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This report looks at the potential impacts autonomous vehicle deployment could have on parking demand and how that might impact urban development. The study focused on three distinct areas of San Francisco. The research found that, contrary to headlines about AV impacts on parking, achieving large reductions in parking demand based on AV deployment will not be easy. To achieve significant parking reductions, AVs would need to be shared (not privately owned), pooled (riders willing to pick up other passengers along the way), have widespread geographic deployment (across entire metropolitan areas), and would need to complement high-capacity transit. Without all or most of these factors, parking demand may only by marginally impacted by AV deployment. The study also found that if parking demand could be reduced, different areas of the city would see quite different results. While many areas in San Francisco would see minimal development impacts as parking is not currently a significant driver or limiter of development, more auto-dominated areas could see substantial impacts if parking demand could be reduced by more than 40%. This raises interesting questions of how these levels of parking demand reduction might impact more auto-dominated and suburban areas throughout the country. This research was funded by Waymo.
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.
Whim is an app service that consolidates transportation services into a monthly subscription. The app includes access to taxis, public transportation, and rental cars. The app's goal is to reduce vehicle ownership by offering convenient access to multiple alternatives.
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 survey provides an inventory of daily travel in the US including demographic data on households, people, vehicles, and detailed information on daily travel by all modes of transportation and for all purposes.
“The introduction of driverless cars could affect how much money cities collect in parking, traffic citations, traffic cameras, towing fees, gasoline taxes, licensing, registration and other revenues.”
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 paper seeks to understand the potential causes of a decline in transit ridership by examining data from seven major U.S. cities – Boston, New York City, Washington D.C., Chicago, Denver, San Francisco and Los Angles.
This article examines the potential effects of driving on health and well-being.
Despite a growing economy, there has been a decrease in the average miles driven due in part to alternate modes of transportation and more opportunities to work and shop remotely.
Electric vehicles only make up a small percentage of the U.S.’s car fleet, however they are becoming more affordable.
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.
The growth of ride-hailing services has led to more traffic and less transit use in the United States, contrary to predictions that suggested the opposite would happen when transportation network companies first started becoming popular. Some data shows that household vehicle ownership increased in cities where Uber and Lyft are most heavily used, while there is also a growing number of urban households that own zero or few cars. The article analyzes this data to determine whether Americans own fewer cars, and discusses how vehicle ownership relates to population growth in several cities.
“Fehr & Peers was engaged by Lyft and Uber to determine their combined Vehicle Miles Traveled (VMT) in six metropolitan regions in September 2018 and compare that value to approximate total VMT in each area for the same period.”
The National Highway Traffic Safety Administration outlines the progression of automated vehicle technology and the ways it will improve safety. The NHTSA released safety guidelines for industry, states, and policymakers in 2017 (Automated Driving Systems: A Vision for Safety), and an expanded set of guidelines in 2018 (Preparing for the Future of Transportation: Automated Vehicles 3.0). Both documents are linked in the NHTSA in Action section at the end of this report.
“Taking Uber or Lyft to and from work and to run errands might seem more expensive than driving yourself–but in many cases, relying on a ride-hailing service is cheaper than buying and using a car of your own. A new calculator compares both scenarios, and might help you decide to ditch car ownership entirely.”
Currently, little planning is being done to prepare for driverless technology. Actors at multiple levels, however, have tools at their disposal to help ensure that new technology does not come at the expense of the nation’s remaining natural habitats. This Article advocates for a shift in paradigm from policies that are merely anti-car to those that are pro-density, and provides suggestions for both cities and suburban areas for how harness the positive aspects of driverless cars while trying to stem the negative. Planning for density regardless of technology will help to ensure that, for the world of the future, there is actually a world.
New data from the US EPA on power plant greenhouse gas emissions are in, and electric vehicles (EV) in the US are even cleaner than they were before. The climate change emissions created by driving on electricity depend on where you live, but on average, an EV driving on electricity in the U.S. today is equivalent to a conventional gasoline car that gets 80 MPG, up from 73 MPG in our 2017 update.
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.
Upon the roll-out of AVs into our streets, the importance of public and private sector partnerships are emphasized. With increased mobility, the demand for private rides could be increased and therefore increase congestion in our streets.
"This report attempts to address these questions by further exploring evidence of how TNCs are affecting the use of public transit and personal automobiles in several regions."
For 50 years, American geography and land use has been centered on the personal car. The three revolutions in vehicle sharing, automation and electrification present new challenges and also great opportunities for land use and transportation planners. Absent policy reform, the three revolutions may contribute to more sprawl, but a sustainable planning approach that supports both higher-density development and lower single-occupant (or zero-occupant) driving can once again put people first rather than their cars.
When ride-hailing services stormed into cities in the 2010s they offered a grand utopian promise: By tapping into America’s vast reservoir of idle vehicles, on-demand, app-based rides would reduce the need for personal car ownership and ultimately remove cars from the road. But now, less than a decade into this experiment, the industry is ‘fessing up. The ride-hailing giants released a joint analysis showing that their vehicles are responsible for significant portions of VMT in six major urban centers. Still, Uber and Lyft’s combined share is still vastly outstripped by personal vehicles.
"This report explores autonomous vehicle benefits and costs, and implications for various planning issues. It investigates how quickly self driving vehicles are likely to be developed and deployed based on experience with previous vehicle technologies, their benefits and costs, and how they are likely to affect travel demands and planning decisions such as optimal road, parking and public transit supply."
One of the more confusing words frequently associated with robocars (and all discussion of the future of transportation) is "shared." Unfortunately, this means two very different things, with quite different consequences.
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