Busses & autonomousTaxis
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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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Busses autonomoustaxis

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


Use autonomous collision avoidance technology to address a big current transit problem

Use Autonomous Collision Avoidance Technology to Address a BIG CURRENT Transit Problem


Good news travel by bus is getting safer

Good News! Travel by Bus is getting safer!


Good news injuries have been trending down

Good News! Injuries have been trending down!


Terrible news claims are going through the roof

Terrible News! Claims are going through the roof!


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”


Busses autonomoustaxis

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 ???????


Busses autonomoustaxis

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


Busses autonomoustaxis

Pixelating the State

with half-mile Pixels

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

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


Busses autonomoustaxis

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


Busses autonomoustaxis

Common Destination (CD)

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

P1

O

TripMiles = L

TripMiles = 2L

TripMiles = 3L


Busses autonomoustaxis

P1

O

PersonMiles = 3L

PersonMiles = 3L

aTaxiMiles = L

AVO = PersonMiles/aTaxiMiles = 3


Busses autonomoustaxis

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


Busses autonomoustaxis

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


Busses autonomoustaxis

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.


Busses autonomoustaxis

CD= 3p: Pixel ->3Pixels Ride-sharing

P1

O

P2


Busses autonomoustaxis

CD= 3p: Pixel ->3Pixels Ride-sharing

P5

P1

O

P3


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)


Busses autonomoustaxis

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:


Bergen county @ block level

Bergen County @ Block Level


Assigning a daily activity trip tour to each person

Assigning a Daily Activity (Trip) Tour to Each Person


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}


Busses autonomoustaxis

NJ_PersonTrip file


Busses autonomoustaxis

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

c


Busses autonomoustaxis

Results


Busses autonomoustaxis

Results


What about the whole country

What about the whole country?


Public schools in the us

Public Schools in the US


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 ????


Trip files are available if you want to play

Trip Files are Available If You want to Play


Busses autonomoustaxis

Discussion!

Thank You

[email protected]

www.SmartDrivingCar.com


Conventional cars drive urban city planning

Conventional Cars Drive Urban/City Planning


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.


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