., Uber or Lyft) and microtransit (e.g., Bridj or Via)."
"Thanks to a literature review, interviews of the players of the French CEP sector and urban parcel delivery sector, as well as comparisons with other European countries, this article analyzes the sector's changes, its drivers, and provides an accurate picture, based on examples and figures, of an under-studied sector. The article also highlights some future prospects for the new segment such as the segment's consolidation and the rise of cross-border e-commerce."
With the potential to save nearly 30,000 lives per year in the United States, autonomous vehicles portend the most significant advance in auto safety history by shifting the focus from minimization of post-crash injury to collision prevention. I have delineated the important public health implications of autonomous vehicles and provided a brief analysis of a critically important ethical issue inherent in autonomous vehicle design. The broad expertise, ethical principles, and values of public health should be brought to bear on a wide range of issues pertaining to autonomous vehicles.
This paper, for the first time, presents comparable projections of travel behavior impacts of the introduction of autonomous vehicles (AVs) into the private car fleet for two countries, namely the USA and Germany. The focus is on fully autonomous vehicles (AVs) which allow drivers to engage in other activities en route. Two 2035 scenarios – a trend scenario and an extreme scenario – are presented for both study countries. For these projections, we combine a vehicle technology diffusion model and an aspatial travel demand model. Factors that influence AV impact in the behavioral model are mainly new automobile user groups, e.g. travelers with mobility impairments, and altered generalized costs of travel, e.g. due to a lower value of travel time savings for car travel. The results indicate that AV penetrations rates might be higher in Germany (10% or 38% respectively) than in the USA (8% or 29% respectively) due to a higher share of luxury cars and quicker fleet turnover. On the contrary, the increase of vehicle mileage induced by AVs is not higher in Germany (+2.4% or +8.6% respectively) than in the USA (+3.4% or +8.6% respectively). This is mainly due to the lack of mode alternatives and lower fuel costs resulting in a higher share of travel times among the total generalized costs of travel in the USA. These results clearly indicate that context factors shaped by national policy will influence AV adoption and impact on travel demand changes. Based on these results the paper draws policy recommendations which will help to harness the advantages of AVs while avoiding their negative consequences.
This document includes the interests of most, if not all, major issues surrounding the impact AVs will have on our communities, government, and environment once they land.
Although recent studies of Shared Autonomous Vehicles (SAVs) have explored the economic costs and environmental impacts of this technology, little is known about how SAVs can change urban forms, especially by reducing the demand for parking. This study estimates the potential impact of SAV system on urban parking demand under different system operation scenarios with the help of an agent-based simulation model. The simulation results indicate that we may be able to eliminate up to 90% of parking demand for clients who adopt the system, at a low market penetration rate of 2%. The results also suggest that different SAV operation strategies and client's preferences may lead to different spatial distribution of urban parking demand.
"This paper builds on the growing scholarship on neighbourhood-level GHG production by combining emissions calculations from embodied energy, building-operating energy, and transportation energy, examining four variations of residential density."
This article examines what's driving interest and experimentation in MaaS in cities around the world, outlines the core elements of MaaS and how this concept could evolve, and describes the role of government and the private sector in realizing the benefits MaaS brings.
Inclusive of manufacturing, transportation to the US, and the use phase, this study looks at the environmental impact of e-scooters compared to the use of alternative modes of transportation.
"This paper assesses alternative fuel options for transit buses. We find that all alternative fuel options lead to higher life cycle ownership and external costs than conventional diesel. When external funding is available to pay for 80% of vehicle purchase expenditures (which is usually the case for U.S. transit agencies), BEBs yield large reductions (17–23%) in terms of ownership and external costs compared to diesel."
Automated vehicle (AV) policy development and assessment is a difficult and complicated process. Today’s road and vehicle policies are the product of a hundred years of lessons learned. They generally address five areas: safety, efficiency, mobility, convenience, and impact on the environment. Now the prospect-turned-reality of automated vehicles entering public roadways has opened up a number of new policy-related questions. Is it enough to simply modify current road and vehicle policies or will new policies need to be developed addressing much broader aspects of the transportation system? How can these policies be developed to accommodate technologies that either do not yet exist or are only now being tested on the road in constrained environments? Perhaps most importantly, how can policy influence technological design to safely operate with other road users and can we look ahead to have a better view of potential unintended consequences?
We quantify the importance of early action to tackle urban sprawl. We focus on the long-term nature of infrastructure decisions, specifically local roadways, which can lock in greenhouse gas emissions for decades to come. The location and interconnectedness of local roadways form a near permanent backbone for the future layout of land parcels, buildings, and transportation options. We provide new estimates of the environmental impact of low-connectivity roads, characterized by cul-de-sacs and T-intersections, which we dub street-network sprawl. We find an elasticity of vehicle ownership with respect to street connectivity of –0.15—larger than suggested by previous research. We then apply this estimate to quantify the long-term emissions implications of alternative scenarios for street-network sprawl. On current trends alone, we project vehicle travel and emissions to fall by ∼3.2% over the 2015–2050 period, compared to a scenario where sprawl plateaus at its 1994 peak. Concerted policy efforts to increase street connectivity could more than triple these reductions to ∼8.8% by 2050. Longer-term reductions over the 2050–2100 period are more speculative, but could be more than 50% greater than those achieved by 2050. The longer the timescale over which mitigation efforts are considered, the more important it becomes to address the physical form of the built environment.
"In this paper we propose a new method to study how replacing privately owned conventional vehicles with automated ones affects traffic delays and parking demand in a city. The model solves what we designate as the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP), which dynamically assigns family trips in their automated vehicles in an urban road network from a user equilibrium perspective where, in equilibrium, households with similar trips should have similar transport costs.
Based on the 2001 and 2009 National Household Travel Surveys, this paper analyzes trends and determinants of multimodal car use in the U.S. during a typical week by distinguishing between (1) monomodal car users who drive or ride in a car for all trips, (2) multimodal car users who drive or ride in a car and also use non-automobile modes, and (3) individuals who exclusively walk, cycle, and/or ride public transportation. We find that during a typical week a majority—almost two thirds—of Americans use a car and make at least one trip by foot, bicycle, or public transportation. One in four Americans uses a car and makes at least seven weekly trips by other modes of transportation. Results from multinomial and logistic regression analyses suggest there may be a continuum of mobility types ranging from monomodal car users to walk, bicycle, and/or public transportation only users—with multimodal car users positioned in-between the two extremes. Policy changes aimed at curtailing car use may result in movements along this spectrum with increasing multimodality for car users.
"This paper provides a review of scenarios on these issues to date. Although some scenario studies provide useful insights about urban growth and change, very few consider detailed impacts of AVs on urban form, such as the density and mix of functions, the layout of urban development and the accessibility of locations, including the distance to transit."
This paper introduces Metrolinx’s recently released Mobility Hub Guidelines and highlights two key aspects of the document: the importance of classifying the current and planned urban context and transportation function at a mobility hub, and methods to overcome challenges in achieving both transport and placemaking roles.
"Our primary focus is travel related energy consumption and emissions, since potential lifecycle impacts are generally smaller in magnitude. We explore the net effects of automation on emissions through several illustrative scenarios, finding that automation might plausibly reduce road transport GHG emissions and energy use by nearly half – or nearly double them – depending on which effects come to dominate."
Concerns over rising fuel prices and greenhouse-gas emissions have prompted research into the influences of built environments on travel, notably vehicle miles traveled (VMT). Accessibility to basic employment has comparatively modest effects, as do size of urbanized area, and rail-transit supplies and usage. Nevertheless, urban planning and city design should be part of any strategic effort to shrink the environmental footprint of the urban transportation sector.
The continued use of minimum parking requirements is likely to encourage automobile use at a time when metropolitan areas are actively seeking to manage congestion and increase transit use, biking, and walking. Widely discussed ways to reform parking policies may be less than effective if planners do not consider the remaining incentives to auto use created by the existing parking infrastructure. Planners should encourage the conversion of existing parking facilities to alternative uses.
"This research analyzed the competitiveness of freight tricycles, low- capacity freight delivery vehicles, as compared with diesel vans in urban areas. Freight tricycles, also known as electric-assisted trikes, are low- emission vehicles powered by a combination of human effort and an electric engine. This research developed a cost model that incorporated vehicle ownership and operation models as well as logistics constraints such as time windows, cargo capacity, fuel consumption, and energy use. Unlike previous research efforts, the model was tailored to the unique characteristics of freight tricycles and diesel van deliveries in urban areas. The model was used to analyze the competitiveness of freight tricycles against diesel- powered delivery vans. "
"Carsharing exemplifies a growing trend towards service provision displacing ownership of capital goods. We developed a model to quantify the impact of carsharing on greenhouse gas (GHG) emissions. The study took into account different types of households and their trip characteristics. The analysis considers five factors by which carsharing can impact GHG emissions: transportation mode change, fleet vintage, vehicle optimization, more efficient drive trains within each vehicle type, and trip aggregation. Access to carsharing has already been shown to lead some users to relinquish ownership of their personal vehicle. We find that even without a reduction in vehicle-kilometers traveled the change in characteristics of the vehicles used in carsharing fleets can reduce GHGs by more than 30%. Shifting some trips to public transit provides a further 10%–20% reduction in GHGs"
"In the United States, road infrastructure funding is declining due to an increase in fuel efficiency and the non-adjustment of fuel taxes to inflation. Legislation to tax plug-in vehicles has been proposed or implemented in several states. This paper assesses (1) the magnitude of the decline in federal fuel tax revenue caused by plug-in vehicles and (2) quantifies the revenue that could be generated from a federal plug in vehicle registration fee.
Different business models of AVs, including Shared AVs (SAVs) and Private AVs (PAVs), will lead to significantly different changes in regional vehicle inventory and Vehicle Miles Traveled (VMT). Most prior studies have already explored the impact of SAVs on vehicle ownership and VMT generation. Limited understanding has been gained regarding vehicle ownership reduction and unoccupied VMT generation potentials in the era of PAVs. Motivated by such research gap, this study develops models to examine how much vehicle ownership reduction can be achieved once private conventional vehicles are replaced by AVs and the spatial distribution of unoccupied VMT accompanied with the vehicle reduction. The models are implemented using travel survey and synthesized trip profile from Atlanta Metropolitan Area. The results show that more than 18% of the households can reduce vehicles, while maintaining the current travel patterns. This can be translated into a 9.5% reduction in private vehicles in the study region. Meanwhile, 29.8 unoccupied VMT will be induced per day per reduced vehicles. A majority of the unoccupied VMT will be loaded on interstate highways and expressways and the largest percentage inflation in VMT will occur on minor local roads. The results can provide implications for evolving trends in household vehicles uses and the location of dedicated AV lanes in the PAV dominated future.
This study examines the potential changes in residential location choice in a scenario where shared autonomous vehicles (SAVs) are a popular mode of travel in the Atlanta metropolitan area. This hypothetical study is based on an agent-based simulation approach, which integrates residential location choice models with a SAV simulation model. The coupled model simulates future home location choices given current home location preferences and real estate development patterns. The results indicate that commuters may relocate to neighborhoods with better public schools and more amenities due to reductions in commute costs.
"Here we present a unique long-term (decadal) record of CO2 mole fractions from five sites across Utah’s metropolitan Salt Lake Valley. Four state-of-the-art global-scale emission inventories also have a nonlinear relationship with population density across the city; however, in contrast to our observations, they all have nearly constant emissions over time. Our results indicate that decadal scale changes in urban CO2 emissions are detectable through monitoring networks and constitute a valuable approach to evaluate emission inventories and studies of urban carbon cycles."
This paper evaluates the greenhouse gas (GHG) emission impacts that result from individuals participating in carsharing organizations within North America. The authors conducted an online survey with members of major carsharing organizations and evaluated the change in annual household emissions (e.g., impact) of respondents that joined carsharing. The results show that a majority of households joining carsharing are increasing their emissions by gaining access to automobiles.
This work describes the design of an agent-based model for shared autonomous vehicle (SAV) operations, the results of many case-study applications using this model, and the estimated environmental benefits of such settings, versus conventional vehicle ownership and use. Preliminary results indicate that each SAV can replace around eleven conventional vehicles, but adds up to 10% more travel distance than comparable non-SAV trips, resulting in overall beneficial emissions impacts, once fleet-efficiency changes and embodied versus in-use emissions are assessed.
"Dockless bike share systems present an opportunity for cities to expand access to bike share by lowering costs and geographic barriers, but also create additional challenges in the areas of maintenance, parking, and right-of- way management. Most dockless providers are also private, venture-capital funded entities, representing a significant departure from current public and non-profit approaches. Other cities have encountered challenges in securing cooperation from these operators in areas such as data transparency. This raises a key question: To what extent can cities use contracts and governance to exchange use of the public right-of-way for operating requirements that advance equity, accessibility, innovation, and other goals? Using case studies from other U.S. cities and drawing insights from the wider “smart mobility” literature, this research presents recommendations for regulating dockless bike share in cities and ties these approaches to the implementation of Nice Ride Minnesota’s dockless pilot. "
Focus on emissions and energy efficiency has long been focused on vehicles and improving their efficiency. This article discusses the option that hasn't been utilized as much by policy markers, to just limit the amount people drive.
The author presents his view of limitations of prediction and how it apply to transportation prediction such as ridership prediction. He describes the concepts for planning the future (with time and space) that always emphasize the freedom as the goal.
In this study, we present exploratory evidence of how “ridesourcing” services (app-based, on-demand ride services like Uber and Lyft) are used in San Francisco. We explore who uses ridesourcing and for what reasons, how the ridesourcing market compares to that of traditional taxis, and how ridesourcing impacts the use of public transit and overall vehicle travel. In spring 2014, 380 completed intercept surveys were collected from three ridesourcing “hot spots” in San Francisco. We compare survey results with matched-pair taxi trip data and results of a previous taxi user survey. We also compare travel times for ridesourcing and taxis with those for public transit.
"This research shows that public transportation (in its current form) will only remain economically competitive where demand can be bundled to larger units. In particular, this applies to dense urban areas, where public transportation can be offered at lower prices than autonomous taxis (even if pooled) and private cars. Wherever substantial bundling is not possible, shared and pooled vehicles serve travel demand more efficiently."
"Automated driving technologies are currently penetrating the market, and the coming fully autonomous cars will have far-reaching, yet largely unknown, implications. A critical unknown is the impact on traveler behavior, which in turn impacts sustainability, the economy, and well-being. Most behavioral studies, to date, either focus on safety and human factors (driving simulators; test beds), assume travel behavior implications (microsimulators; network analysis), or ask about hypothetical scenarios that are unfamiliar to the subjects (stated preference studies). Here we present a different approach, which is to use a naturalistic experiment to project people into a world of self-driving cars. We mimic potential life with a privately-owned self-driving vehicle by providing 60 h of free chauffeur service for each participating household for use within a 7-day period. We seek to understand the changes in travel behavior as the subjects adjust their travel and activities during the chauffeur week when, as in a self-driving vehicle, they are explicitly relieved of the driving task. In this first pilot application, our sample consisted of 13 subjects from the San Francisco Bay area, drawn from three cohorts: millennials, families, and retirees. We tracked each subject’s travel for 3 weeks (the chauffeur week, 1 week before and 1 week after) and conducted surveys and interviews. During the chauffeur week, we observed sizable increases in vehicle-miles traveled and number of trips, with a more pronounced increase in trips made in the evening and for longer distances and a substantial proportion of “zero-occupancy” vehicle-miles traveled."
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