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Impacts on Roads from Automated Driving Systems (ADS)
(May 19, 2022)

Framework for automated driving system impact assessment
Presenter: Scott B. Smith, Ph.D.
Presenter’s Org: Volpe Center, USDOT

T3 webinars are brought to you by the Intelligent Transportation Systems (ITS) Professional Capacity Building (PCB) Program of the U.S. Department of Transportation (USDOT)’s ITS Joint Program Office (JPO). References in this webinar to any specific commercial products, processes, or services, or the use of any trade, firm, or corporation name is for the information and convenience of the public, and does not constitute endorsement, recommendation, or favoring by the USDOT.

[The slides in this presentation contain the USDOT Volpe Center logo.]

Slide 1: Framework for automated driving system impact assessment

T3 Webinar, 19 May 2022
Scott B. Smith, Ph.D., Volpe Center, USDOT

Slide 2: Automated driving system impacts

“Big picture” of automation impacts
Direct and Indirect
A framework breaks a complex problem into (somewhat) manageable pieces

[This slide contains a graphic of labeled, interconnected ovals: Safety, Vehicle Operations, Personal Mobility, Energy/Emissions, Network Efficiency, Travel Behavior, Public Health, Land Use, and Socio-Economic Impacts. The y-axis is labeled Spatial Resolution and has five sections: Person/Vehicle, Street, Corridor, Region, and Nation. The x axis has three labeled sections: Seconds, Time Frame, and Years. Source: USDOT Benefits Estimation Model report and poster from 2017 Automated Vehicles Symposium.]

Slide 3: Automated driving system impacts

Impact Area	Potential Benefit	Potential Dis-Benefit
Safety	Reduction in crashes	New types of crashes
Vehicle Operations	More precise vehicle following and lane keeping	Longer following distances
Personal Mobility	More options, especially for those unable/unwilling to drive. Potentially cheaper	*Can everyone* access the automated vehicles?**
Energy Use and Emissions	Smoother speed profiles, platooning, light-weighting could improve efficiency	Increases in VMT could increase fuel use/pollution
Network Efficiency	May increase throughput	May increase congestion, via increased trips
Public Health	Improved access to medical care, work and recreation for non-motorists	May reduce use of active modes
Travel Behavior and Vehicle Ownership	May decrease need for ownership, potentially reducing fleet size	May lead to more trips, with ability to safely multi-task enroute
Land Use	May encourage density by freeing up space currently devoted to parking	May encourage sprawl

Slide 4: Safety - a significant potential improvement

Automated vehicles that accurately detect, recognize, anticipate, and respond to the movements of all transportation system users could lead to breakthrough gains in transportation safety - Automated Vehicles 3.0
Most motor vehicle crashes involve driver related factors
Some evidence of benefit from currently available crash avoidance technologies
But, while automation may reduce many human-caused crashes, it may contribute to other crashes
Challenges in safety analysis
- Need comparable operational design domains for automation and baseline
- How safe is “safe enough?”
- Crashes are rare events

[This slide contains a photograph of a damaged, overturned car. Source: USDOT Volpe.]

Slide 5: Vehicle operations - unclear effect on road capacity

Automated systems can affect road and intersection capacity
- Following distance
- Gap acceptance
- Required lane width
Simulation studies and field tests suggest different following distances for manual driving, adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC)
(See Eilbert, Berg and Smith (2019) poster and report)
What happens in different operational design domains?

[This slide contains a line graph plotting Capacity versus CAV MPR (%). Four lines are plotted: Aggressive, Normal, Conservative, and ACC. Source: Adebisi, Adekunle, et al. 2020. “Developing Highway Capacity Manual Capacity Adjustment Factors for Connected and Automated Traffic on Freeway Segments.”.]

Slide 6: Energy/emissions - complex impacts

Vehicle fuel consumption per mile
- Vehicle/powertrain resizing
- Smoother traffic flow
- Faster travel may increase fuel use
- Power load of automation hardware and software
Vehicle-miles traveled
- Increased travel
- Shared or not shared
- Zero-occupant vehicles (ZOV)
Self-repositioning of AVs can facilitate electric vehicle use

[This slide contains a graphic of complex impacts, both positive and negative effects, on energy and emissions.]

Slide 7: Personal mobility and accessibility

“Automation has the potential to improve our quality of life and enhance the mobility and independence of millions of Americans, especially older Americans and people with disabilities.” - Automated Vehicles 3.0

Personal mobility for non-motorists
First/last mile services for transit
“Complete Trip” to enable independent and spontaneous travel for those who are underserved now
- Planning the trip
- Getting to the vehicle
- Using the vehicle
- Getting to the final destination

[This slide contains a picture of two people in an automated vehicle which resembles a driverless bus. Source: USDOT.]

Slide 8: Indirect impacts - network efficiency

Primarily from changes in vehicle operations
- Car following
- Intersection
- Needed lane widths
Are vehicles also connected?
Interactions with other road users
- Non-automated vehicles
- Bicyclists, pedestrians

[This slide contains two images: (1) a photo of traffic on a four-lane highway with vehicles traveling in a marked Eco-Lane. Those vehicles have circular lines drawn around them to signify that they are connected vehicles. Source: Thinkstock, and (2) a map marked to show intersection locations. Source: USDOT Volpe.]

Slide 9: Indirect impacts - travel behavior

Personal travel

Privately owned automated vehicles (POV)
- Long trips become less onerous
Shared automated vehicles
- Self-delivery makes this option more convenient than car-sharing today
- Replaces some POVs?
- Provides improved accessibility for non-motorists
- Zero-occupant trips
- Vehicle occupancy: one to many
Freight
- Possibility of new business models, enabled by automation

[This slide contains a graphic of four boxes: Technology is at the top, then Future use of ADS below which splits into Policy and User Attitudes. Source: USDOT Volpe.]

Slide 10: Indirect impacts - public health

Public health is the science of protecting and improving the health of families and communities through promotion of healthy lifestyles, research for disease and injury prevention and detection and control of infectious diseases. - CDC Foundation

Elements of automation that affect public health

Safety: vehicle occupants and non-occupants
Effect of automation on active transportation
Air pollution
Access to opportunities for healthy lifestyles

[This slide contains a photo of a crosswalk in the front of the USDOT Volpe Center. Source: USDOT Volpe.]

Slide 11: Indirect impacts - land use

Parking
- Privately owned vehicles are parked most of the time
- Less need for parking for shared vehicles, because of their higher utilization
- Option of self-repositioning to remote parking?
Sprawl
- Ability to engage in other tasks enroute may make long commutes more attractive

[This slide contains a photo of a parking lot, filled with vehicles, which is surrounded by a body of water. Source: Wikimedia commons.]

Slide 12: Questions?

Scott Smith, Ph.D., Operations Research Analyst, 617-494-2588, scott.smith@dot.gov
www.volpe.dot.gov

[This slide contains a graphic showing USDOT Volpe Center’s Purpose (Advancing transportation innovation for the public good) and Core Values: Public Service, Innovative Solutions, Collaboration and Partnering, Professional Excellence, and Employee Well-Being.]

Slide 13: Selected references (1/2)

Adebisi, Adekunle, Yan Liu, Bastian Schroeder, Jiaqi Ma, Burak Cesme, Anxi Jia, and Abby Morgan. 2020. “Developing Highway Capacity Manual Capacity Adjustment Factors for Connected and Automated Traffic on Freeway Segments.” Transportation Research Record: Journal of the Transportation Research Board 2674 (10): 401–15. https://journals.sagepub.com/doi/10.1177/0361198120934797.
Eilbert, A., Berg, I., Smith, S. (2019), Meta-Analysis of Adaptive Cruise Control Applications: Operational and Environmental Benefits (FHWA-JPO-18-743), https://rosap.ntl.bts.gov/view/dot/41929
Harper, C. D.; Hendrickson, C. T.; Mangones, S.; Samaras, C. (2016) Estimating Potential Increases in Travel with Autonomous Vehicles for the Non-Driving, Elderly and People with Travel-Restrictive Medical Conditions. Transportation Research Part C: Emerging Technologies, 72, 1−9.
Innamaa, S., Smith, S., Barnard, Y., Rainville, L., Rakoff, H.,Horiguchi, R. (2018). Trilateral Impact Assessment Framework for Automation in Road Transportation (p. 42). https://connectedautomateddriving.eu/wp-content/uploads/2018/03/Trilateral_IA_Framework_April2018.pdf
Lee, J., & Kockelman, K. M. (2019). Energy Implications of Self-Driving Vehicles. 98th Annual Meeting of the Transportation Research Board. https://www.caee.utexas.edu/prof/kockelman/public_html/TRB19EnergyAndEmissions.pdf

Slide 14: Selected references (2/2)

Leslie, Andrew J, Raymond J Kiefer, Carol A Flannagan, Susan H Owen, and Brandon A Schoettle. 2022. “Analysis of the Field Effectiveness of General Motors Model Year 2013-2020 Advanced Driver Assistance System Features.” UMTRI-2022-2. https://deepblue.lib.umich.edu/bitstream/handle/2027.42/171916/UMTRI-2022-2.pdf?sequence=1&isAllowed=y.
Smith, S., Koopmann, J., Rakoff, H., Peirce, S., Noel, G., Eilbert, A., & Yanagisawa, M. (2018). Benefits Estimation Model for Automated Vehicle Operations: Phase 2 Final Report (DOT-VNTSC-OSTR-18-01;FHWA-JPO-18-636). https://rosap.ntl.bts.gov/view/dot/34458
Stephens, T. S., Gonder, J., Chen, Y., Lin, Z., Liu, C., & Gohlke, D. (2016). Estimated Bounds and Important Factors for Fuel Use and Consumer Costs of Connected and Automated Vehicles. National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy17osti/67216.pdf
Taiebat, M., Brown, A. L., Safford, H. R., Qu, S., & Xu, M. (2018). A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles. Environmental Science & Technology. https://pubs.acs.org/doi/10.1021/acs.est.8b00127
Wadud, Z., MacKenzie, D., & Leiby, P. (2016). Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles. Transportation Research Part A: Policy and Practice, 86, 1–18. https://www.sciencedirect.com/science/article/pii/S0965856415002694?via%3Dihub

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