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Connected and Automated Vehicles (CAVs): Implications for Travel and Infrastructure Provision

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Ridesharing services are already changing the transportation paradigm. If autonomous vehicles are introduced what other impacts could they have? Is traffic going to get better…or worse? We will cover potential impacts that begin on the roadway and lead to areas that could impact society tremendously. Presented at the 2017 D-STOP Symposium.

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Connected and Automated Vehicles (CAVs): Implications for Travel and Infrastructure Provision

  1. 1. Connected and Automated Vehicles (CAVs): Implications for Travel and Infrastructure Provision Dr. Chandra Bhat Acknowledgements: D-STOP, TxDOT, NCTCOG, Humboldt Award, Dr. Ram Pendyala, Dr. Kostas Goulias, all my graduate/undergraduate students
  2. 2. Source: Disruptive Technologies: Advances that will transform life, Business, and the global economy McKinsey Global Institute May 2013 McKinsey: Autonomous Cars One of 12 Major Technology Disruptors
  3. 3. Automated Vehicles and Transportation Technology Infrastructure Traveler Behavior
  5. 5. Self-Driving Vehicle (e.g., Google) Connected Vehicle AI located within the vehicle AI wirelessly connected to an external communications network “Outward-facing” in that sensors blast outward from the vehicle to collect information without receiving data inward from other sources “Inward-facing” with the vehicle receiving external environment information through wireless connectivity, and operational commands from an external entity AI used to make autonomous decisions on what is best for the individual driver Used in cooperation with other pieces of information to make decisions on what is “best” from a system optimal standpoint AI not shared with other entities beyond the vehicle AI shared across multiple vehicles A more “Capitalistic” set-up A more “Socialistic” set-up Two Types of Technology
  6. 6. Autonomous (Self-driving) Vehicle  Google cars driven 500,000 miles – Release Date Expected 2018
  7. 7. Connected Vehicle Research Addresses suite of technology and applications using wireless communications to provide connectivity  Among vehicle types  Variety of roadway infrastructure
  8. 8. Regular Traffic Conditions PRESENT DAY
  9. 9. Icy Patch PRESENT DAY
  10. 10. Incident PRESENT DAY
  11. 11. Lane blocking, traffic slow down PRESENT DAY
  12. 12. Congestion buildup, late lane changes PRESENT DAY
  13. 13. Congestion propagation to frontage, ramp backed up PRESENT DAY
  14. 14. Regular Traffic Conditions V2V
  15. 15. Icy Patch V2V
  16. 16. Incident: Information propagation V2V
  17. 17. Preemptive lane changing, freeway exit V2V
  18. 18. Re-optimization of signal timing, upstream detours INCIDENT AHEAD TAKE DETOUR V2I
  19. 19. Regular Traffic Conditions AUTONOMOUS
  20. 20. Icy Patch AUTONOMOUS
  21. 21. Avoidance of icy patch, no incident AUTONOMOUS
  22. 22. Traffic slowdown, late lane changing, congestion AUTONOMOUS
  23. 23. Icy Patch AUTONOMOUS + V2X
  24. 24. Avoidance of icy patch, no incident AUTONOMOUS + V2X
  25. 25. Information propagation, preemptive lane changing, freeway exit AUTONOMOUS + V2V
  26. 26. Re-optimization of signal timing, upstream detours INCIDENT AHEAD TAKE DETOUR AUTONOMOUS + V2I
  27. 27. Infrastructure Needs/Planning Driven By…  Complex activity-travel patterns  Growth in long distance travel demand  Limited availability of land to dedicate to infrastructure  Budget/fiscal constraints  Energy and environmental concerns  Information/ communication technologies (ICT) and mobile platform advances Autonomous vehicles leverage technology to increase flow without the need to expand capacity
  28. 28. Technology and Infrastructure Combination Leads To…  Safety enhancement  Virtual elimination of driver error – factor in 80% of crashes  Enhanced vehicle control, positioning, spacing, speed, harmonization  No drowsy, impaired, stressed, or aggressive drivers  Reduced incidents and network disruptions  Offsetting behavior on part of driver
  29. 29.  Capacity enhancement  Platooning reduces headways and improves flow at transitions  Vehicle positioning (lateral control) allows reduced lane widths and utilization of shoulders; accurate mapping critical  Optimized route choice  Energy and environmental benefits  Increased fuel efficiency and reduced pollutant emissions  Clean fuel vehicles  Car-sharing
  31. 31. Impacts on Land-Use Patterns  Live and work farther away  Use travel time productively  Access more desirable and higher paying job  Attend better school/college  Visit destinations farther away  Access more desirable destinations for various activities  Reduced impact of distances and time on activity participation  Influence on developers  Sprawled cities?  Impacts on community/regional planning and urban design
  32. 32. Impacts on Household Vehicle Fleet  Potential to redefine vehicle ownership  No longer own personal vehicles; move toward car sharing enterprise where rental vehicles come to traveler  More efficient vehicle ownership and sharing scheme may reduce the need for additional infrastructure  Reduced demand for parking  Desire to work and be productive in vehicle  More use of personal vehicle for long distance travel  Purchase large multi-purpose vehicle with amenities to work and play in vehicle
  33. 33. Impacts on Mode Choice Automated vehicles combine the advantages of public transportation with that of traditional private vehicles  Catching up on news  Texting friends  Reading novels  Flexibility  Comfort  Convenience What will happen to public transportation? Also automated vehicles may result in lesser walking and bicycling shares Time less of a consideration So, will Cost be the main policy tool to influence behavior?
  34. 34. Impacts on Mode Choice  Driving personal vehicle more convenient and safe  Traditional transit captive market segments now able to use auto (e.g., elderly, disabled)  Reduced reliance/usage of public transit?  However, autonomous vehicles may present an opportunity for public transit and car sharing  Lower cost of operation (driverless) and can cut out low volume routes  More personalized and reliable service - smaller vehicles providing demand-responsive transit service  No parking needed – kiss-and-ride; no vehicles “sitting” around  20-80% of urban land area can be reclaimed  Chaining may not discourage transit use
  35. 35. Impacts on Long Distance Travel  Less incentive to use public transportation?  Should we even be investing in high capital high-speed rail systems?  Individuals can travel and sleep in driverless cars  Individuals may travel mostly in the night  Speed difference?
  36. 36. Mixed Vehicle Operations  Uncertainty in penetration rates of driverless cars  Considerable amount of time of both driverless and traditional car operation  When will we see full adoption of autonomous? Depends on regulatory policies  Need infrastructure planning to support both, with intelligent/dedicated infrastructure for driverless
  37. 37. Concerns about Autonomous Cars  Survey with 1800 individuals in the Puget sound Region Type of concern Not concerned Somewhat unconcerned Neutral/doesn’t know Somewhat concerned Very concerned Equipment and system safety 6.9% 4.4% 22.2% 26.9% 39.6% System and vehicle security 8.4% 5.0% 26.2% 26.8% 33.7% Capability to react to the environment 6.2% 3.2% 18.9% 22.8% 48.9% Performance in poor weather or other unexpected conditions 6.3% 4.3% 21.5% 26.5% 41.4% Legal liability for drivers or owners 6.4% 4.2% 24.3% 27.4% 37.7%
  38. 38. A Behavioral Choice Model of the Use of Car-Sharing and Ride-Sourcing Services Felipe F. Dias, Patrícia Lavieri, Venu M. Garikapati, Sebastian Astroza, Ram M. Pendyala and Chandra R. Bhat
  39. 39. Shared Autonomous Vehicles (SAV) vs. Private Ownership  Private ownership Chauffeuring household members  Shared Autonomus Vehicles (SAV) Acquired by mobility providers (Uber, Lyft, car2go…) Travelers purchasing transportation $/trip $/mile $/minute
  40. 40. Potential Impacts on the Transportation Network and on the Environment High empty- vehicle-miles traveled Cancel any network operation gain due to AV platooning Increased congestion Reduced AV owners’ value of travel time PRIVATELY OWNED AV Increased energy consumption Low empty- vehicle-miles traveled Network operation gain due to AV platooning Low congestion Fares control value of travel time SHARED AV Reduced energy consumption Subsided fares for social inclusion
  41. 41. Policy Implications  Results show:  Individuals with green lifestyle preferences and who are tech-savvy are more likely to adopt car-sharing services, use ride-sourcing services, and embrace autonomous vehicle-sharing in the future.  Younger and more educated urban residents are more likely to be early adopters of autonomous vehicle technologies, favoring a sharing-based service model.  Individuals who currently eschew vehicle ownership, and have already experienced car-sharing or ride-sourcing services, are especially likely to be early adopters of AV sharing services.  Most effective way to move AV adoption toward a sharing model (rather than an ownership model) is to enhance neighborhood densification.  Will new mobility options reduce bicycling, walking, and the use of public transportation (PT) services?
  42. 42. Modeling Implications  Current approach can help forecast autonomous vehicle impacts under alternative future scenarios: can be implemented within an agent-based microsimulation model system  By considering latent (and stochastic) psychological constructs, our approach provides “true” estimates of the effects of current residential and mobility choices on future AV-related choices, but  Travel behavior community: need for a better understanding underlying psychological motivations and preferences  The cursory attention we have paid to such psychological underpinnings in our current modeling approaches will not suffice as we move into a new transportation era of innovative mobility-technology services  Need a better understanding of the individual observed attributes that characterize factors such as being green and tech-savvy  Future research efforts should strive to address the data limitations of this study
  43. 43. A Behavioral Choice Model of the Use of Car-Sharing and Ride-Sourcing Services Felipe F. Dias, Patrícia Lavieri, Venu M. Garikapati, Sebastian Astroza, Ram M. Pendyala and Chandra R. Bhat