计量经济分析 = Econometric analysis

Autonomous vehicles (AVs) represent a potentially disruptive yet beneficial change to our transportation system. This new technology has the potential to impact vehicle safety, congestion, and travel behavior. All told, major social AV impacts in the form of crash savings, travel time reduction, fuel efficiency and parking benefits are estimated to approach $2000 to per year per AV, and may eventually approach nearly $4000 when comprehensive crash costs are accounted for. Yet barriers to implementation and mass-market penetration remain. Initial costs will likely be unaffordable. Licensing and testing standards in the U.S. are being developed at the state level, rather than nationally, which may lead to inconsistencies across states. Liability details remain undefined, security concerns linger, and without new privacy standards, a default lack of privacy for personal travel may become the norm. The impacts and interactions with other components of the transportation system, as well as implementation details, remain uncertain. To address these concerns, the federal government should expand research in these areas and create a nationally recognized licensing framework for AVs, determining appropriate standards for liability, security, and data privacy.

https://www.aamva.org/WorkArea/DownloadAsset.aspx?id=6738

Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations

Autonomous vehicles (AVs) represent a potentially disruptive yet beneficial change to the transportation system This new technology has the potential to impact vehicle safety, congestion, and travel behavior All told, major social AV impacts in the form of crash savings, travel time reduction, fuel efficiency and parking benefits are estimated to approach $2,000 to per year per AV, and may eventually approach nearly $4,000 when comprehensive crash costs are accounted for Yet barriers to implementation and mass-market penetration remain Initial costs will likely be unaffordable Licensing and testing standards in the US are being developed at the state level, rather than nationally, which may lead to inconsistencies across states Liability details remain undefined, security concerns linger, and without new privacy standards, a default lack of privacy for personal travel may become the norm The impacts and interactions with other components of the transportation system, as well as implementation details, remain uncertain To address these concerns, the federal government should expand research in these areas and create a nationally recognized licensing framework for AVs, determining appropriate standards for liability, security, and data privacy

Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations

https://www.tandfonline.com/doi/pdf/10.1080/15472450.2017.1291351

Policy and society related implications of automated driving: A review of literature and directions for future research

Technological advances are bringing connected and autonomous vehicles (CAVs) to the ever-evolving transportation system. Anticipating public acceptance and adoption of these technologies is important. A recent internet-based survey polled 347 Austinites to understand their opinions on smart-car technologies and strategies. Results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation ($7253) appears to be much higher than that for adding partial (Level 3) automation ($3300) to their current vehicles. Ordered probit and other model specifications estimate the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding various automation technologies and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios ($1, $2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, and home-location decisions after AVs and SAVs become a common mode of transport. Higher-income, technology-savvy males, who live in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.

Assessing public opinions of and interest in new vehicle technologies: An Austin perspective

Shared autonomous (fully-automated) vehicles (SAVs) represent an emerging transportation mode for driverless and on-demand transport. Early actors include Google and Europe’s CityMobil2, who seek pilot deployments in low-speed settings. This work investigates SAVs’ potential for U.S. urban areas via multiple applications across the Austin, Texas, network. This work describes advances to existing agent- and network-based SAV simulations by enabling dynamic ride-sharing (DRS, which pools multiple travelers with similar origins, destinations and departure times in the same vehicle), optimizing fleet sizing, and anticipating profitability for operators in settings with no speed limitations on the vehicles and at adoption levels below 10 % of all personal trip-making in the region. Results suggest that DRS reduces average service times (wait times plus in-vehicle travel times) and travel costs for SAV users, even after accounting for extra passenger pick-ups, drop-offs and non-direct routings. While the base-case scenario (serving 56,324 person-trips per day, on average) suggest that a fleet of SAVs allowing for DRS may result in vehicle-miles traveled (VMT) that exceed person-trip miles demanded (due to anticipatory relocations of empty vehicles, between trip calls), it is possible to reduce overall VMT as trip-making intensity (SAV membership) rises and/or DRS users become more flexible in their trip timing and routing. Indeed, DRS appears critical to avoiding new congestion problems, since VMT may increase by over 8 % without any ride-sharing. Finally, these simulation results suggest that a private fleet operator paying $70,000 per new SAV could earn a 19 % annual (long-term) return on investment while offering SAV services at $1.00 per mile for a non-shared trip (which is less than a third of Austin’s average taxi cab fare).

Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas

Shared autonomous (fully-automated) vehicles (SAVs) represent an emerging transportation mode for driverless and on-demand transport. Early actors include Google and Europe’s CityMobil2, who are seeking early pilot deployments in low-speed settings. This work seeks to understand SAVs’ potential for U.S. urban areas via multiple applications across the Austin, Texas, network. This work describes advances to existing agent- and network-based SAV simulations by enabling dynamic ride-sharing (DRS, to pool multiple travelers with similar origins, destinations and departure times in the same vehicle), optimizing fleet sizing, and anticipating profitability for operators in settings with no speed limitations on the vehicles and at adoption levels below 10 percent of all personal trip-making in the region. Results suggest that DRS reduces total service times (wait times plus in-vehicle travel times) and travel costs for SAV users, even after accounting for extra passenger pick-ups, drop-offs and non-direct routings. While the base-case scenario (serving 56,324 person-trips per day, on average) showed that a fleet of SAVs allowing for DRS may result in vehicle-miles traveled that exceed person-trip miles demanded (due to anticipatory relocations of empty vehicles, between trip calls), it is possible to reduce overall VMT as trip-making intensity (SAV membership) rises and/or DRS users become more flexible in their trip timing and routing. Finally, these simulation results suggest that a private fleet operator paying $70,000 per new SAV could earn a 19% annual (long-term) return on investment while offering SAV services at $1.00 per mile of a non-shared trip (which is less than a third of Austin’s average taxi cab fares).

Dynamic Ride-Sharing and Optimal Fleet Sizing for a System of Shared Autonomous Vehicles

Motorcycles are an important form of personal transportation used by many Americans. They provide an enjoyable recreational opportunity for many and a convenient, functional mode of transportation for others. However, U.S. motorcycle crash rates are 68% higher (per vehicle-mile traveled [VMT]) than those for other vehicles, and their fatal crash rates are more than 28 times higher. This investigation examines the riding and crash experiences and safety perceptions and recommendations of 238 U.S. motorcyclists. Top rider recommendations to other motorcyclists are to wear a helmet, to avoid riding under the influence (of alcohol or drugs), and to obtain motorcycle training. Ordered probit model results for helmet-use prediction suggest that those who ride on a daily or weekly basis and have received formal motorcycle training are more likely to wear a helmet. Other results show that rider training is most common among those who wear a helmet, ride on a weekly or monthly basis, and ride to and from work, sch...

Motorcycle Use in the United States: Crash Experiences, Safety Perspectives, and Countermeasures

Dr. Kara Kockelman (Research Supervisor) with Dr. Stephen Boyles, Paul Avery, Dr. Christian Claudel, Lisa Loftus-Otway, Dr. Daniel Fagnant, Prateek Bansal, Michael W. Levin, Dr. Yong Zhao, Dr. Jun Liu, Lewis Clements, Wendy Wagner, Dr. Duncan Stewart, Dr. Guni Sharon, Dr. Michael Albert, Dr. Peter Stone, Josiah Hanna, Rahul Patel, Hagen Fritz, Tejas Choudhary, Tianxin Li, Aqshems Nichols, Kapil Sharma, and Michele Simoni

https://rosap.ntl.bts.gov/view/dot/36427/dot_36427_DS1.pdf

Bringing Smart Transport to Texans: Ensuring the Benefits of a Connected and Autonomous Transport System in Texas

6 2 1 2 Abstract 3 In recent years vehicle automation technology has experienced rapid gains. Yet little research has been 4 conducted on self-driving vehicles' potential impacts on long-distance personal travel, which represents a 5 major area of travel growth in the United States. Automated vehicles (AVs) offer flexible trip time and 6 origin-destination pairings at lower (perceived) travel tie costs; thus, they have the potential to 7 dramatically change how travelers pursue long-distance tours. 8 By analyzing travel surveys and then developing a statewide simulation experiment of long-distance 9 travel, this paper anticipates AVs' impacts on long-distance travel choices. The research explores 10 Michigan State's 2009Long-Distance Travel Survey and estimates a long-distance trip generation model 11 and modal-agnostic long-distance mode-choice model. These models were applied in a statewide 12 simulation experiment in which AVs are introduced as a new mode with lower perceived travel time costs 13 (via lowered values of travel time en route) and higher travel costs, (to reflect the initially high price of 14 complete vehicle automation). This experiment highlights the potential shifts in mode choices across 15 different trip distances and purposes. Specifically, for travel under 500 miles, AVs tend to draw from 16 personal vehicles use and airlines equally. Airlines are estimated to remain preferred for distances over 17 500 miles (e.g. 43.6% of trips greater than 500 miles were by air, and 70.9% of trips greater than 1000 18 miles were by air). Additionally, at certain AV travel time valuations, travel cost is not a significant 19 factor. As the perceived travel time benefits from hands-free travel rise, monetary costs become less 20 important. Introduction 1 In recent years the technological state of vehicle automation has been experiencing rapid gains, and 2 multiple auto manufacturers and technology providers appear set to introduce a self-driving vehicle by or 3 before 2020 (Bierstadt et al. 2014). These developments will have profound impacts across the 4 transportation system, for roadway safety, urban congestion, environmental implications, freight delivery, 5 personal mobility, and travel behavior in general. One area of impact where little research has been 6 conducted to date is on the potential impacts of vehicle automation on long-distance personal travel, 7 which represents one of the largest areas of travel growth in the United States (US Travel Association 8 2015). Long-distance travel decisions, including the number of annual trips, timing of trips, destinations, 9 durations, …

/pdf/long-distance-travel-mode-shifts-due-to-automated-vehicles-a-139ox9ex7o.pdf

Daniel J Fagnant

Papers

Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas

Dynamic Ride-Sharing and Optimal Fleet Sizing for a System of Shared Autonomous Vehicles

Motorcycle Use in the United States: Crash Experiences, Safety Perspectives, and Countermeasures

Bringing Smart Transport to Texans: Ensuring the Benefits of a Connected and Autonomous Transport System in Texas

Long-Distance Travel Mode-Shifts Due to Automated Vehicles: A Statewide Mode-Shift Simulation Experiment and Travel Survey Analysis