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Assimilating Self-Driving Cars Into Society: Travel Impacts & Transport Policy Choices

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This presentation will discuss the difficult regulatory issues surrounding introducing self-driving cars onto our roadways. Many novel questions will be discussed, such as how to allocate liability for an autonomous vehicle, the self-driving cars’ impact on public transportation and infrastructure, and personal privacy concerns.

Published in: Technology

Assimilating Self-Driving Cars Into Society: Travel Impacts & Transport Policy Choices

  1. 1. ASSIMILATING SELF‐DRIVING CARS INTO SOCIETY: Travel Impacts & Transport Policy Choices Panelists Kara Kockelman & Lisa Loftus‐Otway, UT Austin
  2. 2. Self-driving cars are game changers.  Revolution or Evolution....  Where do you think we are?  A lot can happen in 13 years…
  3. 3. 1900: 5th Ave NYC Easter Parade Spot  the car!
  4. 4. 1913: 5th Ave NYC Easter Parade Source: George Grantham Bain Collection Spot the  horse!
  5. 5. Introduction “You can count on one hand the number of years it will  take before ordinary people can experience (AVs).” –Sergey Brin, at the 2012 signing of California’s SB 1298.
  6. 6. Opportunities for CAVs  U.S. Safety  In 2014, 6.0 million crashes in the U.S. resulting in 32,675 deaths &  >$500+ billion in comprehensive costs.  Driver error is primary cause of > 90% of U.S. crashes.  40% of fatal crashes involve alcohol, drugs, fatigue &/or distraction.   AVs can dramatically impact safety by reducing human errors.  U.S. Congestion  7 billion hours of delay & $160 billion  losses in 2014.  Reductions possible via traffic smoothing, tighter headways,  cooperative adaptive cruise control (CACC) & fewer crashes.
  7. 7. Opportunities (2)  Traveler Behaviors  Car‐sharing & ride‐sharing  Increased mobility for  elderly, disabled, & children?  Parking benefits  Latent & induced VMT  Freight Movement  Reduced labor & thus shipping costs  Improved fuel economies from tight‐headway drafting 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% Midnight 3:00 AM 6:00 AM 9:00 AM Noon 3:00 PM 6:00 PM 9:00 PM Midnight Vehs ≤ 10 yrs Vehs ≤ 15 yrs All vehs
  8. 8. Part 1 What Are AVs Worth? - to Society & to Individual Owners
  9. 9. Annual Per-AV Economic Impacts Assumed Market Share 10% 50% 90% Crashes Lives Saved 1,100 9,600 21,700 Economic Savings per AV per year $430 $770 $960 Comprehen. Savings per AV per yr $1,390 $2,480 $3,100 Congestion Travel Time Savings (M Hours) 756 1,680 2,770 Fuel Savings (M Gallons) 102 224 724 Savings per AV per year $1,320  $590  $550  Other Impacts Parking Savings per AV per year $250  $250  $250  VMT Increase 2.0% 7.5% 9.0% Change in Total # Vehicles ‐4.7% ‐23.7% ‐42.6% (U.S. Context, $2012)
  10. 10. Totaled Social Benefits Assumed Market Share 10% 50% 90% Annual U.S. Savings: Economic Benefits Only $26 B $102 B $201 B Annual U.S. Savings: Comprehensive Benefits $38 B $211 B $447 B Savings Per AV per year: Econ. Benefits Only $2,000  $1,610  $1,760  Savings Per AV per year: Comprehen. Benefits $2,960  $3,320  $3,900  Net Present Value (NPV) of AV Benefits minus  Purchase Price (Econ. Benefits Only) $5,200 $7,250 $10,400 Net Present Value of AV Benefits minus Purchase  Price (Comprehensive Benefits) $12,500 $20,300  per AV sold $26,700 Added Purchase Price $10,000 $5,000 $3,000
  11. 11. U.S. Industry Impacts, at 100% Adoption Industry Industry Size ($B/yr) Industry Impact ($B/yr) % Change in Industry $ per Capita per Year Insurance $180B/yr $108B/yr 60% $339/person/yr Freight Transportation $604 $100 17% $313 Land Development $931 $45 5% $142 Automotive $570 $42 7% $132 Personal Transport $86 $27 31% $83 Electronics & Software Technology $203 $26 13% $83 Auto Repair $58 $21 36% $66 Digital Media $42 $14 33% $44 Medical $2,700 $12 0% $36 Oil and Gas $284 $10 4% $31 Construction/Infrastructure $169 $8 4% $24 Traffic Police $10 $5 50% $16 Law $277 $3 1% $10 Industry-based Totals $6,113 $420B/year 7% $1,318/person/year
  12. 12. Adding in Additional Effects: Travel Time “Productivity” Rises + Pain & Suffering from Crashes Fall Economy-Wide (non-Industry-based) Effects Economic Impact ($B/yr) $ per Capita per Year Productivity en Route $645 Billion/year $2,022/person-year Pain & Suffering + other Crash Costs $488 B/year $1,530 Additional Effects $1,133 B/year $3,552/person-year Overall Totals (industry + other) $1.4 Trillion per year! $4,419 per person-year
  13. 13. Part 2 Forecasting Americans’ Long-Term Adoption of Connected & Autonomous Vehicle (C/AV) Technologies
  14. 14. Willingness to Pay (WTP) Average  WTP Average WTP (if WTP > 0) % of Respondents  with $0 WTP Electronic Stability Control $52 $79 33.4% Lane Centering $205 $352 41.7% Left Turn Assist $119 $221 46.1% Cross Traffic Sensor $169 $252 32.8% Adaptive Headlight  $203 $345 41.1% Pedestrian Detect  $145 $232 37.5% Adaptive Cruise Control $126 $202 37.7% Blind Spot Monitoring $160 $210 23.7% Traffic Sign Recognition $93 $204 54.4% Emergency Automatic Braking  $183 $257 28.7% Level 3 Automation $2,438 $5,470 55.4% Self‐parking Valet System $436 $902 51.7% Level 4 Automation $5,857 $14,196 58.7% Connectivity (DSRC) $67 $111 39.1%
  15. 15. Simulating Fleet Evolution Vehicle inventory Demographics Travel Patterns Technology evolution Transaction decision model (multinomial logit) Add technologies to old vehicles Sell a vehicle and buy vehicles Buy vehicles Sell a vehicle Add connectivity if WTP≥ Price Buy new or used? (Logit) LV4 WTP ≥ Price LV3 WTP ≥ Price Dispose of the oldest vehicle Add connectivity if WTP≥ Price Vehicle is already LV3 or LV4 End: Do nothing End: Dispose of the oldest vehicle New Used End: Add LV4 End: Add LV1, LV2, or self- parking valet if WTP≥ Price No End: Add LV3 No No Yes Yes Yes Same process for each household, every year.
  16. 16. Predicted Shares of US Light-duty Vehicles 59.5% 83.5% 100.0% 100.0% 100.0% 100.0% 100.0% 0.0% 100.0% 0% 25% 50% 75% 100% 2015 2020 2025 2030 2035 2040 2045 DSRC‐based Connectivity 43.0% 43.8% 24.8% 43.4% 43.2% 70.7% 59.7% 0.0% 87.2% 0% 25% 50% 75% 100% 2015 2020 2025 2030 2035 2040 2045 Level 4 Automation
  17. 17. Part 3 Agent-Based Models for Shared AVs + = &  Less than 20% of newer (& 15% of all) personal vehicles are in‐use at  peak times, even with 5‐minute pickup & drop‐off buffers.  Car‐sharing programs like ZipCar &  Car2go have expanded quickly,  with the number of U.S. users doubling every  year or two, over the  past decade.  Shared Autonomous Vehicles (SAVs) can help overcome car‐sharing  barriers, like return‐trip certainty &  vehicle access distances.
  18. 18. Agent-Based Model Framework  Grid‐based 10 mi x 10 mi urban area with 0.25‐sq. mile zones.  Trip generation:  Poisson‐based PK & OP counts for trip generation, every 5 minutes.  Higher trip production & attraction rates closer to city center.  Mostly round‐trip travel, with 78% travelers returning via SAVs.  Random departure times & trip distances (2009 NHTS).  SAVs travel at fixed speeds, with 5 min. intervals. 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 Midnight ‐ 3 AM 3 AM ‐ 6 AM 6 AM ‐ 9 AM 9 AM ‐ Noon Noon ‐ 3 PM 3 PM ‐ 6 PM 6 PM ‐ 9 PM 9 PM ‐ Midnight Trip Generation Trip Distances (mi.)Dwell Times (hrs.)
  19. 19. Example: One SAV’s 24-hour Journey Higher AM Trip  Attraction Higher PM Trip  Attraction • Red Arrows SAV Relocation • Blue Arrows Serving Riders 5 mi 10 mi SE
  20. 20. Case Study Results  100 days were simulated to assess SAV travel implications. Parameter Value Service area 10 mi. x 10 mi. Outer trip generation rate 9 trips/cell/day CBD edge trip generation rate 27 trips/cell/day CBD core trip generation rate 30 trips/cell/day Off‐peak speed 33 mph Peak speed 21 mph AM peak 7 AM ‐ 8 AM PM peak 4 PM ‐ 6:30 PM Trip share returning by SAV 78% Scenario Results  Each SAV replaced 9 to 13  conventional vehicles.  Avg. wait time ≈ 2.8 min.  11% new/induced (empty‐ vehicle) travel.  Yet 5% to 50% (GHG vs. VOCs)  life‐cycle emissions reductions,  thanks to smaller vehicles, fewer cold starts, & less  parking infrastructure!
  21. 21. Part 4 What if SAVs Serve Central Austin, & Offer Dynamic Ride-Sharing (DRS)?
  22. 22. One SAV’s 24-hour day Dropoff Pickup Travel
  23. 23. Case Study Results  1:10 & 1:8 veh. replacement  rates (with & w/o DRS)  System pays for itself with  just $1/mile fares!  Electric vehicles (Leaf &  Model S) also tested (with  inductive charging), using  100 mi x 100 mi region.  DRS saves more emissions ‐ & VMT even falls (vs. BAU). Measure With DRS Without DRS SAV fleet size 1,855 2,181 Veh. replacement rate 9.95 8.47 Average wait time 57 sec 47 sec % Waiting > 10 min. 0.60% 0.33% 5‐6 PM avg. wait 3.0 min 2.4 min Avg. total trip time 14.4 min 13.8 min New VMT introduced 4.90% 7.92% # rides shared 5,754 0 % VMT shared 4.50% 0%  24‐hour days simulated with 56,300 to 270,000 trips served.  Excellent Level of Service (typ. wait time < 3 min.)
  24. 24. Part 5 What if SAVs Serve the Entire Region? And Are SAElectricVs?
  25. 25. Shared Autonomous Electric Vehicles SAEVs or
  26. 26. Station Generation via 30-day Initial Run Check for  unmet  requests Find closest  SAEV SAEV has  range to meet  trip request? Unmet  request Send SAEV to  serve trip Yes t=t+1sec Next  timestep:  t=t+1sec No new  requests None  available Is a charging  station also in  range? No Create new  station at  vehicle’s  location No SAEV begins  charging SAEV heads  to closest  station SAEV removed  from  consideration Try again, in  next timestep Yes
  27. 27. Charging Station Locations  Charging stations generated based on demand.   Number of charging stations formed is dependent only  on vehicle range.   Stations  formed for  200‐mile  range (left)  & 60‐mile  range  (right) 
  28. 28. Central Austin Station Locations Assuming 60 mile range + 4 hour charge time + 5:1 travelers per  SAEV  (= 28 stations over 6 x 10 mi area)
  29. 29. Austin SAEV Results Scenario Gas  SAV Short‐ Range  SAEV Long‐Range  Fast Charge Long‐ Range  SAEV Short‐Range  but Fast  Charge Long‐Range, Fast Charge,  Smaller Fleet Range (mi) 525 60 200 200 60 200 Recharge/Refuel Time (min) 2 240  30 240 30 30 # of Charging/Gas Stations 19 155 155 155 155 155 Fleet Size (# vehicles) 5,893 5,893 5,893 5,893 5,893 4,124 Avg. Daily miles per Vehicle 452 201 354 441 355 501 % of Unserved Trips 1.62 60.6 19.4 2.67 16.2 15.2 Avg. Daily Trips per Vehicle 28.5 11.4 23.4 28.2 24.3 35.1 Avg.  Wait Time Per Trip (min) 4.45 9.82 8.76 5.49 6.16 9.55 % Unoccupied Travel 6.05 13.1 7.88 6.86 14.2 8.62 % Travel for Charging 0.65 5.59 1.26 1.05 5.34 1.27 • Fleet size is key to lower response times. Tripling fleet size (from 9:1 to 3:1  travelers per SAEV) lowers average response times by >75%. • Longer charge times increase response times (& unserved trips rise 19% to 61%) • Longer ranges lower empty VMT, but fast‐charging improves response times. • Trips in Austin’s urban core are served best (e.g., never exceed 30‐min wait times).
  30. 30. SAEV Cost Assumptions • Conventional BEV Costs: $25,000 (short range) to $35,000 (long‐range) • Self‐driving Technology Cost: $5,000 to $25,000 per vehicle • Battery Replacement: $100 ‐ $190 per kWh (once per vehicle life) • Vehicle Maintenance: 5.4¢ to ‐6.6¢ per mile • Insurance & Registration: $550 ‐ $2,200 per vehicle‐year • Electricity: 8¢ to 20¢ per kWh • Level II Chargers: $8,000 ‐ $18,000 each • Level II Charger Maintenance: $25 ‐ $50 per year, per charger • Fast (Level III) Charger: $10,000 ‐ $100,000 per charger • Fast Charger Maintenance: $1,000 ‐ $2,000 per year, per charger • Station Properties: $1,980 to $6,900 per vehicle space (based on location)
  31. 31. Financial Results: Costs per Mile Mid‐Range Expected Costs per mile Gasoline  SAV Short‐ Range  SAEV Long‐ Range  SAEV Fast‐ Charge,  Long‐ Range SAEV Fast‐ Charge  SAEV Fast‐Charge,  Long‐Range  Reduced  Fleet Electricity/Fuel 6.39¢/mi 4.51 4.26 4.21 4.57 4.29 Vehicle Maint.,  Admin + Attendants 18.4¢/mi 19.7 18.6 18.4 19.9 18.7 Charger Costs (Land  + Infrastructure) n/a 3.57 1.35 2.15 6.30 0.76 Vehicle Purchase  19.6¢/mi 27.7 29.4 28.3 25.3 28.4 Battery Costs n/a 1.58 4.91 4.85 1.60 4.95 Total Costs per Mile 45¢/mi 59¢/mi 59¢/mi 59¢/mi 59¢/mi 59¢/mi Daily Vehicle Profit  ($1/mile fare) $234 /veh‐day $72 $132 $170 $126 $187 #Trips/vehicle‐day 28 trips /veh‐day 11 23 28 24 35 Response time/trip 4.4 min 9.8 8.8 5.5 6.2 9.6
  32. 32. Part 6 How Should We Modify our Travel Demand Models & Plan for the Future?
  33. 33. More Complete Model Assumptions  Vehicle ownership changes over time (AVs cost more & SAVs  allow people to avoid ownership).  Travel times are less burdensome (for drivers)  Lower values of travel time thanks to more productive (& restful!) in‐ vehicle activities  Affect trip mode choice probabilities  Travel costs may fall  AVs can head to lower‐cost parking locations  Shared AVs reduce overall vehicle‐use costs  Dynamic ride‐sharing reduces per‐trip costs even further  Link capacities rise on roadways  V2V communications (e.g., CACC) + smart intersections (long term)  AVs may eventually follow at shorter headways & distances  Hopefully lower travel times & travel‐time unreliability…
  34. 34. However, we also expect…  Longer travel distances (more distant destinations).  More trip‐making by those presently unlicensed, with  disabilities &/or other difficulties driving.  Less air travel by passengers & rail travel by freight.  Possibly larger, less‐efficient vehicles, for longer‐distance  trips, & more land use sprawl. This means…  Rising congestion & infrastructure damage in many locations.  Need for smarter system management, including incentives  for ride‐sharing & non‐motorized travel, route guidance,  credit‐based congestion pricing & micro‐tolling ‐ to  internalize externalities & operate more efficiently, equitably,  & sustainably!
  35. 35. In Conclusion…  CAVs offer tremendous benefits for mobility, safety & parking,  but will add VMT & congestion.  SAVs offer a new & exciting (transit?) mode, with each SAV  replacing ~8 personal vehicles, for same level of motorized trip‐ making.  SAVs add 7‐10% extra VMT (though DRS may reduce VMT).  Yet SAVs may bring useful travel‐cost savings, emissions benefits + profits for transit providers.  Traditional travel models cannot capture the details of SAV  systems & CAV operations. Microsimulation is needed.  Smart system management practices are also needed, to avoid  gridlock, sprawl, greater energy use, & other downsides.
  36. 36. So, what is going on in ‘the law’  around the world?
  37. 37. United States: Federal • National Highway Traffic Safety Administration (NHTSA)  preliminary policy on Automated Vehicles in 2013 – Outlined definitions for Levels 0 through 4 of automation • NHTSA in September 2016 issued new Policy on Autonomous  Vehicles  – Adopted SAE J3016 definitions (L0 through Level 5) as  their standard. • Deliberately issued as policy & not regulations, with goal to  set stage for consistent national framework but providing  flexibility to states.
  38. 38. NHTSA 2016 Policy Delineated roles/responsibilities for state(s) policy: ‐ States retain their traditional responsibilities for vehicle  licensing, registration, traffic laws & enforcement, and motor  vehicle insurance & liability. ‐ NHTSA continued preemption for interpretations,  exemptions, notice, and rulemaking & enforcement  authority. Manufacturer responsibility to determine their system  conforms with SAE J3016.
  39. 39. NHTSA’s Framework for Vehicle Performance Guidance
  40. 40. U.S.: Federal (2) • NHTSA October 2016: Policy on Cyber Security in  Autonomous Vehicles – Covers all vehicles not just HAVs & applies to designers,  supplies, manufactures & modifiers • FAST Act 2015, §24302 limitations on data retrieved from  Event Data Recorders (EDRs) – NHTSA required to determine amount of time EDRs  should capture & record data for retrieval ... to provide  sufficient information to investigate a motor vehicle  crash
  41. 41. U.S.: State‐level • Over 80 bills are currently in front of U.S. state legislatures on  this topic. • Nevada created legislation allowing testing in 2011. • California legislation authorized a pilot program in 2014. • Michigan: 2013 allowed testing of automated vehicles as long  as human was in car. • Michigan: 2016 allows driverless cars to be driven for any of the  following purposes, no human required to be in car: – Personal use; road testing; as part of a SAVE program or “on‐ demand automated vehicle network;” & as part of a platoon.
  42. 42. California’s & Michigan’s approaches differ...  California: prescriptive approach  Required rulemaking by agencies, pilot tests must be  authorized, test vehicles do not require driver behind  wheel, but qualified test drivers must have ability to take  control, minimum insurance surety bond of $5 million.  Michigan: framework approach  Initially, Automakers can test AVs as long as human in car  in original legislation.  No agency rulemaking required.  Current legislation, allows driverless cars to be driven for  multiple  purposes not just road testing, without a human  in the vehicle as the AI is considered the driver. 
  43. 43. Stats from California’s pilot..... • 17 testing permits (mainly Tier 1’s, OEMS & technology  manufacturers) • 26 traffic accidents involving HAVs (Google 22, Delphi 1, Cruise  1, GMCruise 1, Nissan 1) • Reported disengagements from automated mode between Dec  1, 2015 & Nov 30, 2016: – Bosch – 1442 – 982 miles driven(MD) – BMW – 1 – 638 MD – Delphi – 178 – 3125 MD – Ford – 3 – 590 MD  – Google – 465 – 1,060,199 MD – GM Cruise – 284  – 9970 MD – Nissan – 28 – 4,099 MD – Mercedes – 336 – 673 MD – Tesla – 182 – 530 MD https://www.dmv.ca.gov/portal/dmv/detail/vr/autonomous/autonomousveh_ol316 https://www.dmv.ca.gov/portal/dmv/detail/vr/autonomous/disengagement_report_2016
  44. 44. European Union EU has not yet passed any legislation specifically on HAVs.   EU Directive 2007/46 EEC regulates how vehicles should be designed  & operated.   EU Roadworthiness Directive 2014/45 EU sets out basis for  roadworthiness.  Problems with the Vienna Convention on Road  Traffic Article 8’s  language stating ‘every driver shall at all times be able to control his  vehicle’ incidentally prevented the development & testing of HAVs.  Amendments to the Convention in 2016 allow drivers to take hands  off wheel in self‐driving cars. ECE Regulation 79 also creates an impediment through requirements  for specific steering configurations:  advanced driver steering system is only allowed to control steering as  long as the driver remains in primary control of vehicle at all times. 
  45. 45. EU Member States  Finland, France, Germany, Netherlands, Sweden & UK  implemented legislation in 2015/2016   Finland, Netherlands & Sweden, all have similar  systems to  California’s legislation & regulations for  pilot tests, & for driver licensing.   UK issued code of  practice in July 2015, which must be  followed by any groups conducting HAV testing. This  includes licensing & training provisions, & a risk  management process by the testing group.    UK issued Vehicle Technology and Aviation Bill 2016‐ 2017 in February 2017 – outlines liability for insurers of  automated vehicles.
  46. 46. Activities in Canada, Japan & Australia  Canada has not yet created federal regulation  Province of Ontario in 2016 produced legislation &  regulations for a pilot program  Japan has allowed road testing, & is working to  develop regulations  Australia has not federally legislated  National Transport Commission has set out  recommendations & policy positions in 2016  Government of New South Wales introduced legislation in  September 2015 for road testing.  NSW released a future  transport roadmap in fall 2016 that outlined the ministry’s  view on the transition to HAVs
  47. 47. Initial Legal Issues  Privacy, Liability, Cybersecurity & Freedom of  Information Requests / State Open Records  Requests all raised as concerns for automated &  connected vehicles.  No case law yet on these issues.  NHTSA & FTC have noted they are reviewing  hacking & privacy of consumer data in HAVs.    Federal statutes also provide penalties under the  Computer Fraud and Abuse Act, Digital Millennium  Copyright Act, Wiretap Act, & Patriot Act. 
  48. 48. Initial legal issues (2)  Privacy realm three areas have been identified  as needing changes to law: 1. Autonomy privacy (i.e. an individual’s privacy  under 4th amendment to the U.S. Constitution  e.g. illegal search & seizure);  2. Personal information privacy, and  3. Surveillance. • California passed law regarding consumer  privacy in HAVs
  49. 49. Initial Conclusions  Many jurisdictions around the world have  begun to draft legislation & regulations with  a primary focus on pilot testing.   California has begun to review how it needs to  amend its laws & regulations.   Legal articles have primarily focused on  privacy, liability, cyber security &  constitutional protections. 
  50. 50. Thank you! Questions & Suggestions? kkockelm@mail.utexas.edu loftusotway@mail.utexas.edu

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