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Automated Traffic Control Paradigms: Thinking Beyond Signals

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2015 D-STOP Symposium session by D-STOP researcher Steve Boyles. Watch the presentation at http://youtu.be/cO6qCwhVz8A?t=33m18s

Get symposium details: http://ctr.utexas.edu/research/d-stop/education/annual-symposium/

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Automated Traffic Control Paradigms: Thinking Beyond Signals

  1. 1. Modeling for an Automated Vehicle World Stephen Boyles Assistant Professor Civil, Architectural & Environmental Engineering The University of Texas at Austin March 2, 2015 D-STOP Symposium Austin, TX
  2. 2. What do we mean by “automated”? This talk is about future possibilities of fully-autonomous vehicles which do not require human interaction at all.
  3. 3. Why is everybody talking about AVs? Enormous opportunities for improving mobility, reducing congestion, improving safety, etc.
  4. 4. The technology for AVs is imminent... ...policymaking is the likely bottleneck, not technology.
  5. 5. So, there’s no time like the present to plan. While AVs present great opportunities, it is not clear that these opportunities will be realized. ?
  6. 6. Talk outline: 1. How can we model impacts of AVs on congestion? 2. How can we model impacts of AVs on traveler choices? 3. What are the new opportunities for traffic control? 4. Implications for transportation modeling in the future
  7. 7. Transportation models can roughly be grouped into “supply” and “demand” “Supply” side Traffic jams Signalized control Contraflow lanes “Demand” side Route choice Traveler information Mode choice Autonomous vehicles interact very heavily with both of these.
  8. 8. “Supply” side Traffic jams Signalized control Contraflow lanes “Demand” side Route choice Traveler information Mode choice These two “sides” interact with each other heavily, and an equilibrium concept is often used to reconcile them. Travel choices determine congestion, but congestion affects travel choices.
  9. 9. SUPPLY MODELS
  10. 10. On a highway, AVs can increase capacity because they require less following distance.
  11. 11. In traffic flow theory, this will change the shape of the “fundamental diagram” relating vehicle density to flow. 0 1000 2000 3000 4000 5000 6000 7000 8000 0 50 100 150 200 250 300 Flow(veh/hr) Density (veh/mi) 0.25 0.5 1 1.5 Levin & Boyles (2015, under review)
  12. 12. At intersections, we can do even better. Dresner & Stone (2010)
  13. 13. At intersections, we can do even better. Dresner & Stone (2010)
  14. 14. At intersections, we can do even better. Dresner & Stone (2010)
  15. 15. Levin & Boyles (2014) These high-detail simulation models can also be approximated for use in models with hundreds of intersections. Reservation-based intersections in DTA Model
  16. 16. DEMAND MODELS
  17. 17. Will AVs induce new demand? How will they affect choice of destination? Parking? Transit? vs.
  18. 18. Trip generation Productions and attractions Trip distribution Person-trips per origin-destination Mode choice Origin-destination trips per mode Traffic assignment Routes and flows at user equilibrium feedback
  19. 19. Transit Park at destination • Parking fee Return to origin • Fuel costs logit model minimum cost Personal vehicle Person trips
  20. 20. Downtown Austin network – 88 zones – 634 nodes – 1574 links – 62836 trips – 84 bus routes 10 value of time classes Levin & Boyles (2015)
  21. 21. Levin & Boyles (2015) 20 21 22 23 24 25 26 27 28 29 0 2 4 6 8 10 Avg.linktraveltime(sec) Number of classes with autonomous vehicles Effects on traffic
  22. 22. Levin & Boyles (2015) Effects on transit 14000 15000 16000 17000 18000 19000 20000 21000 0 2 4 6 8 10 Transitdemand(persontrips) Number of classes with autonomous vehicles
  23. 23. REAL-TIME CONTROL
  24. 24. Improved signal operations
  25. 25. Dynamic lane reversal
  26. 26. Carrots and sticks...
  27. 27. In the future, we may see the distinction between “operations” and “planning” start to blur. Planning: Long-term time horizon; future forecasts or scenarios; alternatives analysis and project rankings Operations: Present-day modeling; real- time observation and control; travel information provision The future: Future planning and policy analysis accounting for real-time control technologies
  28. 28. Conclusions • AVs present tremendous opportunity, but must be carefully planned for.
  29. 29. Conclusions • AVs present tremendous opportunity, but must be carefully planned for. • Emerging modeling techniques can help guide policy.
  30. 30. Conclusions • AVs present tremendous opportunity, but must be carefully planned for. • Emerging modeling techniques can help guide policy. • The future is now!

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