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
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)
SmartDrivingCars & Trucks
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”
Value Proposition
Market Force
Societal
Implications
Zero
Zero
Zero
Zero
1
“Cruise
Control”
Infinitesimal
Some Comfort
Infinitesimal
Infinitesimal
2
“CC +
Emergency
Braking”
Infinitesimal
Some Safety
Small; Needs help From
“Flo & the Gecko”
(Insurance Industry)
“20+%” fewer
accidents; less severity;
fewer insurance claims
3 “Texting
Some
Liberation (some of the
time/places) ; much
more Safety
Consumers Pull,
TravelTainment Industry
Push
Increased car sales,
many fewer insurance
claims, Increased VMT
Always
Get to be Chauffeured;
Get to Buy Mobility
“by the Drink” rather
than “by the Bottle”
Profitable Business
Opportunity for
Utilities/Transit Companies
Personal Car becomes
“Bling” not instrument
of personal mobility,
VMT ?; Comm. Design ?
Energy, Congestion,
Environment?
0
“55 Chevy”
Machine”
4 “aTaxi “
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.
2.
3.
4.
5.
6.
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
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:
•“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
58
Average
Pop/Block
81.6
Publically available data:
• Distributions of Demographic Characteristics
– Age
– Gender
– Household size
– Name (Last, First)
Gender:
female
Input:
Output:
51.3%
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
input:
output:
67.5%
67.5%
18.0%
17.9%
12.0%
12.1%
2.5%
2.5%
Size: Probability: cdf:
Expectation:
2
0.30
0.300
0.6
3
0.08
0.380
0.24
4
0.06
0.440
0.24
5
0.04
0.480
0.2
6
0.04
0.520
0.24
3
0.01
0.525
0.015
1
0.16
0.685
0.16
2
0.07
0.755
0.14
3
0.05
0.805
0.15
4
0.03
0.835
0.12
5
0.03
0.865
0.15
1
0.12
0.985
0.12
2
0.01
0.990
0.01
3
0.005
0.995
0.015
4
0.005
1.000
0.02
2.42
Beginnings of
County
Person Household
County Index
Index
Last Name
0
1
1 PREVILLE
0
2
1 PREVILLE
0
3
1 PREVILLE
0
4
2 DEVEREUX
0
5
2 DEVEREUX
0
6
2 DEVEREUX
0
7
3 WHEDBEE
0
8
4 CARVER
0
9
4 CARVER
0
10
5 TINSLEY
First
Name
RICHARD
JACK
CHARLES
SUE
ANTON
KATIE
LINDA
ROBERT
JENNIFER
ELLEN
Middle
Initial
G.
J.
X.
B.
P.
S.
C.
Z.
P.
U.
NJ_Resident file
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
6 at-home-worker
5 worker
6 at-home-worker
4 college on campus:
Home
Latitude
39.43937
39.43937
39.43937
39.43937
39.43937
39.43937
39.43937
39.43937
39.43937
40.85646
Home
Longitude
-74.495087
-74.495087
-74.495087
-74.495087
-74.495087
-74.495087
-74.495087
-74.495087
-74.495087
-74.197833
Task 1
2010 Census
# 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]
Home
County
Using Census Journey-toWork (J2W) Tabulations to
assign Employer County
Task 2
C2C
Journey2Work
WorkCounty Destination
RandomDraw:
Journey2Work
Home Home
State County
County Name
34
1 Atlantic Co. NJ
34
1 Atlantic Co. NJ
34
1 Atlantic Co. NJ
34
1 Atlantic Co. NJ
Work
County
http://www.census.gov/population/www/cen2000/commuting/files/2KRESCO_NJ.xls
6
6
9
9
37 L. A. Co. CA
65 Riverside Co. CA
3 Hartford Co. CT
5 Litchfield Co. CT
Work Work
State County
County Name
6
59 Orange Co. CA
6
85 Santa Clara Co. CA
10
3 New Castle Co. DE
10
5 Sussex Co. DE
34
34
34
34
Workers
12
9
175
9
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/2KWRKCO_NJ.xls
Person Household
First
County Index
Index Last Name Name
0
1
1 PREVILLE RICHARD
0
2
1 PREVILLE JACK
0
3
1 PREVILLE CHARLES
0
4
2 DEVEREUX SUE
0
5
2 DEVEREUX ANTON
0
6
2 DEVEREUX KATIE
0
7
3 WHEDBEE LINDA
0
8
4 CARVER ROBERT
0
9
4 CARVER JENNIFER
0
10
5 TINSLEY ELLEN
Middle
Initial
G.
J.
X.
B.
P.
S.
C.
Z.
P.
U.
Worker
Age
Gender Index
Worker Type
24 FALSE
5 worker
7 FALSE
0 grade School
1 FALSE
7 under 5
24 TRUE
6 at-home-worker
2 FALSE
7 under 5
6 TRUE
0 grade School
26 TRUE
6 at-home-worker
24 FALSE
5 worker
25 TRUE
6 at-home-worker
23 TRUE
4 college on c ampus:
Home
Home Employer
Latitude Longitude County
39.43937 -74.495087
22
39.43937 -74.495087
39.43937 -74.495087
39.43937 -74.495087
39.43937 -74.495087
39.43937 -74.495087
39.43937 -74.495087
39.43937 -74.495087
0
39.43937 -74.495087
40.85646 -74.197833
33
7
5
4
Using Employer Data to
assign a Workplace
Characteristics
Name
NAICS Code
County
1 VIP SKINDEEP
Atlantic
10 Acres Motel
Atlantic
1001 Grand Street
Investors
Atlantic
Atlantic
11th Floor Creative
Atlantic
Group
52399903 Misc Financial
Inves
Lessors Of Res
53111004
Buildg
Motion Picture
51211008
Prod
123 Cab Co
48531002
1006 S Main St LLC
Employment-Weighted
Random Draw
NAICS
Description
Other Personal
81219915
Care
Hotels & Motels
72111002
Ex Casino
123 Junk Car
Removal
1400 Bar
1-800-Got-Junk?
Atlantic
Atlantic
Atlantic
Atlantic
Taxi Svc
Used Merch
45331021
Stores
72241001
Drinking Places
Other Non-Haz
56221910
Waste Disp
Employ
ment
Latitude
Longitude
2
39.401104
-74.514228
2
39.437305
-74.485488
3
39.619732
-74.786654
5
39.382399
-74.530785
2
39.359014
-74.430151
2
39.391600
-74.521715
2
39.361705
-74.435779
4
39.411266
-74.570083
4
39.423954
-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 274,549
BER 905,116
BUR 448,734
CAM 513,657
CAP
97,265
CUM 156,898
ESS 783,969
GLO 288,288
HUD 634,266
HUN 128,349
MER 366,513
MID 809,858
MON 630,380
MOR 492,276
OCE 576,567
PAS 501,226
SAL
66,083
SOM 323,444
SUS 149,265
UNI 536,499
WAR 108,692
NYC
86,418
PHL
18,586
BUC
99,865
SOU
13,772
NOR
5,046
WES
6,531
ROC
32,737
Total: 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
Distribution of
Arrival/Departure
Times
Trip Type; SIC
Assigning Trip
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
All Trips
Home
County
ATL
BER
BUC
BUR
CAM
CAP
CUM
ESS
GLO
HUD
HUN
MER
MID
MON
MOR
NOR
NYC
OCE
PAS
PHL
ROC
SAL
SOM
SOU
SUS
UNI
WAR
WES
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
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
1,824,093
371,169
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
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
32.2
13.3
29.4
26.5
36.5
19.8
71.6
32.6
12.0
35.1
16,304
477,950
29.3
Total 32,862,668
590,178,597
19.3
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
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
Zoomed-In Grid of Mercer
NJ State Grid
Pixelating the State
with half-mile Pixels
xPixel = floor{108.907 * (longitude + 75.6)}
yPixel = floor{138.2 * (latitude – 38.9))
An
a PersonTrip
aTaxiTrip
{oYpixel,
{oLat,
oXpixel,
oLon,oTime
oTime(Hr:Min:Sec)
(Hr:Min:Sec)
,,dYpixel,
,dLat, dLon,
dXpixel,
Exected:
Exected:
dTime}
dTime}
}
P1
O
O
D
Common Destination (CD)
CD=1p: Pixel -> Pixel (p->p) Ride-sharing
P1
O
L
TripMiles = 3L
2L
P1
O
PersonMiles = 3L
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
a PersonTrip
from NYC
An aTaxiTrip
(orTrainArrivalTime,
PHL or any Pixel containing
a TrainExected:
station)dTime}
{oYpixel, oXpixel,
dYpixel, dXpixel,
D
O
aTaxiTrip
Princeton Train Station
NYC
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.
CD= 3p: Pixel ->3Pixels Ride-sharing
P1
O
P2
CD= 3p: Pixel ->3Pixels Ride-sharing
P5
P1
O
P3
CD= 3sp: Pixel ->3SuperPixels Ride-sharing
SP5
SP1
P5
P1
O
SP4
P3
P6
SP6
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 1.02 1.02 1.02
1.00 1.02 1.42 1.53 1.56 1.57
1.00 1.04 1.55 1.73 1.80 1.83
1.00 1.05 1.63 1.87 1.97 2.02
1.00 1.06 1.69 1.98 2.11 2.17
1.00 1.07 1.74 2.07 2.22 2.30
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 1.01 1.10 1.11 1.11 1.11
1.00 1.09 1.95 2.56 2.92 3.12
1.00 1.12 2.07 2.84 3.40 3.77
1.00 1.17 2.16 3.00 3.67 4.16
1.00 1.20 2.23 3.12 3.86 4.45
1.00 1.23 2.29 3.22 4.02 4.67
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
Number of Schools in US
50,000
40,000
PUBLIC
30,000
CHARTER
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
Nation-Wide Businesses
13.6 Million Businesses
{Name, address, Sales, #employees}
Sales
Volume No. Businesses
Rank
State
1
California
$1,889
1,579,342
2
Texas
$2,115
999,331
3
Florida
$1,702
895,586
4
New York
$1,822
837,773
5
Pennsylvania
$2,134
550,678
9
New Jersey
$1,919
428,596
45
Washington DC
$1,317
49,488
47
Rhode Island
$1,814
46,503
48
North Dakota
$1,978
44,518
49
Delaware
$2,108
41,296
50
Vermont
$1,554
39,230
51
Wyoming
$1,679
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
alaink@princeton.edu
www.SmartDrivingCar.com
Scope of “Automated Vehicles”
Tesla Car Transporter
Rio Tinto Automated Truck
Rio Tinto Automated Train
Tampa Airport 1st APM 1971
Automated Guided Vehicles
Copenhagen Metro
Elevator
Mercedes Intelligent Drive
Rivium 2006 ->
Milton Keynes, UK
Aichi, Japan, 2005 Expo
CityMobil2
Heathrow PodCar
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