orfe.princeton.edu

Transcript orfe.princeton.edu

Uncongested Mobility for All:

A Proposal for an Area Wide Autonomous Taxi System in New Jersey

Jaison Zachariah ‘13 Jingkang Gao ‘13 Tala Mufti *13 Recent Grads, Operations Research & Financial Engineering

Princeton University

Alain L. Kornhauser *71 Professor, Operations Research & Financial Engineering Director, Program in Transportation Faculty Chair, PAVE (Princeton Autonomous Vehicle Engineering

Princeton University

Presented at

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

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

What is a SmartDrivingCar?

Preliminary Statement of Policy Concerning Automated Vehicles Level “Less” Zero Value Proposition Zero Market Force Zero Societal Implications Zero 0 “55 Chevy” 1 “Cruise Control” 2 “CC + Emergency Braking” 3 “Texting Machine” 4 “aTaxi “ Infinitesimal Infinitesimal Some Always Some Comfort Infinitesimal Some Safety Liberation (some of the time/places) ; much more Safety Get to be Chauffeured; Get to Buy Mobility “by the Drink” rather than “by the Bottle” Small; Needs help From “Flo & the Gecko” (Insurance Industry) Consumers Pull, TravelTainment Industry Push Profitable Business Opportunity for Utilities/Transit Companies Infinitesimal “20+%” fewer accidents; less severity; fewer insurance claims Increased car sales, many fewer insurance claims, Increased VMT Personal Car becomes “Bling” not instrument of personal mobility, VMT ?; Comm. Design ? 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’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

• • • “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

Project Overview

Overview of Data Production

1. Generate each person that lives or works in NJ 2. Assign work places to each worker 3. Assign schools to each student 4. Assign tours / activity patterns 5. Assign other trips 6. Assign arrival / departure times

• •

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?

Project Overview

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 for “every” NJ Traveler on a typical day

NJ_Resident file

Start with Publically available data:

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

County ATL BER BUR CAM CAP CUM ESS GLO HUD HUN MER MID MON MOR OCE PAS SAL SOM SUS UNI WAR Total Population

274,549 905,116 448,734 513,657 97,265 156,898 783,969 288,288 634,266 128,349 366,513 809,858 630,380 492,276 576,567 501,226 66,083 323,444 149,265 536,499 108,692 8,791,894

Census Blocks

5,941 11,171 7,097 7,707 3,610 2,733 6,820 4,567 3,031 2,277 4,611 9,845 10,067 6,543 10,457 4,966 1,665 3,836 2,998 6,139 2,573 118,654

Median Pop/ Block

26 58 41 47 15 34 77 40 176 31 51 50 39 45 31 65 26 51 28 61 23

Average Pop/Block

46 81 63 67 27 57 115 63 209 56 79 82 63 75 55 101 40 84 50 87 42 74.1

Bergen County @ Block Level

County BER Population

907,128

Census Blocks

11,116

Median Pop/ Block

Average Pop/Block

81.6

Publically available data:

• Distributions of Demographic Characteristics – Age – Gender – Household size – Name (Last, First)

Gender: female Input:

51.3%

Output:

51.3%

Ages (varying linearly over interval): [0,49] [50,64] [65,79] [80,100] Household: couple couple + 1 couple + 2 couple + 3 couple + 4 couple + grandparent: single woman single mom + 1 single mom + 2 single mom + 3 single mom + 4 single man single dad + 1 single dad + 2 single dad + 3 Size:

2 3 4 5 6 3 1 2 3 4 5 1 2 3 4

Probability:

0.30

0.08

0.06

0.04

0.01

0.16

0.07

0.05

0.03

0.12

0.01

0.005

input:

67.5% 18.0% 12.0% 2.5%

cdf:

0.300

0.380

0.440

0.480

0.520

0.525

0.685

0.755

0.805

0.835

0.865

0.985

0.990

0.995

1.000

output:

67.5% 17.9% 12.1% 2.5%

Expectation:

0.6

0.24

0.2

0.24

0.015

0.16

0.14

0.15

0.12

0.15

0.12

0.01

0.015

0.02

2.42

Beginnings of

NJ_Resident

file

County 2010 Census

County

0 0 0

Person Index Household Index Last Name

1 2 1 PREVILLE 1 PREVILLE

First Name Middle Initial

RICHARD G.

JACK J.

0 0 0 0 3 4 5 6 1 PREVILLE CHARLES X.

2 DEVEREUX SUE B.

2 DEVEREUX ANTON 2 DEVEREUX KATIE P.

0 0 0 7 8 9 10 3 WHEDBEE LINDA 4 CARVER ROBERT 4 CARVER 5 TINSLEY ELLEN C.

JENNIFER P.

Age

24 7 1 24 2 6 26 24 25 23

Gender

FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE TRUE

Worker Index Worker Type

5 worker 0 grade School 7 under 5 6 at-home-worker 7 under 5 0 grade School

Home Latitude Home Longitude

39.43937 -74.495087

6 at-home-worker 5 worker 39.43937 -74.495087

39.43937 -74.495087

6 at-home-worker 39.43937 -74.495087

4 college on campus: 40.85646 -74.197833

Task 1 # People, Lat, Lon, For each person Vital Stats RandomDraw: Age, M/F, WorkerType ,

WorkerType Index 0 1 2 3 4 5 6 7 WorkerType String:

grade school middle school high school college: commute college: on campus worker at-home worker and retired nursing home and under 5

Distribution:

100% ages [6,10] 100% ages [11,14] 100% ages [15,18] Sate-wide distribution Sate-wide distribution Drawn to match J2W Stats by County Remainder + 100% ages [65,79] 100% ages [0,5] and 100% ages [80,100]

C2C Journey2Work Task 2 Home County

Using Census Journey-to Work (J2W) Tabulations to assign Employer County

WorkCounty Destination RandomDraw: Journey2Work Work County

Home State

34 34 34 34

Home County County Name

1 Atlantic Co. NJ 1 Atlantic Co. NJ 1 Atlantic Co. NJ 1 Atlantic Co. NJ http://www.census.gov/population/www/cen2000/commuting/files/2KRESCO_NJ.xls

http://www.census.gov/population/www/cen2000/commuting/files/2KWRKCO_NJ.xls

6 6 9 9 37 L. A. Co. CA 65 Riverside Co. CA 3 Hartford Co. CT 5 Litchfield Co. CT

Work State

6 6 10 10

Work County County Name

59 Orange Co. CA 85 Santa Clara Co. CA 3 New Castle Co. DE 5 Sussex Co. DE 34 34 34 34 1 Atlantic Co. NJ 1 Atlantic Co. NJ 1 Atlantic Co. NJ 1 Atlantic Co. NJ

Workers

12 9 175 9 33 7 5 4 County 0 0 0 0 0 0 0 0 0 0 Person Index 1 Household Index Last Name First Name 1 PREVILLE RICHARD G.

Middle Initial 2 3 1 PREVILLE JACK J.

1 PREVILLE CHARLES X.

4 5 6 7 8 9 10 2 DEVEREUX SUE 2 DEVEREUX ANTON 2 DEVEREUX KATIE 3 WHEDBEE LINDA 4 CARVER 4 CARVER 5 TINSLEY ELLEN B.

ROBERT Z.

JENNIFER P.

Age Gender 24 FALSE 7 FALSE 1 FALSE 24 TRUE 2 FALSE 6 TRUE 26 TRUE 24 FALSE 25 23 TRUE TRUE Worker Index Worker Type 5 worker 0 grade School 7 under 5 Home Latitude Home Longitude 39.43937 -74.495087

Employer County 22

39.43937 -74.495087

6 at-home-worker 7 under 5 0 grade School 6 at-home-worker 5 worker 39.43937 -74.495087

39.43937 -74.495087

6 at-home-worker 39.43937 -74.495087

4 college on c ampus: 40.85646 -74.197833

Employment-Weighted Random Draw

Using Employer Data to assign a Workplace Characteristics

Name County

1 VIP SKINDEEP Atlantic 10 Acres Motel Atlantic 1001 Grand Street Investors Atlantic 1006 S Main St LLC Atlantic 11th Floor Creative Group Atlantic 123 Cab Co 123 Junk Car Removal 1400 Bar Atlantic Atlantic Atlantic 1-800-Got-Junk?

Atlantic

NAICS Code NAICS Description

81219915 Other Personal Care 72111002 Hotels & Motels Ex Casino 52399903 Misc Financial Inves 53111004 Lessors Of Res Buildg 51211008 Motion Picture Prod 48531002 Taxi Svc 45331021 Used Merch Stores 72241001 Drinking Places 56221910 Other Non-Haz Waste Disp

Employ ment

2 2 3 5 2 2 2 4 4

Latitude

39.401104

39.437305

39.619732

39.382399

39.359014

39.391600

39.361705

39.411266

39.423954

Longitude

-74.514228

-74.485488

-74.786654

-74.530785

-74.430151

-74.521715

-74.435779

-74.570083

-74.557892

Using School Data to Assign School Characteristics

Assigning a Daily Activity (Trip) Tour to Each Person

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 ATL BER BUR CAM CAP CUM ESS GLO HUD HUN MER MID MON MOR OCE PAS SAL SOM SUS UNI WAR NYC PHL BUC SOU NOR WES ROC Total: 274,549

905,116 448,734 513,657 97,265 156,898 783,969 288,288 634,266 128,349 366,513 809,858 630,380 492,276 576,567 501,226 66,083 323,444 149,265 536,499 108,692 86,418 18,586 99,865 13,772 5,046 6,531 32,737 9,054,849

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

Task 8 Trip Type; SIC Distribution of Arrival/Departure Times Time Generator: RandomDraw: Time Distribution

Assigning Trip Departure Times

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

Home County

ATL BER BUC BUR CAM CAP CUM ESS GLO HUD HUN MER

Trips #

936,585 3,075,434 250,006 1,525,713 1,746,906 333,690 532,897 2,663,517 980,302 2,153,677 437,598 1,248,183

MID MON MOR NOR NYC OCE PAS PHL ROC SAL SOM SOU SUS

2,753,142 2,144,477 1,677,161 12,534 215,915 1,964,014 1,704,184 46,468 81,740 225,725 1,099,927 34,493 508,674

UNI WAR

1,824,093 371,169

WES

16,304

Total 32,862,668 All Trips TripMiles AverageTM Miles Miles

27,723,931 40,006,145 9,725,080 37,274,682 27,523,679 11,026,874 18,766,986 29,307,439 23,790,798 18,580,585 13,044,440 22,410,297 47,579,551 50,862,651 33,746,360 900,434 4,131,764 63,174,466 22,641,201 1,367,405 2,163,311 8,239,593 21,799,647 2,468,016 16,572,792 21,860,031 13,012,489 477,950

590,178,597

32.2

13.3

29.4

26.5

36.5

19.8

71.6

32.6

12.0

35.1

29.3

19.3

29.6

13.0

38.9

24.4

15.8

33.0

35.2

11.0

24.3

8.6

29.8

18.0

17.3

23.7

20.1

71.8

19.1

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}

NJ_PersonTrip file

Warren County

Population: 108,692

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!)

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

• • • •

“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

• • • •

“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

•

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

NJ State Grid Zoomed-In Grid of Mercer

Pixelating the State with half-mile Pixels

xPixel = floor{108.907 * (longitude + 75.6)} yPixel = floor{138.2 * (latitude – 38.9))

O O { oYpixel, oXpixel, oTime (Hr:Min:Sec)

dYpixel, dXpixel, Exected: dTime } P 1 D

O Common Destination (CD) CD=1p: Pixel -> Pixel (p->p) Ride-sharing P 1

O P 1 aTaxiMiles = L AVO = PersonMiles/aTaxiMiles = 3

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

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

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

{ (or PHL or any Pixel containing a Train station) } NYC D aTaxiTrip O Princeton Train Station

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.

P 2 O CD= 3p: Pixel ->3Pixels Ride-sharing P 1

O CD= 3p: Pixel ->3Pixels Ride-sharing P 1 P 3 P 5

O CD= 3sp: Pixel ->3SuperPixels Ride-sharing P 1 SP 1 SP 4 P 3 P 6 SP 5 P 5 SP 6

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

c

Results

DD = 0 DD = 1 DD = 2 DD = 3 DD = 4 DD = 5

Salem County - True Average Vehicle Occupancy

CD = 0 CD = 1 CD = 2 CD = 3 CD = 4 CD = 5 1.00

1.00

1.02

1.04

1.05

1.06

1.07

1.02

1.42

1.55

1.63

1.69

1.74

1.02

1.53

1.73

1.87

1.98

2.07

1.02

1.56

1.80

1.97

2.11

2.22

2.30

1.02

1.57

1.83

2.02

2.17

DD = 0 DD = 1 DD = 2 DD = 3 DD = 4 DD = 5

Hudson County - True Average Vehicle Occupancy

CD = 0 CD = 1 CD = 2 CD = 3 CD = 4 CD = 5 1.00

1.00

1.01

1.09

1.12

1.17

1.20

1.23

1.10

1.95

2.07

2.16

2.23

2.29

1.11

2.56

2.84

3.00

3.12

3.22

1.11

2.92

3.40

3.67

3.86

4.02

4.67

1.11

3.12

3.77

4.16

4.45

Results

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

Public Schools in the US

Quick stats on Public Schools (2011)

60 000 50 000 40 000 30 000 20 000 10 000 Primary

School Type

Primary Middle High Other No Answer

Total

Middle

# of CHARTER

2,584 615 1,316 1,145 564

6,224

High Other

# of PUBLIC

51,793 16,332 19,762 5,847 3,525

97,259

No Answer

Total

54,377 16,947 21,078 6,992 4,089

103,483

PUBLIC CHARTER

Nation-Wide Businesses

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

1 2 3

45 47

48 49 50 51

State

California Texas Florida

New York

Pennsylvania

New Jersey

Washington DC Rhode Island

North Dakota Delaware Vermont Wyoming

Sales Volume No. Businesses

$1,889 1,579,342 $2,115 $1,702

$1,822

999,331 895,586

837,773

$2,134

$1,919

$1,317 $1,814

$1,978 $2,108 $1,554 $1,679 550,678

428,596

49,488 46,503

44,518 41,296 39,230 35,881

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 ????

Discussion!

Thank You

[email protected]

www.SmartDrivingCar.com

Scope of “Automated Vehicles” Tesla Car Transporter Rio Tinto Automated Truck Rio Tinto Automated Train Automated Guided Vehicles Tampa Airport 1 st APM 1971 Copenhagen Metro Elevator Mercedes Intelligent Drive Rivium 2006 -> Milton Keynes, UK CityMobil2 Heathrow PodCar Aichi, Japan, 2005 Expo

•

SmartDrivingCars: Post-DARPA Challenges

(2010-today) VisLab.it (U. of Parma, Italy) – Had assisted Oshkosh in DARPA Challenges – Stereo vision + radars (Video has no sound)

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

Click images to view videos S-Class WW Launch May ‘13 MB @ Frankfurt Auto Show Sept ‘13 MB Demo Sept ‘13 Intelligent Drive (active steering Volvo Truck Emergency braking  )