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Uncongested Mobility for All: A Proposal for an Area Wide Autonomous Taxi System in New Jersey By Jaison Zachariah ‘13

Uncongested Mobility for All: A Proposal for an Area Wide Autonomous Taxi System in New Jersey By Jaison Zachariah ‘13 Jingkang Gao ‘13 Tala Mufti *13 Recent Grads, Operations Research & Financial Engineering Princeton University

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Uncongested Mobility for All: A Proposal for an Area Wide Autonomous Taxi System in New Jersey By Jaison Zachariah ‘13

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  1. Uncongested Mobility for All: A Proposal for an Area Wide Autonomous Taxi System in New Jersey ByJaison Zachariah ‘13 Jingkang Gao ‘13 Tala Mufti *13 Recent Grads, Operations Research & Financial Engineering Princeton University Alain L. Kornhauser *71Professor, Operations Research & Financial Engineering Director, Program in Transportation Faculty Chair, PAVE (Princeton Autonomous Vehicle Engineering Princeton University Presented at January 20, 2014

  2. Outline What is an autonomousTaxi (aTaxi) Synthesizing an Appropriate Representation of All Person Trips in New Jersey on a Typical Weekday How much Ride-Sharing (AVO) could various aTaxi service offerings stimulate Next Step

  3. 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

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

  5. What about Level 4 Implications on Energy, Congestion, Environment? • What if a “Community Design” (New Jersey) only had • Walking, • Bicycling, • NJ Transit Rail • aTaxis for mobility. What are the Societal Implications of that Mobility (Energy, Pollution, Congestion) ? (Hint: It’s all about Ride-Sharing!)

  6. New Jersey “Today” • New Jersey’s existing Land-uses generate about 32 million Trips / Day • The Automobile (~ 28 million) • Walking + bicycling (~3 million) • Bus + rail Transit (~1 million) • While Concentrated at some Times in some Corridors • Most of those trips are enormously diffuse in time and space

  7. Creating the NJ_PersonTrip file • “every” trip that each Traveler is likely to make on a typical day. NJ_PersonTrip file {oLat, oLon, oTime, dLat, dLon, Est_dTime} • Start with • NJ_Residentfile(120,000 Census Blocks) • NJ_Employment file (430,000 businesses) • NJ_School file (18,000 schools) • Readily assign trips between Home and Work/School • Trip Activity -> Stop Sequence • Home, Work, School characteristics synthesized in NJ_Resident file

  8. Project Overview Overview of Data Production Generate each person that lives or works in NJ Assign work places to each worker Assign schools to each student Assign tours / activity patterns Assign other trips Assign arrival / departure times

  9. 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?

  10. 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

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

  12. 2010 Population census @Block Level • 8,791,894 individuals distributed 118,654 Blocks.

  13. Bergen County @ Block Level

  14. Publically available data: • Distributions of Demographic Characteristics • Age • Gender • Household size • Name (Last, First)

  15. Beginnings of NJ_Resident file County Task 1 2010 Census # People, Lat, Lon, For each person Vital Stats RandomDraw: Age, M/F, WorkerType,

  16. Home County Using Census Journey-to-Work (J2W) Tabulations to assign Employer County Task 2 C2C Journey2Work Work County WorkCounty Destination RandomDraw: Journey2Work http://www.census.gov/population/www/cen2000/commuting/files/2KRESCO_NJ.xls http://www.census.gov/population/www/cen2000/commuting/files/2KWRKCO_NJ.xls

  17. Using Employer Data to assign a Workplace Characteristics Employment-Weighted Random Draw

  18. Using School Data to Assign School Characteristics

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

  20. Final NJ_Resident file Home County Person Index Household Index Full Name Age Gender Worker Type Index Worker Type String Home lat, lon Work or School lat,lon Work County Work or School Index NAICS code Work or School start/end time

  21. Assigning “Other” Locations 1. Select Other County Using: Attractiveness-Weighted Random Draw Attractiveness (i)= (Patrons (I)/AllPatrons)/{D(i,j)2 + D(j,k)2}; Where i is destination county; j is current county; k is home county 2. Select “Other” Business using: Patronage-Weighted Random Draw within selected county

  22. Assigning Trip Departure Times Task 8 Trip Type; SIC Distribution of Arrival/Departure Times Time Generator: RandomDraw: Time Distribution Trip Departure time (SeconsFromMidnight) • For: H->W; H->School; W->Other • Work backwards from Desired Arrival Time using • Distance and normally distributed Speed distribution, and • Non-symmetric early late probabilities • Else, Use Stop Duration with non-symmetric early late probabilities based on SIC Cod

  23. 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}

  24. NJ_PersonTrip file

  25. NJ_PersonTrip file

  26. Warren County Population: 108,692 Intra-pixel Trips

  27. aTaxi Implications on Mobility, Energy, Congestion, Environment • What if the only way to get around was by • Walking, • Bicycling, • NJ Transit Rail • aTaxis What are the Societal Implications of this System (Mobility, Energy, Pollution, Congestion) ? (Hint: It’s all about Ride-Sharing!)

  28. aTaxi Implications on Mobility, Energy, Congestion, Environment • 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

  29. 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”

  30. 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.

  31. 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

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

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

  34. 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

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

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

  37. 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

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

  39. 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

  40. 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

  41. 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.

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

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

  44. CD= 3sp: Pixel ->3SuperPixels Ride-sharing SP5 SP1 P5 P1 SP4 O P3 P6 SP6

  45. 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

  46. Results

  47. Results

  48. What about the whole country? Extending the Activity-Based Person-Trip Synthesizer to all 330 million Americans Judy Sun ‘14 & Luke Cheng ’14 ORF467 F13

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