1. Faculty Chair, PAVE (Princeton Autonomous Vehicle Engineering
Smart Driving Cars: (remove?)
History and Evolution of
Automated Vehicles
By Alain L. Kornhauser Professor, Operations Research & Financial Engineering Director, Program in Transportation
Faculty Chair, PAVE (Princeton Autonomous Vehicle Engineering
Princeton University Board Chair, Advanced Transit Association (ATRA)
Presented at
September 20, 2013

3. Outline Discussion DELETE Slide
75 years of Automation in Surface Transportation: (1939 -> 2014)
SmartDrivingCars (“Automated Vehicles”) Concepts and Elements:
Pre DARPA Challenges
DARPA Challenges -> 2018
Ultimate Vision: Beyond 2018
Discussion
DELETE Slide

4. Scope of “Automated Vehicles”
Surface TransportationDELETE Slide
Vehicle + Running Surface (aka roadway, guideway, railway)
Vehicle
Who is the Customer: Consumer or Business (could be Private or Public Entity)
Consumer: purchases vehicle to satisfy personal needs and desires
Business: purchases vehicle to provide services to end customers
Running Surface
Consumer: Brings its own vehicle (could be automated, conventional, bicycle, feet)
Business: “owner” of running surface “purchases” vehicles to provide services to end customers (Example: Tampa Airport)
Questions/Discussion

5. Scope of “Automated Vehicles”
Surface Transportation
Vehicle + Running Surface (aka roadway, guideway, railway)
Who is the Customer for the Vehicle
Consumer or Business (could be Private or Public Entity)
Consumer: purchases vehicle to satisfy personal needs and desires
Business: purchases vehicle to provide services to end customers
Who is the Running Surface’s Customer
Consumer: Brings its own vehicle (could be automated, conventional, bicycle, feet)
Business: “owner” of running surface “purchases” vehicles to provide services to end customers (Example: Tampa Airport)
Questions/Discussion

6. Customers of Surface Transportation
Who are the Customers for the Running Surface?
Consumers: They bring their own personal vehicle (could be automated, conventional, bicycle, feet)
Can’t afford to “exclusively own” the Running Surface
Can only “rent”; contribute incrementally
Substantial critical mass/participation needed
Businesses : They bring a fleet of vehicles
If Fleet is large enough, they can afford to “exclusively own” the running surface
(Example: Tampa Airport)
Could also participate incrementally
(Example: Conventional Trucking and Bus Transit companies)
Who are the Customers for the Vehicles?
Consumer: Purchases vehicle to satisfy personal needs and desires
(Example: Why did you buy a car?)
Business: Purchases vehicle to provide services to end customers
(Ex: Enterprise Rent a Car: Conventional Car Rental (“we’ll pick you up), Car Sharing, Investor in MobilEye)
Questions/Discussion

7. Defining “Automated Vehicles”
Surface Transportation
Vehicle + Running Surface (aka roadway, guideway, railway)
Vehicle
Who is the Customer: Consumer or Business (could be Private or Public Entity)
Consumer: purchases vehicle to satisfy personal needs and desires
Business: purchases vehicle to provide services to end customers
Running Surface
Consumer: Brings its own vehicle (could be automated, conventional, bicycle, feet)
Business: “owner” of running surface “purchases” vehicles to provide services to end customers (Example: Tampa Airport)
Questions/Discussion

8. Vehicles Running Surface
Many
Vehicles
1 or Few
1 or Few
Many
Running Surface

10. Outline Automated Vehicles: 1939 -> Beyond 2018
Defining “Automated Vehicles” (aka “SmartDrivingCars”)
The 1st 75 years: (1939 -> 2014)
1939 -> DARPA Challenges
DARPA Challenges -> Today
The next 75 years: (2014 -> 2089)
Today -> 2018 (next 5 years)
Beyond 2018 (next 70 years)
Questions/Discussion

11. Scope of Surface Transportation
Vehicle +
Running Surface
(aka roadway, guideway, railway)
Questions/Discussion

12. 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 >
Milton Keynes, UK
Aichi, Japan, 2005 Expo
CityMobil2
Heathrow PodCar

13. Preliminary Statement of Policy Concerning Automated Vehicles
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

14. Automation of Transportation: In the late 60s…
Some thought that: “The automation & computer technology that took us to the moon could now revolutionize mass transit and save our cities from the onslaught of the automobile”
Donn Fichter “Individualized Automatic Transit and the City” 1964
Westinghouse Skybus Late 60’s-
PRT
APM
Automated Vehicles Operating in their own Exclusive Roadway

15. APM
Automated People Movers
Automated Vehicle, Exclusive Guideway, Long Headway (Large Vehicles)
Now exist in essentially every Major Airport and a few Major Activity Centers

16. PRT
Personal Rapid Transit
Automated Vehicle, Exclusive Guideway, Short Headway (Small Vehicles)
Starting in the early 70’s U of Minnesota became the center of PRT research focused on delivering auto-like ubiquitous mobility throughout urban areas
J. Edward Anderson
William Garrard
Alain Kornhauser
Since Demand very diffuse (Spatially and Temporally):
Many stations served by Many small vehicles
(rather than a few large vehicles).
Many stations
Each off-line with interconnected mainlines
To minimize intermediate stops and transfers
Many small vehicles
Require more sophisticated control systems,
both longitudinal and lateral.

17. PRT
Personal Raid Transit
Some early test- track success…

18. Was built and operational for many years
DFW AirTrans PRT
Was built and operational for many years

19. Remains a critical mobility system today & Planning an expansion
Morgantown 1975
Video1 Video2
Remains a critical mobility system today & Planning an expansion

20. We can Build them and operate them Safely
Today…
We can Build them and operate them Safely
Morgantown 1975
Remains a critical mobility system today & planning an expansion

21. Masdar & Heathrow are operational; Suncheon in testing
And Today…
Masdar & Heathrow are operational; Suncheon in testing
Masdar, Abu Dhabi
Heathrow PodCar
Suncheon, Korea

22. Far-term Opportunities for Driverless Transit
Each Year: students Design a NJ-wide PRT network
Objective: to effectively serve essentially all NJ travel demand (all 30x106 daily non-walk trips)
Locate Stations so that “every” demand point is within “5 minute walk” of a station; “efficiently” interconnect all stations; maintain existing NJ Transit Rail and express bus operations )
Typically:
~10,000 stations
~10,000 miles of guideway

23. County Stations Miles Atlantic 191 526 Middlesex 444 679 Bergen 1,117
878
Monmouth
335
565
Burlington
597
488
Morris
858
694
Camden
482
355
Ocean
540
1,166
Cape May
976
497
Passaic
1185
1,360
Cumberland
437
1,009
Salem
285
772
Essex
595
295
Somerset
568
433
Gloucester
412
435
Sussex
409
764
Hudson
467
122
Union
577
254
Hunterdon
405
483
Warren
484
Mercer
413
403
Total
11,295
12,261
Marginal

24. Far-term Opportunities for Driverless Transit
Each Year: students Design a NJ-wide PRT network
Objective: to effectively serve essentially all NJ travel demand (all 30x106 daily non-walk trips)
Locate Stations so that “every” demand point is within “5 minute walk” of a station; “efficiently” interconnect all stations; maintain existing NJ Transit Rail and express bus operations )
Typically:
~10,000 stations $2.5M/station………….. ~$ 25B
~10,000 miles of $10M/mile… ~$ 100B
~750,000 PRT vehicles: @ $100K/vehicle … ~$ 75B
Optimistic Capital Cost: …………………………… ~$ 200B
NJ’s Personal Expenditures for Mobility: ….. ~$ 25B/year

25. But implementation progress is excruciatingly slow …
Many years ago…The Executive Director of APTA put his arm around me and said:
“Alain…
Personal Rapid Transit (PRT) is the ‘System of the Future’,
And…..
Always Will Be!!!

26. What he was saying was…
Final Region-wide system would be really great, but…
Any great Final System MUST evolve from some great (feasible) initial system and MUST be great (feasible) at every step along the way, otherwise…
It will always be “a system of the future”.
The dedicated grade-separated guideway infrastructure requirement of PRT may simply be too onerous and risky for it to be feasible let alone great at any point except at the ultimate nirvana which is unreachable. Yipes!

27. What about the Personal Car (Vehicles purchased by consumers for personal mobility)

28. SmartDrivingCars: Pre-DARPA Challenges
Focused on Creating New Roadways
To be used Exclusively by Automated Vehicles
GM 1939 World’s Fair
Zworykin & RCA-Sarnoff in Princeton, Late 50s*
* VK Zworykin & L Flory “Electronic Control of Motor Vehicles on Highways” Proc. 37th Annual Mtg Highway Research Board, 1958
Cruise Control (1958)
1945 Invented by Ralph Teetor
1958 Chrysler Imperial 1st intorduction
GM first offered on the 1959 model year Cadillac.

29. SmartDrivingCars: Pre-DARPA Challenges
Anti-lock Braking Systems (ABS) (1971) (wikipedia)
Chrysler & Bendix Corporation, introduced a computerized, three-channel, four-sensor all-wheel[6] ABS called "Sure Brake" for its 1971 Imperial.
Ford added an antilock braking system called "Sure-track" to the rear wheels of Lincoln Continentals as a standard option
General Motors :"Trackmaster" rear-wheel only ABS . An option on rear-wheel drive Cadillac models and the Oldsmobile Toronado.
“Wire Sniffing” Cars
Robert E OSU, Early 70s*
* “A Headway Safety Policy for Automated Highway Operations” R.E. Fenton 1979

30. SmartDrivingCars: Pre-DARPA Challenges
National Automated Highway System Research Program (NAHSRP) ( )
Goal to develop specifications for a fully automated highway system
Benefits:
#1. Roadway capacity (led to focus on platooning)
#2. Safety
#3…. Robust to Weather, Mobility, Energy, Land Use, Commercial efficiency, travel time
Focused on automated roadways serving only fully automated vehicles.
References
The National Automated Highway System That Almost Was;
TRB Special Report 253 “National Automated Highway System Research Program: A Review”
Cheon, Sanghyun “An overview of Automated Highway Systems (AHS) and the Social and Institutional Challenges they Face” (Excellent critique, well worth reading)
Video Summary of NAHSRP (compare the tone to what Mercedes did in Frankfurt. We’ve come a very long way!)
Program Cancelled! No Feasible Initial Condition
No reason to build Automated Highway If there are no Automated Cars & visa versa.

31. Evolution of SmartDrivingCars Concept
2004
CMU “Red Team”
2005
StanfordRacing Team
2007
CMU “Tartan Team”
DARPA Grand Challenge Created in response to a Congressional and DoD mandate:
a field test intended to accelerate R&D in autonomous ground vehicles
that will help save American lives on the battlefield. 

32. 2005
2007
Slide 49
Link to Presentation
Not Easy
Old House
2005
2007

33. Prospect Eleven & 2005 Competition

34. the making of a monster

35. 2005 Grand Challenge

36. Objective Constraints Guiding Principles
Enrich the academic experience of the students
Constraints
Very little budget
Simplicity
Guiding Principles

37. a homemade set of gears to drive their pickup. I could see from the
“Unlike the fancy “drive by wire” system employed by Stanford and VW, Princeton’s students built
a homemade set of gears to drive their pickup. I could see from the
electronics textbook they were using that they were learning as they went.”

38. Fall 2004

40. Fall 2005

42. It wasn’t so easy…

43. (a video presentation)
Pimp My Ride
(a video presentation)

44. Achievements in the
2005

45. Link to GPS Tracks

46. Participation in the 2007

47. Prospect12_TestRun

48. Today.. Continuing to work on Prospect 12
Vision remains our focus for depth mapping, object recognition and tracking
Objective is to pass NJ Driver’s Test.

49. Fundamental Contribution of DARPA:
2007
CMU “Tartan Team”
2004
CMU “Red Team”
2005
StanfordRacing Team
Change the mind set to: focus on the Individual Vehicle
Make it “Autonomous”:
Able to “Drive” without any outside help,
behavior modification of conventional roadway users and,
physical changes in existing roadways.

50. SmartDrivingCars: Post-DARPA Challenges (2007 -> today)
Driverless (Level 4); Non-Exclusive Roadway; Low Speed (< 25kph)
CityMobil (2008 -> 2012)
Low speed (< 25 kph),
Lidar pedestrian detection
Non-exclusive simple roadway
just lane markings
Includes pedestrians & low speed vehicles
La Rochelle (CyberCars/Cybus/INRIA
CityMobil2 (2012->…)
Slated for 5 city deployments + Singapore + ???

51. Where Are We with Automated Cars/SmartDrivingCars?

52. SmartDrivingCars: Post-DARPA Challenges (2010-today)
Google Self-Driving Car
Adopted Sebastian Thrun from Stanford DARPA Team +
“Standard Lexus” w/ electronically controlled Brakes, Throttle & Steering (BTS):
Google “straps on”
Sensors: LIDAR’s “depth point cloud”, GPS, Radars & Cameras
Communications: to access 3D digital map data
Software & Computer: Converts Sensor Output into BTS Inputs

53. Has Self-Driven ~ 500,000 miles @ “Level 3: Limited Self-Driving Automation”
But…Hardware too expensive and Reliance on 3D Google Maps is “non-elegant”

54. 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)

55. SmartDrivingCars: Post-DARPA Challenges (2010-today)
University Efforts:
CMU + GM
Stanford + VW
Va Tech + Google?
Oxford + Nissan
Parma + Fiat
Princeton (PAVE) …

56. Google has “shown” “we can build it!”
What is the “Pull”? Why do this?
Google has “shown” “we can build it!”
But, Don Draper (Mad Man) Has Done a Great Job Convincing Us that We Love to Drive! (Or, do we?? Are we any good at it?)
What will Consumers Buy?

57. The Automobile’s 1st 125 Years (1886-2011)
Benz patent st Automobile
Benz Circa 2011
Delivered: Enormous Personal Freedom & Mobility
But…Safe Operation Requires Continuous Vigilance

58. We Love the Freedom & Mobility
But…Continuous Vigilance is an unrealistic requirement for drivers

59. Txtng while driving is out of control…

60. TravelTainment Industry Wants Everyone’sAttention

61. In In 717 out of 723 accidents ((99%)
In In 717 out of 723 accidents ((99%)
“In 717 out of 723 crashes (99%), a driver behavioral error caused or contributed to the crash”

62. Early Estimate of Motor Vehicle Traffic Fatalities in 2012

63. But… Not Likely to be Effective
Response is Laudable
Kirkland, WA
But… Not Likely to be Effective

64. (air bags, seat belts, crash worthiness, …)
Up to today:
Primarily concerned with safety standards associated with Crash Mitigation
(air bags, seat belts, crash worthiness, …)

65. Preliminary Statement of Policy Concerning Automated Vehicles
Extending its vehicle safety standards from Crash Mitigation to Crash Avoidance with Aim at Full Self-Driving Automation
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

66. Preliminary Statement of Policy Concerning Automated Vehicles
What the Levels Deliver:
Levels 1 -> 3: Increased Safety, Comfort & Convenience

67. “Level 2- Combined Automation wit Constant Vigilance ”
Where Are We Now?
Available in ShowRooms for Consumers
“Level 2- Combined Automation wit Constant Vigilance ”

68. Active Lane-Keeping Assist (braking not steering  )
Click images to view videos
MB Frankfurt Intro
Active Lane-Keeping Assist (braking not steering  )
Intelligent Drive (active steering  )
Volvo Truck Emergency braking

69. Enhancements to Driver Asst. Package
History and Development of DISTRONIC: Price reduction, intelligent packaging and availability cross-carline
MY 2000
MY Current
MY Future
DISTRONIC PLUS
Autonomous Braking Intervention
In “Driver Assistance Package” with Blind Spot Assist/Lane Keeping Assist $2,950
Available Cross-Carline on almost every model
Enhancements to Driver Asst. Package
Steering Assist
BAS with Cross-Traffic Assist
PRE-SAFE Brake with Pedestrian Detection
PRE-SAFE PLUS – protection during rear collisions
In “Driver Assistance Package” with Blind Spot Assist/Lane Keeping Assist +$2,800
Only available on S/E
Introduce DISTRONIC
Adaptive Cruise Control
In package for +$3,700
Only available on S/CL

70. DOT HS 810 767 Pre-Crash Scenario Typology for Crash Avoidance Research
More on Google: Levandowski Presentation

71. With Mercedes the Market Leader in “Level 2-” and an incremental price tag that can be absorbed by a Price Leading Insurance Company, then other automakers will be enticed to follow which should lead to:
Viral adoption by the car buying public
“Moore’s Law type of price/performance improvement
Market-driven Transition to “Level 2” and “Level 3” at same or even lower price structure
Adoption and enhancement rates that are comparable to that enjoyed by airbags

72. Collision and Lane-Departure Warning (Stereo Image Processing Package)
Where Are We Now?
After-market:
Collision and Lane-Departure Warning
(Stereo Image Processing Package)
A New Product Category:  the PCwD !
Personal Collision-warning Device (PCwD)
Available at under $850
Consumer After-market version of Collision & Lane-Departure Systems in:

73. What are the Current Challenges?

74. Current State of SmartDrivingCars
Much of the public interest has been promoted by the car.
It is not driverless…
Not yet
But substantial advancements have focused on:
Development of a self-driving vehicle that can operate in the existing environment.
Motivated by fact: >90% of road traffic accidents involve human error. So… remove the human from the loop.
Often… People really do not want to drive.
Driven over 500,000 miles in self-driving mode

75. Expected Implications of “Google Cars”
Using AAA Liability Rates per Fatality and Injury
DOT HS Pre-Crash Scenario Typology for Crash Avoidance Research

76. Expected Implications of “Google Cars”
Using AAA Liability Rates per Fatality and Injury

77. Expected Implications of “Google Cars”
Using AAA Liability Rates per Fatality and Injury

78. Expected Implications of “Google Cars”
$475/yr are “Pass-through” Dollars
Assume: Emergence of “Price Leaders”
$450/yr Discount for Me
$25/yr for Flo or the Gecko or ???
To enhance “Combined Ratio”
Could Discount Finance: ??

79. What’s in the Showroom Today?
A number of car companies have been offering active Forward Collision Avoidance Systems for several years
Volvo
Acura
Mercedes
Subaru …

80. Preliminary Results are Disappointing

81. But what has been sold so far doesn’t really work…

85. Tested: Autonomous Emergency Braking (AEB) Systems

86. Tested: Autonomous Emergency Braking (AEB) Systems
Model
Score:
City
Rating:
Inter-Urban
Mercedes-Benz
E-Class
3.0 points
Good (video)
2.7 points
Good
Volvo V40
2.1 points
2.2 points
Mitsubishi Outlander
1.9 points
Adequate
Volvo XC60
Adequate (video)
- - -
Fiat 500L
1.8 points
Ford Focus
1.7 points
Volkswagen Golf
Honda Civic
0.44 points
Marginal (video)

87. However, It seems that some manufacturers are finally ….
Getting Serious about Collision Avoidance
And Serious about using it sell cars
And Serious about having it work

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

89. Enhancements to Driver Asst. Package
History and Development of DISTRONIC: Price reduction, intelligent packaging and availability cross-carline
MY 2000
MY Current
MY Future
DISTRONIC PLUS
Autonomous Braking Intervention
In “Driver Assistance Package” with Blind Spot Assist/Lane Keeping Assist $2,950
Available Cross-Carline on almost every model
Enhancements to Driver Asst. Package
Steering Assist
BAS with Cross-Traffic Assist
PRE-SAFE Brake with Pedestrian Detection
PRE-SAFE PLUS – protection during rear collisions
In “Driver Assistance Package” with Blind Spot Assist/Lane Keeping Assist +$2,800
Only available on S/E
Introduce DISTRONIC
Adaptive Cruise Control
In package for +$3,700
Only available on S/CL

90. Near Term Opportunities
DOT HS Pre-Crash Scenario Typology for Crash Avoidance Research
$475 are Pass-through Dollars

91. Expected Implications of “Google Cars”
$475/yr are “Pass-through” Dollars
Assume: Emergence of “Price Leaders”
$450/yr Discount for Me
$25/yr for Flo or the Gecko or ???
To enhance “Combined Ratio”
Could Discount Finance: ??

92. Could “Pass-through” Finance Mercedes Intelligent Drive??
(Available in ‘14 S-Class)
May well Deliver 2/3rds the safety of Google Car
$475 Pass-through becomes: $320
$20/yr for Flo or the Gecko
$300/yr to Mercedes
Therefore: I get Intelligent Drive for Free (thank you Google) !! Plus:
Prob. of my car killing me is reduced factor: 2/3*.81= 0.54 (half)
Prob. of my car injuring me is reduced factor: 2/3*.65= 0.44
I “Save” my expected “deductible self-insurance”: $247/yr
I have more Comfort
I have more Convenience
I have “Anxiety” relief
What a great Business Case! I’m a Buyer!

93. Expected Implications…
With Mercedes as the Market Leader offering “Level 2-” SmartDriving Technology at an incremental price tag that can be absorbed by a “Price Leading” Insurance Company, should lead to:
Other automakers will be enticed to follow (race seems to have already started)
Viral adoption by the car buying public
“Moore’s Law type of price/performance improvement
Market-driven Transition to “Level 2” and “Level 3” at same or even lower price structure
Adoption and enhancement rates that are comparable to that enjoyed by airbags may be likely

94. Thank You

95. Where might We End Up?

96. Preliminary Statement of Policy Concerning Automated Vehicles
What the Levels Deliver:
Levels 1 -> 3: Increased Safety, Comfort & Convenience
Level 4 (Driverless Opportunity) : Mobility, Efficiency, Equity
Revolutionizes “Mass Transit” by Greatly Extending the Trips that can be “zero” cost of Labor.
(That was always the biggest “value” of PRT; zero labor cost for even zero-occupant trips)

97. Far-term Opportunities for Driverless Transit
Biggest Issues have been:
How to get started
How to evolve
Cost & complexity of guideway
What if ????
automatedTaxi (aTaxi) used existing streets
Minimal Infrastructure Requirement (minimal public meetings & $$)
Potential Service Offerings
Conventional Taxi service
Great Mobility, but no “Mass Transit” societal benefits: congestion, energy, environment
Curb-side aTaxi stands offering on-demand walk-up service (PRT-like service)
Good mobility, but what are the societal implications
Door2Door Coordinated Shared Ride (“Smart Para-Transit (SPT)”)
Since the above serve individual trips (sometimes simultaneously)
Must have a precise representation of individual trips

98. Most every day… Almost 9 Million NJ residents
0.25 Million of out of state commuters
Make 30+ Million trips
Throughout the 8,700 sq miles of NJ
Where/when do they start?
Where do they go?
Does anyone know???
I certainly don’t
Not to sufficient precision for credible analysis

99. I’ve Tried… I’ve harvested one of the largest troves of GPS tracks
Literally billions of individual trips,
Unfortunately, they are spread throughout the western world, throughout the last decade.
Consequently, I have only a very small ad hoc sample of what happens in NJ on a typical day. (and no way to scale it up)

101. Trip Synthesizer Motivation – Publicly available data do not contain:
Project Overview
Trip Synthesizer
Motivation – Publicly available data do not contain:
Spatial precision
Where are people leaving from?
Where are people going?
Temporal precision
At what time are they travelling?
There is no data telling us when people are leaving their house, when they are coming back, where they are going in between
ORF 467 Fall 2012

102. Why do I want to know every trip?
Academic Curiosity
If offered an alternative, which ones would likely “buy it” and what are the implications.
More specifically:
If an alternative transport system were available, which trips would be diverted to it and what operational requirements would those trip impose on the new system?
In the end…
a transport system serves individual decision makers. It’s patronage is an ensemble of individuals,
I would prefer analyzing each individual trip patronage opportunity.

103. Synthesize from publically 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

104. Overview of Data Production
Project Overview
Overview of Data Production
Generate population
Assign work places
Assign schools
Assign tours / activity patterns
Assign other trips
Assign arrival / departure times
ORF 467 Fall 2012

105. Creating the NJ_Resident file
for “every” NJ Traveler on a typical day
NJ_Resident file
Start with Publically available data:

106. 2010 Population census @Block Level
8,791,894 individuals distributed 118,654 Blocks.
County
Population
Census Blocks
Median Pop/ Block
Average Pop/Block
ATL
274,549
5,941 26 46
BER
905,116
11,171 58 81
BUR
448,734
7,097 41 63
CAM
513,657
7,707 47 67
CAP
97,265
3,610 15 27
CUM
156,898
2,733 34 57
ESS
783,969
6,820 77
115
GLO
288,288
4,567 40
HUD
634,266
3,031
176
209
HUN
128,349
2,277 31 56
MER
366,513
4,611 51 79
MID
809,858
9,845 50 82
MON
630,380
10,067 39
MOR
492,276
6,543 45 75
OCE
576,567
10,457 55
PAS
501,226
4,966 65
101
SAL
66,083
1,665
SOM
323,444
3,836 84
SUS
149,265
2,998 28
UNI
536,499
6,139 61 87
WAR
108,692
2,573 23 42
Total
8,791,894
118,654
74.1

107. Publically available data:
Distributions of Demographic Characteristics
Age
Gender
Household size
Name (Last, First)
Ages (varying linearly over interval):
input:
output:
[0,49]
67.5%
[50,64]
18.0%
17.9%
[65,79]
12.0%
12.1%
[80,100]
2.5%
Household:
Size:
Probability:
cdf:
Expectation:
couple 2
0.30
0.300
0.6
couple + 1 3
0.08
0.380
0.24
couple + 2 4
0.06
0.440
couple + 3 5
0.04
0.480
0.2
couple + 4 6
0.520
couple + grandparent:
0.01
0.525
0.015
single woman 1
0.16
0.685
single mom + 1
0.07
0.755
0.14
single mom + 2
0.05
0.805
0.15
single mom + 3
0.03
0.835
0.12
single mom + 4
0.865
single man
0.985
single dad + 1
0.990
single dad + 2
0.005
0.995
single dad + 3
1.000
0.02
2.42
Gender:
Input:
Output:
female
51.3%

108. Beginnings of NJ_Resident file
County
Person Index
Household Index
Last Name
First Name
Middle Initial
Age
Gender
Worker Index
Worker Type
Home Latitude
Home Longitude 1
PREVILLE
RICHARD G. 24
FALSE 5
worker 2
JACK J. 7
grade School 3
CHARLES X.
under 5 4
DEVEREUX
SUE B.
TRUE 6
at-home-worker
ANTON P.
KATIE S.
WHEDBEE
LINDA C. 26 8
CARVER
ROBERT Z. 9
JENNIFER 25 10
TINSLEY
ELLEN U. 23
college on campus:
County
2010 Census
# People,
Lat, Lon,
For each person
Vital Stats
RandomDraw:
Age, M/F, WorkerType,
Task 1
WorkerType Index
WorkerType String:
Distribution:
grade school
100% ages [6,10] 1
middle school
100% ages [11,14] 2
high school
100% ages [15,18] 3
college: commute
Sate-wide distribution 4
college: on campus 5
worker
Drawn to match J2W Stats by County 6
at-home worker and retired
Remainder + 100% ages [65,79] 7
nursing home and under 5
100% ages [0,5] and 100% ages [80,100]

109. WorkCounty Destination RandomDraw:
Using Census Journey-to-Work (J2W) Tabulations to assign Employer County
WorkCounty Destination RandomDraw:
Journey2Work
Home
County
C2C
Work
Task 2
Home State
Home County
County Name
Work State
Work County
Workers 34 1
Atlantic Co. NJ 6 59
Orange Co. CA 12 85
Santa Clara Co. CA 9 10 3
New Castle Co. DE
175 5
Sussex Co. DE 37
L. A. Co. CA 33 65
Riverside Co. CA 7
Hartford Co. CT
Litchfield Co. CT 4
County
Person Index
Household Index
Last Name
First Name
Middle Initial
Age
Gender
Worker Index
Worker Type
Home Latitude
Home Longitude
Employer County 1
PREVILLE
RICHARD G. 24
FALSE 5
worker 22 2
JACK J. 7
grade School 3
CHARLES X.
under 5 4
DEVEREUX
SUE B.
TRUE 6
at-home-worker
ANTON P.
KATIE S.
WHEDBEE
LINDA C. 26 8
CARVER
ROBERT Z. 9
JENNIFER 25 10
TINSLEY
ELLEN U. 23
college on c ampus:

110. Using Employer Data to assign a Workplace Characteristics
Name
County
NAICS Code
NAICS Description
Employment
Latitude
Longitude
1 VIP SKINDEEP
Atlantic
Other Personal Care 2
10 Acres Motel
Hotels & Motels Ex Casino
1001 Grand Street Investors
Misc Financial Inves 3
1006 S Main St LLC
Lessors Of Res Buildg 5
11th Floor Creative Group
Motion Picture Prod
123 Cab Co
Taxi Svc
123 Junk Car Removal
Used Merch Stores
1400 Bar
Drinking Places 4
1-800-Got-Junk?
Other Non-Haz Waste Disp
Employment-Weighted
Random Draw

111. Using School Data to Assign School Characteristics

112. 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

113. Creating the NJ_PersonTrip file
“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
Start with
NJ_ResidentTrip file
NJ_Employment file
Readily assign trips between Home and Work/School
Trip Activity -> Stop Sequence
Home, Work, School characteristics synthesized in NJ_Resident file

114. Assigning a Daily Activity (Trip) Tour to Each Person

115. 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

116. Assigning Trip Departure Times
Distribution of
Arrival/Departure
Times
Trip Type; SIC
Time Generator:
RandomDraw:
Time Distribution
Trip Departure time
(SeconsFromMidnight)
Task 8
Assigning Trip Departure Times
“Bell” Time
Time 1
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

117. NJ_PersonTrip file 9,054,849 records
All Trips
Home County
Trips
TripMiles
AverageTM #
Miles
ATL
936,585
27,723,931
29.6
BER
3,075,434
40,006,145
13.0
BUC
250,006
9,725,080
38.9
BUR
1,525,713
37,274,682
24.4
CAM
1,746,906
27,523,679
15.8
CAP
333,690
11,026,874
33.0
CUM
532,897
18,766,986
35.2
ESS
2,663,517
29,307,439
11.0
GLO
980,302
23,790,798
24.3
HUD
2,153,677
18,580,585
8.6
HUN
437,598
13,044,440
29.8
MER
1,248,183
22,410,297
18.0
MID
2,753,142
47,579,551
17.3
MON
2,144,477
50,862,651
23.7
MOR
1,677,161
33,746,360
20.1
NOR
12,534
900,434
71.8
NYC
215,915
4,131,764
19.1
OCE
1,964,014
63,174,466
32.2
PAS
1,704,184
22,641,201
13.3
PHL
46,468
1,367,405
29.4
ROC
81,740
2,163,311
26.5
SAL
225,725
8,239,593
36.5
SOM
1,099,927
21,799,647
19.8
SOU
34,493
2,468,016
71.6
SUS
508,674
16,572,792
32.6
UNI
1,824,093
21,860,031
12.0
WAR
371,169
13,012,489
35.1
WES
16,304
477,950
29.3
Total
32,862,668
590,178,597
19.3
 9,054,849 records
One for each person in NJ_Resident file
Specifying 32,862,668 Daily Person Trips
Each characterized by a precise
Origination, Destination and Departure Time

119. “Pixelated” New Jersey
(“1/2 mile square; 0.25mi2)
aTaxi “PRT” Concept
During peak demand aTaxis operate between aTaxiStands
Autonomous vehicles wait for walk-up customers
Located in “center” of each pixel (< ~ ¼ mile walk)
Departure is Delayed to facilitate ride-sharing
Focus of Analysis:
what is the ride-share potential?
Ridesharing delivers:
Congestion relief
Energy savings
Reduced costs/passenger
Environmental sustainability

120. New Jersey Summary Data
Item
Value
Area (mi2)
8,061
# of Pixels Generating at Least One O_Trip
21,643
Area of Pixels (mi2)
5,411
% of Open Space
32.9%
# of Pixels Generating 95% of O_Trips
9,519
# of Pixels Generating 50% of O_Trips
1,310
# of Intra-Pixel Trips
447,102
# of O_Walk Trips
1,943,803
# of All O_Trips
32,862,668
Avg. All O_TripLength (miles)
19.6
# of O_aTaxi Trips
30,471,763
Avg. O_aTaxiTripLength (miles)
20.7
Median O_aTaxiTripLength (miles)
12.5
95% O_aTaxiTripLength (miles)
38.0

121. Warren County
Population: 108,692
Intra-pixel Trips

124. “Pixelated” New Jersey
(“1/2 mile square; 0.25mi2)
aTaxi Concept
During peak demand aTaxis operate between aTaxiStands
Autonomous vehicles wait for walk-up customers
Located in “center” of each pixel (max ¼ mile walk)
Departure is Delayed to facilitate ride-sharing
Focus of Analysis:
what is the ride-share potential?
Ridesharing delivers:
Congestion relief
Energy savings
Reduced costs/passenger
Environmental sustainability

125. Warren County
Population: 108,692
Intra-pixel Trips

127. What about Manhattan?

128. Initial Deployment… Needs to be Driverless Doesn’t Need To Be…
With Excellent Pedestrian Recognition
Doesn’t Need To Be…
Fast
Everywhere
Let’s start Slow and Narrow:
Like CityMobile2…
Say mph, Along a Corridor

129. What About……

130. What About…… Driverless electric shuttle to be trialled in Singapore
(video of Luxembourg Demonstration) 

132. What About……

133. Discussion!
Thank You

134. NJ Transit
Train Station
“Consumer-shed”

135. “Pixelated” New Jersey
(“1/2 mile square; 0.25mi2)
aTaxi Concept – (PRT) Model
Personal Rapid Transit Model
aTaxi Concept – SPT Model
Smart Para Transit Transit Model
Ref:

136. New Jersey Summary Data
Item
Value
Area (mi2)
8,061
# of Pixels Generating at Least One O_Trip
21,643
Area of Pixels (mi2)
5,411
% of Open Space
32.9%
# of Pixels Generating 95% of O_Trips
9,519
# of Pixels Generating 50% of O_Trips
1,310
# of Intra-Pixel Trips
447,102
# of O_Walk Trips
1,943,803
# of All O_Trips
32,862,668
Avg. All O_TripLength (miles)
19.6
# of O_aTaxi Trips
30,471,763
Avg. O_aTaxiTripLength (miles)
20.7
Median O_aTaxiTripLength (miles)
12.5
95% O_aTaxiTripLength (miles)
38.0

138. State-wide automatedTaxi (aTaxi)
Serves essentially all NJ travel demand (32M trips/day)
Shared ridership potential:

139. State-wide automatedTaxi (aTaxi)
Serves essentially all NJ travel demand (32M trips/day)
Shared ridership potential:

140. State-wide automatedTaxi (aTaxi)
Fleet size (Instantaneous Repositioning)

141. State-wide automatedTaxi (aTaxi)
Abel to serve essentially all NJ travel demand (32M trips/day)
Shared ridership allows
Peak hour; peak direction: Av. vehicle occupancies to can reach ~ 3 p/v and eliminate much of the congestion
Essentially all congestion disappears with appropriate implications on the environment
Required fleet-size under 2M aTaxis (about half)
(3.71 registered automobiles in NJ (2009)

142. Discussion!

143. Thank You

145. “Manhattan Customer-shed” Regions for NJ Transit Train Stations
Metro Park
Metuchen
Edison
New Brunswick
Yellow Lines connect 0.25 mi2 areas to nearest NJT Train Station
where Distance is a
“Manhattan Metric” = |Dx|+ |Dy|
Princeton
Princeton Jct.
Hamilton
Trenton

146. “Manhattan Customer-shed” Regions for NJ Transit Train Stations
Edison
“Manhattan Customer-shed” Regions for NJ Transit Train Stations
New Brunswick
Princeton
Princeton Jct.
Yellow Lines connect 0.25 mi2 areas to nearest NJT Train Station
where Distance is a
“Manhattan Metric” = |Dx|+ |Dy|
Hamilton

147. Thank You

148. More Sensors, More Protection*
Under the microscope: the new sensors
Highly sophisticated sensors and the necessary networked algorithms provide the foundation for innovative new functions. DISTRONIC PLUS with Steering Assist, BAS PLUS and PRE-SAFE® Brake all employ sensor fusion using the same stereo camera and multistage radar sensors.
Mercedes-Benz is making a major leap forward with the introduction of the Stereo Multi-Purpose Camera (SMPC), or stereo camera for short. It has an opening angle of 45° and is capable of three dimensional detection of crossing objects and pedestrians, and calculating their path. The camera's two "eyes" provide it with a three-dimensional view of the area up to around 50 meters in front of the vehicle, and it is able to monitor the overall situation ahead for a range of up to 500 meters. In this way, the new camera is able to provide data for processing by various systems.
Intelligent algorithms evaluate this information in order to detect and carry out spatial classification of both vehicles that are driving ahead, oncoming or crossing, as well as pedestrians and a variety of traffic signs within a large field of vision.
Whereas the stereo camera's lenses act as the car's eyes, the radar sensors are its ears, so to speak, and provide additional data. The system of radar sensors comprises two short-range radar sensors in the front bumper with a range of 30 m, opening angle 80°, which are complemented by a long-range radar sensor (range 200 m, opening angle 18°) including mid-range scan (60 m, 60°). The data from the camera and radars is amalgamated in a control unit in order to provide the system-specific data for the various functions.
Highly sophisticated sensors and the necessary networked algorithms provide the foundation for innovative new functions using the same stereo camera and multistage radar sensors
S-Class
* w/Driver Assistance Pkg.

149. DISTRONIC PLUS® with Steering Assist
DISTRONIC PLUS with Steering Assist. Convenient support with lateral guidance of the vehicle,
The DISTRONIC PLUS adaptive control system is a driver aid designed to keep the vehicle at the desired distance from another vehicle in front that is travelling slower than the selected cruising speed. This radar-based function has now been enhanced by the addition of Steering Assist, which helps drivers to stay centered in their lane by generating the appropriate steering torque when travelling on a straight road and even in gentle bends.
The stereo camera recognizes lane markings as well as vehicles driving ahead together with their three-dimensional positioning, and relays this information to the electric steering assistance system. When driving at slow speeds, e.g. in congested traffic, Steering Assist can use the vehicle ahead as a means of orientation, enabling semi-autonomous following even when there are no clear lane markings visible. As a result, the system is able to further enhance driving comfort and substantially ease the driver's workload in many traffic situations.
The system's design is so refined that the sensors can detect whether the driver's hands are actually on the steering wheel. If they are not, a visual warning is issued first. Should the driver fail to react to this prompt, a warning signal sounds and lateral lane guidance is deactivated. This does not affect the cruise control function, however, which continues to be operative. DISTRONIC PLUS with Steering Assist can be activated as before with a lever on the steering column in a speed range from km/h. Any speed between 30 km/h and 200 km/h can be selected as the desired cruising speed.
DISTRONIC PLUS has been expanded to include a new sub-function called Steering Assist  the function assists the driver with lateral vehicle guidance to keep the vehicle in lane
•By using the sensors and additionally the camera, the system is able to respond quickly to vehicles joining the lane and to vehicles in front
•The system can prevent overtaking on the wrong side on multilane roads by moderately adjusting the distance to vehicles in the left/right-hand lane
This radar-based function is now enhanced by the addition of Steering Assist, which helps drivers to stay centered in their lane by generating the appropriate steering torque when travelling on a straight road and even in gentle bends.

150. BAS PLUS® with Cross-Traffic Assist
Apart from material damage, accidents at junctions often result in serious personal injuries, too. The new Brake Assist BAS PLUS from Mercedes-Benz is therefore capable of more than just helping the driver to avoid collisions with vehicles ahead or lessen their consequences in a purely longitudinal direction: the new Cross-Traffic Assist function can also come to the driver's aid when there is a risk of a collision with cross traffic at junctions.
If this anticipatory system detects a hazardous situation of this type, it prompts the driver to start emergency braking by activating visual and acoustic warnings. If the driver presses the brake pedal too tentatively, BAS PLUS will step in by automatically boosting brake pressure for effective emergency braking, even applying the brakes at full power if necessary.
BAS PLUS installed in the new S-Class is able to provide braking assistance in situations where other vehicles are crossing the vehicle's path
•Another milestone of the anticipatory braking assistance system BAS PLUS is pedestrian/object protection
BAS PLUS is able to detect standing or slow-moving pedestrians and objects that are in the path of the vehicle and helps the driver to brake as required
Brake Assist BAS PLUS is capable of more than just helping the driver to avoid collisions with vehicles ahead or lessen their consequences in a purely longitudinal direction: the new Cross-Traffic Assist function can also come to the driver's aid when there is a risk of a collision with cross traffic at junctions.

151. PRE-SAFE® Brake
PRE-SAFE® PLUS offers an extension of the familiar occupant protection measures in situations where traffic behind poses a danger. A radar sensor in the rear bumper monitors the traffic behind the vehicle. Apart from this, the PRE-SAFE® anticipatory occupant protection measures, such as the reversible belt tensioners, are deployed. Plus, if the vehicle is stopped PRE-SAFE® PLUS will also come to the driver's aid by increasing the brake pressure in order to keep the vehicle firmly braked during a possible rear-end collision. Keeping the vehicle firmly braked greatly reduces the strain placed on the occupants, such as the risk of whiplash injuries. At the same time, it serves to protect other road users by restricting uncontrolled vehicle movements after the initial impact that could lead to secondary collisions, such as running into a vehicle in front or colliding with pedestrians or other road users at junctions.

152. PRE-SAFE® PLUS: Rear-end Collision Protection
PRE-SAFE® PLUS offers an extension of the familiar occupant protection measures in situations where traffic behind poses a danger. A radar sensor in the rear bumper monitors the traffic behind the vehicle. Apart from this, the PRE-SAFE® anticipatory occupant protection measures, such as the reversible belt tensioners, are deployed. Plus, if the vehicle is stopped PRE-SAFE® PLUS will also come to the driver's aid by increasing the brake pressure in order to keep the vehicle firmly braked during a possible rear-end collision. Keeping the vehicle firmly braked greatly reduces the strain placed on the occupants, such as the risk of whiplash injuries. At the same time, it serves to protect other road users by restricting uncontrolled vehicle movements after the initial impact that could lead to secondary collisions, such as running into a vehicle in front or colliding with pedestrians or other road users at junctions.
Using a radar sensor in the rear bumper to monitor the traffic behind the vehicle, PRE-SAFE PLUS provides occupant protection for rear passengers by engaging measures and increasing the brake pressure in order to keep the vehicle firmly braked during a possible rear-end collision.

153. Assorted Videos of Self-Driving Cars
Assorted Videos of Self-Driving Cars

154. State-wide automatedTaxi (aTaxi)
Ability to serve essentially all NJ travel demand in
sharedRide mode during peak demand,with
premium door2door mode available during off peak hours
Shared ridership allows
Av. peak hour vehicle occupancies to ~ 3 persons/vehicle
Essentially all congestion disappears with appropriate implications on the environment
Required fleet-size under 1M aTaxis
(3.71 registered automobiles in NJ (2009)

155. Thank You

156. PRT: Tough business case:
Segregated guideway too often needs to be elevated
Tends to pass by bedroom windows
Tough sell at public meetings
Guideway is Expensive
Small initial systems tend to be in areas of high demand
While a small vehicle system would work, so will a larger vehicle system, which has less risk
Example: airports, large APM (Automated People Mover) just fine even though they have no expansion potential (nor does the “owner” have expansion desires)
Therefore, not easy to get started.

157. Automation of Road Vehicles
Automated Highways (1939 -> 1999)
Automated vehicles on exclusive automated highways
Tough business case:
no one will build an automated highway if there are no cars to run on it
No one will buy an automated car if it doesn’t have any roads to run on
(Henry Ford lobbied hard (created a film & “propaganda” subsidiary) to have “Farm2Market” roads built throughout the country so that buyers of his cars & trucks would have somewhere to drive them.
National Automated Highway System Research Program (1992~1997)
National Automated Highway System Research Program A Review
AN OVERVIEW OF AUTOMATED HIGHWAY SYSTEMS (AHS) AND THE SOCIAL AND INSTITUTIONAL CHALLENGES THEY FACE

158. Automation of Road Vehicles
Concept of Automated Vehicles Sharing Roadways with Conventional Human-driven Veicles (1994 -> )
I suggested the concept during the National Automated Highway System Research Program (1992~1997); however, it wasn’t pursued.
Concept gained some traction during DARPA Challenges (2004,5,7)
Concept Propelled by Google’s initiative to develop “Driverless-car” starting in 2010.
NHTSA “Automation “Levels”
(Level 0 (no automation) through Level 4 (driverless)
Google: “Level 4” Product Market ready by 2018
Nissan: 2 “Level 4” Models in showroom by 2020
Volvo: Zero deaths by 2020

159. Week 1

160. Human Factors: Why should I remain alert to “save the day”
I’m not trained, I’ll probably screw up!
System needs to be aware of the environment when it is preforming
Well (something that it can’t handle has no reasonable expectation of occurring within the next T minutes and I have a bailout plan (pull over to the side of the road and stop) should the driver not confirm that he/she is ready to become vigilant)
OK, (same as Well, except I have no bailout plan , so stay vigilant)
Can’t hand it (Don’t turn on the systems because something that it can’t handle has a reasonable expectation of occurring within the next T minutes)

161. Human Factors:
System needs to be aware of its expected ability to perform within the next “T minutes”
Well (something that it can’t handle has no reasonable expectation of occurring within the next T minutes and I have a bailout plan (pull over to the side of the road and stop) should the driver not confirm that he/she is ready to become vigilant )
OK, (same as Well, except I have no bailout plan , so stay vigilant)
Can’t handle it (Don’t turn on the systems because something that it can’t handle has a reasonable expectation of occurring within the next T minutes)
The above allows the driver to make an intelligent decision as

162. While there are substantial challenges for PRT..
All other forms of Transit are today hopelessly uncompetitive in serving anything but a few infinitesimally small niche markets.

163. 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”
Table 1-37

164. 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 Automobile 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 enables 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.