Appropriate Modelling of Travel Demand in a SmartDrivingCar World

1. Appropriate Modelling of Travel Demand in a SmartDrivingCar World by Alain L. Kornhauser, PhDProfessor, Operations Research & Financial EngineeringDirector, Program in Transportation Faculty Chair, PAVE (Princeton Autonomous Vehicle Engineering) Princeton University Presented at American Planning Association 2014 Annual Conference Atlanta, GA April 26, 2014

2. Conventional Cars Drive Urban/City Planning

3. Current State of Public Transport… • Not Good!: • Serves about 2% of all motorized trips • Passenger Miles (2007)*: • 2.640x1012 Passenger Car; • 1.927x1012 SUV/Light Truck; • 0.052x1012 All Transit; • 0.006x1012 Amtrak • Does a little better in “peak hour” and NYC • 5% commuter trips • NYC Met area contributes about half of all transit trips • Financially it’s a “train wreck” http://www.bts.gov/publications/national_transportation_statistics/2010/pdf/entire.pdf, Table1-37

4. Transit’s Fundamental Problem… • Transit is non-competitive to serve most travel demand • Travel Demand(desire to go from A to B in a time window DT) • A & B are walk accessible areas, typically: • Very large number of very geographically diffused {A,B} pairs • DT is diffused throughout the day with only modest concentration in morning and afternoon peak hours • The conventionalAutomobile at “all” times Serves… • Essentially all {A,B} pairs demand-responsively within a reasonable DT • Transit at “few” times during the day Serves… • a modest number of A & B on scheduled fixed routes • But very few {A,B} pairs within a reasonable DT • Transit’s need for an expensive driver Forces it to only offer infrequent scheduled fixed route service between few {A,B} pairs • But… Transit can become demand-responsive serving many {A,B} if the driver is made cheap and it utilizes existing roadway infrastructure. 0.25 mi.

5. What is a SmartDrivingCar? Preliminary Statement of Policy Concerning Automated Vehicles Level 0 (No automation) The human is in complete and sole control of safety-critical functions (brake, throttle, steering) at all times. Level 1 (Function-specific automation) The human has complete authority, but cedes limited control of certain functions to the vehicle in certain normal driving or crash imminent situations. Example: electronic stability control  Level 2 (Combined function automation) Automation of at least two control functions designed to work in harmony (e.g., adaptive cruise control and lane centering) in certain driving situations. Enables hands-off-wheel and foot-off-pedal operation. Driver still responsible for monitoring and safe operation and expected to be available at all times to resume control of the vehicle. Example: adaptive cruise control in conjunction with lane centering Level 3 (Limited self-driving) Vehicle controls all safety functions under certain traffic and environmental conditions. Human can cede monitoring authority to vehicle, which must alert driver if conditions require transition to driver control. Driver expected to be available for occasional control. Example: Google car Level 4 (Full self-driving automation) Vehicle controls all safety functions and monitors conditions for the entire trip. The human provides destination or navigation input but is not expected to be available for control during the trip. Vehicle may operate while unoccupied. Responsibility for safe operation rests solely on the automated system & Trucks SmartDrivingCars

6. What is a SmartDrivingCar? Preliminary Statement of Policy Concerning Automated Vehicles

7. What is a SmartDrivingCar? Preliminary Statement of Policy Concerning Automated Vehicles

8. What is a SmartDrivingCar? Preliminary Statement of Policy Concerning Automated Vehicles

9. What is a SmartDrivingCar? Preliminary Statement of Policy Concerning Automated Vehicles

10. What is a SmartDrivingCar? Preliminary Statement of Policy Concerning Automated Vehicles

11. What is a SmartDrivingCar? Preliminary Statement of Policy Concerning Automated Vehicles

12. Preliminary Statement of Policy Concerning Automated Vehicles What the Levels Deliver: Levels 1 -> 2: Increased Safety, Comfort& Convenience Primarily an Insurance Discount Play Levels 3: Increased Pleasure,Safety,Comfort& Convenience An EnormousConsumer Play Level 4 (Driverless Repositioning) :Pleasure, Mobility, Efficiency, Equity Revolutionizes “Mass Transit” by Greatly Extending the Trips that can be served @ “zero” cost of Labor. (That was always the biggest “value” of PRT; zero labor cost for even zero-occupant trips) A Corporate Utility/Fleet Play

13. What about Level 4 Implications on Energy, Congestion, Environment? • Assuming PLANNERS continue to PLAN as they do now. • How will people “get around”? • Assuming this new way of “getting around” offers different opportunities and constraints for PLANNERS to improve “Quality of Life”. • How will Zoning/Land-Use Change? • How will people “get around”?

14. What about Level 4 Implications on Energy, Congestion, Environment?Assuming Planners Don’t Change • Land-Use hasn’t changed • Trip ends don’t change! • Assume Trip Distribution Doesn’t Change • Then it is only Mode Split. • Do I: • Walk? • Ride alone? • Ride with someone? • All about Ride-sharing

15. Kinds of RideSharing • “AVO < 1” RideSharing • “Daddy, take me to school.” (Lots today) • “Organized” RideSharing • Corporate commuter carpools (Very few today) • “Tag-along” RideSharing • One person decides: “I’m going to the store. Wanna come along”. Other: “Sure”. (Lots today) • There exists a personal correlation between ride-sharers • “Casual” RideSharing • Chance meeting of a strange that wants to go in my direction at the time I want to go • “Slug”, “Hitch hiker”

16. aTaxis and RideSharing • “AVO < 1” RideSharing • Eliminate the “Empty Back-haul”; AVO Plus • “Organized” RideSharing • Diverted to aTaxis • “Tag-along” RideSharing • Only Primary trip maker modeled, “Tag-alongs” are assumed same after as before. • “Casual” RideSharing • This is the opportunity of aTaxis • How much spatial and temporal aggregation is required to create significant casual ride-sharing opportunities.

17. Spatial Aggregation • By walking to a station/aTaxiStand • At what point does a walk distance makes the aTaxi trip unattractive relative to one’s personal car? • ¼ mile ( 5 minute) max • Like using an Elevator! Elevator

18. What about Level 4 Implications on Energy, Congestion, Environment?Assuming Planners Don’t Change • No Change in Today’s Walking, Bicycling and Rail trips • Today’s Automobile trips become aTaxi or aTaxi+Rail trips with hopefully LOTS of Ride-sharing opportunities

19. Pixelation of New Jersey Zoomed-In Grid of Mercer NJ State Grid

20. Pixelating the State with half-mile Pixels xPixel = floor{108.907 * (longitude + 75.6)} yPixel = floor{138.2 * (latitude – 38.9))

21. An aTaxiTrip {oYpixel, oXpixel, oTime (Hr:Min:Sec) , } An aTaxiTrip {oYpixel, oXpixel, oTime (Hr:Min:Sec) ,dYpixel, dXpixel, Exected: dTime} a PersonTrip {oLat, oLon, oTime (Hr:Min:Sec) ,dLat, dLon, Exected: dTime} P1 D O O

22. Common Destination (CD) CD=1p: Pixel -> Pixel (p->p) Ride-sharing P1 O TripMiles = L TripMiles = 2L TripMiles = 3L

23. P1 O PersonMiles = 3L PersonMiles = 3L aTaxiMiles = L AVO = PersonMiles/aTaxiMiles = 3

24. Elevator Analogy of an aTaxi Stand Temporal Aggregation Departure Delay: DD = 300 Seconds Kornhauser Obrien Johnson 40 sec Popkin 3:47 Henderson Lin 1:34

25. Elevator Analogy of an aTaxi Stand 60 seconds later Christie Maddow 4:12 Henderson Lin Young 0:34 Samuels 4:50 Popkin 2:17

26. Spatial Aggregation • By walking to a station/aTaxiStand • A what point does a walk distance makes the aTaxi trip unattractive relative to one’s personal car? • ¼ mile ( 5 minute) max • By using the rail system for some trips • Trips with at least one trip-end within a short walk to a train station. • Trips to/from NYC or PHL

27. a PersonTrip from NYC (or PHL or any Pixel containing a Train station) An aTaxiTrip {oYpixel, oXpixel, TrainArrivalTime, dYpixel, dXpixel, Exected: dTime} NJ Transit Rail Line to NYC, next Departure NYC D O aTaxiTrip Princeton Train Station

28. Spatial Aggregation • By walking to a station/aTaxiStand • A what point does a walk distance makes the aTaxi trip unattractive relative to one’s personal car? • ¼ mile ( 5 minute) max • By using the rail system for some trips • Trips with at least one trip end within a short walk to a train station. • Trips to/from NYC or PHL • By sharing rides with others that are basically going in my direction • No trip has more than 20% circuity added to its trip time.

29. CD= 3p: Pixel ->3Pixels Ride-sharing P1 O P2

30. CD= 3p: Pixel ->3Pixels Ride-sharing P5 P1 O P3

31. What about Level 4 Implications on Energy, Congestion, Environment? • I just need a Trip File for some Local • {Precise O, Precise oTime, Precise D} • For All Trips! • “Precise” Location: Within a Very Short Walk ~ Parking Space -> Front Door (Properly account for accessibility differences: conventionalAuto v aTaxi) • “Precise” oTime : “to the second” (Properly account for how long one must wait around to ride with someone else)

32. Project Overview Trip Synthesizer (Activity-Based) • Motivation – • Publicly available TRAVEL Data do NOT contain: • Spatial precision • Where are people leaving from? • Where are people going? • Temporal precision • At what time are they travelling?

34. Synthesize from available data: • “every” NJ Traveler on a typical day NJ_Resident file • Containing appropriate demographic and spatial characteristics that reflect trip making • “every” trip that each Traveler is likely to make on a typical day. NJ_PersonTrip file • Containing appropriate spatial and temporal characteristics for each trip

35. Creating the NJ_Residentfile for “every” NJ Traveler on a typical day NJ_Resident file Start with Publically available data:

36. Bergen County @ Block Level

37. Assigning a Daily Activity (Trip) Tour to Each Person

38. NJ_PersonTrip file • 9,054,849 records • One for each person in NJ_Resident file • Specifying 32,862,668 Daily Person Trips • Each characterized by a precise • {oLat, oLon, oTime, dLat, dLon, Est_dTime}

39. NJ_PersonTrip file

41. http://orfe.princeton.edu/~alaink/NJ_aTaxiOrf467F13/Orf467F13_NJ_TripFiles/MID-1_aTaxiDepAnalysis_300,SP.xlsxhttp://orfe.princeton.edu/~alaink/NJ_aTaxiOrf467F13/Orf467F13_NJ_TripFiles/MID-1_aTaxiDepAnalysis_300,SP.xlsx c

42. Results

43. Results

44. What about the whole country?

45. Public Schools in the US

46. Nation-Wide Businesses 13.6 Million Businesses{Name, address, Sales, #employees}

47. US_PersonTrip file will have.. • 308,745,538 records • One for each person in US_Resident file • Specifying 1,009,332,835 Daily Person Trips • Each characterized by a precise • {oLat, oLon, oTime, dLat, dLon, Est_dTime} • Will Perform Nationwide aTaxi AVO analysis • Results ????