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Busses & autonomousTaxis. 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 PAVE – Summer Workshop Princeton, NJ

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slide1

Busses & autonomousTaxis

by

Alain L. Kornhauser, PhDProfessor, Operations Research & Financial EngineeringDirector, Program in Transportation

Faculty Chair, PAVE (Princeton Autonomous Vehicle Engineering)

Princeton University

Presented at

PAVE – Summer Workshop

Princeton, NJ

August 4-6, 2014

casualty and liability claims are a huge drain on the industry
Casualty and Liability Claims are a Huge Drain on the Industry
  • For the 10 year period 2002-2011, more than $4.1 Billion was spent on casualty and liability claims
  • For many self-insured transit agencies these expenses are direct “out-of-pocket”
slide8

The Cost of Installing an Active Collision Avoidance System on a Bus Could be Recovered in as Little as One Year Through Reductions in Casualty and Liability Claims

why new jersey
Why New Jersey?
  • Observation: In 2 Years, NJ Transit will initiate a new Bus Replacement Cycle (That will extend for about 15 years)
  • Action Item:
    • Ensure that the Procurement Specifications include “Level 2” SmartDriving Technologies
near term opportunity for a substantive extension of autonomous transit
Near-term Opportunity for a Substantive Extension of Autonomous Transit
  • Specific: General Mobility for Fort Monmouth Redevelopment
  • Currently: Decommissioned Ft. Monmouth is vacant .
    • Ft. Monmouth Economic Revitalization Authority (FMERA) is redeveloping the 3 sq. mile “city”
    • Focus is on attracting high-tech industry
    • The “Fort” needs a mobility system.
    • FMERA is receptive to incorporating an innovative mobility system
    • Because it is being redeveloped as a “new town” it can accommodate itself to be an ideal site for testing more advanced driverless systems.
the initial project

Princeton University (with American Public Transit Association (APTA), Greater Cleveland Transit, and insurance pools from WA, CA, OH & VA)

Pending $5M Grant from

Federal Transit Administration

The Initial Project:

Focused on

Research, Certification and Commercialization

of

SmartDriving Technology to Buses

proposal done december 2 2013 for next 6 months silence from fta

In those 6 months approximately:

39 Fatalities

7,200 Injuries

$180M Claims

“Level 2 Collision Avoidance Technology”

Could cut those numbers in half

Proposal Done: December 2, 2013:

For next 6 months: Silence from FTA

Why the delay in spending $5M to get the process started ???????

slide13

Discussion!

Thank You

[email protected]

www.SmartDrivingCar.com

what about level 4 implications on energy congestion environment
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”?
what about level 4 implications on energy congestion environment assuming planners don t change
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
kinds of ridesharing
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”
ataxis and ridesharing
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.
spatial aggregation
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

what about level 4 implications on energy congestion environment assuming planners don t change1
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
pixelation of new jersey
Pixelation of New Jersey

Zoomed-In Grid of Mercer

NJ State Grid

slide21

Pixelating the State

with half-mile Pixels

xPixel = floor{108.907 * (longitude + 75.6)}

yPixel = floor{138.2 * (latitude – 38.9))

slide22

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

slide23

Common Destination (CD)

CD=1p: Pixel -> Pixel (p->p) Ride-sharing

P1

O

TripMiles = L

TripMiles = 2L

TripMiles = 3L

slide24

P1

O

PersonMiles = 3L

PersonMiles = 3L

aTaxiMiles = L

AVO = PersonMiles/aTaxiMiles = 3

slide25

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

slide26

Elevator Analogy of an aTaxi Stand

60 seconds later

Christie

Maddow

4:12

Henderson

Lin

Young

0:34

Samuels

4:50

Popkin

2:17

spatial aggregation1
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
slide28

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

spatial aggregation2
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.
what about level 4 implications on energy congestion environment1
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)

slide33

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?
synthesize from available data
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
creating the nj resident file
Creating the NJ_Residentfile

for “every” NJ Traveler on a typical day

NJ_Resident file

Start with Publically available data:

nj persontrip file
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}
slide42

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

nation wide businesses
Nation-Wide Businesses

13.6 Million Businesses{Name, address, Sales, #employees}

us persontrip file will have
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 ????
slide54

Discussion!

Thank You

[email protected]

www.SmartDrivingCar.com

current state of public transport
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

transit s fundamental problem
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.