1. by Alain L. Kornhauser, PhD Professor, Operations Research & Financial Engineering Director, 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 Appropriate Modelling of Travel Demand in a SmartDrivingCar World

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)*: 2.640x10 12 Passenger Car; 1.927x10 12 SUV/Light Truck; 0.052x10 12 All Transit; 0.006x10 12 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.pdfhttp://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  A & B are walk accessible areas, typically: – Very large number of very geographically diffused {A,B} pairs  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  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  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. 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 SmartDrivingCars & Trucks What is a SmartDrivingCar?

6. Preliminary Statement of Policy Concerning Automated Vehicles What is a SmartDrivingCar? Level“Less”Value PropositionMarket ForceSocietal Implications

7. Preliminary Statement of Policy Concerning Automated Vehicles What is a SmartDrivingCar? Level“Less”Value PropositionMarket ForceSocietal Implications 0 “55 Chevy” Zero

8. Preliminary Statement of Policy Concerning Automated Vehicles What is a SmartDrivingCar? Level“Less”Value PropositionMarket ForceSocietal Implications 0 “55 Chevy” Zero 1 “Cruise Control” InfinitesimalSome ComfortInfinitesimal

9. Preliminary Statement of Policy Concerning Automated Vehicles What is a SmartDrivingCar? Level“Less”Value PropositionMarket ForceSocietal Implications 0 “55 Chevy” Zero 1 “Cruise Control” InfinitesimalSome ComfortInfinitesimal 2 “Collision Avoidance & Lane Centering” InfinitesimalMuch Safety (but Consumers don’t pay for Safety) Needs help From “Flo & the Gecko” (Insurance incentivizes adoption) “50%” fewer accidents; less severity-> 50% less insurance $ liability

10. Preliminary Statement of Policy Concerning Automated Vehicles What is a SmartDrivingCar? Level“Less”Value PropositionMarket ForceSocietal Implications 0 “55 Chevy” Zero 1 “Cruise Control” InfinitesimalSome ComfortInfinitesimal 2 “Collision Avoidance & Lane Centering” InfinitesimalMuch Safety (but Consumers don’t pay for Safety) Needs help From “Flo & the Gecko” (Insurance incentivizes adoption) “50%” fewer accidents; less severity-> 50% less insurance $ liability 3 “Texting Machine” SomeLiberation (some of the time/places) ; more Safety Consumers Pull, TravelTainment Industry Push Increased car sales, many fewer insurance claims, slight + in VMT

11. Preliminary Statement of Policy Concerning Automated Vehicles What is a SmartDrivingCar? Level“Less”Value PropositionMarket ForceSocietal Implications 0 “55 Chevy” Zero 1 “Cruise Control” InfinitesimalSome ComfortInfinitesimal 2 “Collision Avoidance & Lane Centering” InfinitesimalMuch Safety (but Consumers don’t pay for Safety) Needs help From “Flo & the Gecko” (Insurance incentivizes adoption) “50%” fewer accidents; less severity-> 50% less insurance $ liability 3 “Texting Machine” SomeLiberation (some of the time/places) ; more Safety Consumers Pull, TravelTainment Industry Push Increased car sales, many fewer insurance claims, slight + in VMT 4 “aTaxi “ AlwaysChauffeured, Buy Mobility “by the Drink” rather than “by the Bottle” Profitable Business Opportunity for Utilities/Transit Companies Personal Car becomes “Bling” not instrument of personal mobility, VMT ?; Comm. Design ? Energy, Congestion, Environment?

12. Preliminary Statement of Policy Concerning Automated Vehicles What the Levels Deliver: Levels 1 -> 2: Increased Safety, Comfort & Convenience 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) 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) Primarily an Insurance Discount Play A Corporate Utility/Fleet Play Levels 3: Increased Pleasure, Safety, Comfort & Convenience An Enormous Consumer Play

13. 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”? What about Level 4 Implications on Energy, Congestion, Environment?

14. 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 What about Level 4 Implications on Energy, Congestion, Environment? Assuming Planners Don’t Change

15. “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” Kinds of RideSharing

16. “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. aTaxis and RideSharing

17. 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! Spatial Aggregation Elevator

18. 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 What about Level 4 Implications on Energy, Congestion, Environment? Assuming Planners Don’t Change

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

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

21. a PersonTrip {oLat, oLon, oTime (Hr:Min:Sec),dLat, dLon, Exected: dTime} a PersonTrip {oLat, oLon, oTime (Hr:Min:Sec),dLat, dLon, Exected: dTime} O O D P1P1 An aTaxiTrip {oYpixel, oXpixel, oTime (Hr:Min:Sec), } An aTaxiTrip {oYpixel, oXpixel, oTime (Hr:Min:Sec), } An aTaxiTrip {oYpixel, oXpixel, oTime (Hr:Min:Sec),dYpixel, dXpixel, Exected: dTime} An aTaxiTrip {oYpixel, oXpixel, oTime (Hr:Min:Sec),dYpixel, dXpixel, Exected: dTime}

22. P1P1 O Common Destination (CD) CD=1p: Pixel -> Pixel (p->p) Ride-sharing Common Destination (CD) CD=1p: Pixel -> Pixel (p->p) Ride-sharing TripMiles = L TripMiles = 2L TripMiles = 3L

23. P1P1 O PersonMiles = 3L aTaxiMiles = L AVO = PersonMiles/aTaxiMiles = 3 PersonMiles = 3L aTaxiMiles = L AVO = PersonMiles/aTaxiMiles = 3

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

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

26. 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 Spatial Aggregation

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

28. 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. Spatial Aggregation

29. P1P1 P2P2 O CD= 3p: Pixel ->3Pixels Ride-sharing

30. P1P1 P5P5 O P3P3

31. – 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) What about Level 4 Implications on Energy, Congestion, Environment?

32. 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? Trip Synthesizer (Activity-Based) Project Overview

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_Resident file for “every” NJ Traveler on a typical day NJ_Resident file Start with Publically available data:

36. Bergen County @ Block Level CountyPopulationCensus Blocks Median Pop/ Block Average Pop/Block BER 907,128 11,1165881.6

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} All Trips Home County TripsTripMilesAverageTM #Miles ATL 936,585 27,723,93129.6 BER 3,075,434 40,006,14513.0 BUC 250,006 9,725,08038.9 BUR 1,525,713 37,274,68224.4 CAM 1,746,906 27,523,67915.8 CAP 333,690 11,026,87433.0 CUM 532,897 18,766,98635.2 ESS 2,663,517 29,307,43911.0 GLO 980,302 23,790,79824.3 HUD 2,153,677 18,580,5858.6 HUN 437,598 13,044,44029.8 MER 1,248,183 22,410,29718.0 MID 2,753,142 47,579,55117.3 MON 2,144,477 50,862,65123.7 MOR 1,677,161 33,746,36020.1 NOR 12,534 900,43471.8 NYC 215,915 4,131,76419.1 OCE 1,964,014 63,174,46632.2 PAS 1,704,184 22,641,20113.3 PHL 46,468 1,367,40529.4 ROC 81,740 2,163,31126.5 SAL 225,725 8,239,59336.5 SOM 1,099,927 21,799,64719.8 SOU 34,493 2,468,01671.6 SUS 508,674 16,572,79232.6 UNI 1,824,093 21,860,03112.0 WAR 371,169 13,012,48935.1 WES 16,304 477,95029.3 Total 32,862,668 590,178,59719.3

39. NJ_PersonTrip file

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

42. Results

44. What about the whole country?

45. Public Schools in the US

46. Nation-Wide Businesses RankState Sales VolumeNo. Businesses 1California$1,8891,579,342 2Texas$2,115999,331 3Florida$1,702895,586 4New York$1,822837,773 5Pennsylvania$2,134550,678 9New Jersey$1,919428,596 45Washington DC$1,31749,488 47Rhode Island$1,81446,503 48North Dakota$1,97844,518 49Delaware$2,10841,296 50Vermont$1,55439,230 51Wyoming$1,67935,881 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 ????

52. Trip Files are Available If You want to Play

53. Thank You alaink@princeton.edu www.SmartDrivingCar.com Discussion!