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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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slide1

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

outline
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

slide3

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

slide4

What is a SmartDrivingCar?

Preliminary Statement of Policy Concerning Automated Vehicles

what about level 4 implications on energy congestion environment
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!)

new jersey today
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
creating the nj persontrip file
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
overview of data production

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

slide10

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:

2010 population census @block level
2010 Population census @Block Level
  • 8,791,894 individuals distributed 118,654 Blocks.
publically available data
Publically available data:
  • Distributions of Demographic Characteristics
    • Age
    • Gender
    • Household size
    • Name (Last, First)
beginnings of nj resident file
Beginnings of NJ_Resident file

County

Task 1

2010 Census

# People,

Lat, Lon,

For each person

Vital Stats

RandomDraw:

Age, M/F, WorkerType,

using census journey to work j2w tabulations to assign employer county

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

final nj resident file
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

assigning other locations
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

assigning trip departure times
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
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}
slide27

Warren County

Population: 108,692

Intra-pixel Trips

ataxi implications on mobility energy congestion environment
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!)

slide30

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
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
pixelation of new jersey
Pixelation of New Jersey

Zoomed-In Grid of Mercer

NJ State Grid

slide35

Pixelating the State

with half-mile Pixels

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

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

slide36

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

slide37

Common Destination (CD)

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

P1

O

TripMiles = L

TripMiles = 2L

TripMiles = 3L

slide38

P1

O

PersonMiles = 3L

PersonMiles = 3L

aTaxiMiles = L

AVO = PersonMiles/aTaxiMiles = 3

slide39

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

slide40

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
slide42

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.
slide47

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

what about the whole country

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

nation wide businesses
Nation-Wide Businesses

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

us persontrip file will have
US_PersonTrip file will have..
  • ~330 Million records
    • One for each person in US_Resident file
  • Specifying ~1.2 Billion Daily Person Trips
    • Each characterized by a precise
      • {oLat, oLon, oTime, dLat, dLon, Est_dTime}
  • Will Perform Nationwide aTaxi AVO analysis
  • Results ????
slide55

Discussion!

Thank You

alaink@princeton.edu

www.SmartDrivingCar.com

scope of automated vehicles
Scope of “Automated Vehicles”

Aichi, Japan, 2005 Expo

Mercedes Intelligent Drive

Rio Tinto Automated Truck

Milton Keynes, UK

Heathrow PodCar

Tampa Airport 1st APM 1971

Copenhagen Metro

Elevator

Automated Guided Vehicles

Rivium 2006 ->

Tesla Car Transporter

Rio Tinto Automated Train

CityMobil2

slide57

SmartDrivingCars: Post-DARPA Challenges (2010-today)

(Video has no sound)

  • VisLab.it (U. of Parma, Italy)
    • Had assisted Oshkosh in DARPA Challenges
    • Stereo vision + radars
slide58

Crash Mitigation

(air bags, seat belts, crash worthiness, …)

Up to today: The Primary Purview of

Good News: Effective in reducing Deaths and Accident Severity

Bad News: Ineffective in reducing Expected Accident Liability &

Ineffective in reducing Insurance Rates

slide59

Click images to view videos

Intelligent Drive (active steering  )

MB @ Frankfurt Auto Show Sept ‘13

S-Class WW Launch May ‘13

Volvo Truck Emergency braking

MB Demo Sept ‘13