1. Professor, Operations Research & Financial Engineering
enRouteCommerce
Beyond LBS (Location-Based Services)
By Alain L. Kornhauser
Professor, Operations Research & Financial Engineering
CoDirector, NJ Center for Transportation Information & Decision Engineering Princeton University
April 12, 2006
Orf 401
April 12, 2006
Week 10

2. all-in-one
Sat/Nav solution
April 12, 2006
Week 10

3. Harvesting Truck Data “hourly” location reportings for ID Latitude
249,467 “trucks”
13-day period: August 29 – September 10, 2002 (also 2003,2004 & 2005)
100 million (average 376 per truck):
Spatial resolution ~ 400 m / 3-sigma ID
Latitude
Longitude
GMT 1 n w
456289 n w
459743 …
249,467 n w
461674
April 12, 2006
Week 10

4. 1st view of 300,000 trucks over 13 days
April 12, 2006
Week 10

5. Path for one Week for Truck # 751 out of 27,417
April 12, 2006
Week 10

6. Data Processing Objectives
Display
Individual trucks over time
All trucks over time
All trucks with geographic commonality
Latitude
Longitude
View
Time
Latitude
Longitude A B
TADS
April 12, 2006
Week 10

7. Instantaneous location of the 27,417 trucks that were moving
April 12, 2006
Week 10

8. April 12, 2006
Week 10

9. Median Speed (by direction) on National Highway Network
4:10 pm April 12, 2006
> 40 mph
< 40 mph
4:10 pm April 12, 2006
height ~ speed
April 12, 2006
Week 10

10. Median Speed (by direction) on National Highway Network
4:10 pm April 12, 2006
> 40 mph
< 40 mph
4:10 pm April 12, 2006
height ~ speed
April 12, 2006
Week 10

11. CoPilot Data “3-sec” position,velocity reportings
“haphazardly” since May 1,2000
About 100 million, growing at greater than 1M/wk
Spatial resolution ~ 20 m / 3-sigma ID
Latitude
Longitude
Speed
Heading
Date
GMT
korn061202 n w
65.3
134
06/12/02
041645
April 12, 2006
Week 10

12. April 12, 2006
Week 10

13. Finally… Convergence of
Decisions that Improve
Quality of Life
GPS
& Wireless
& Processing
enRouteCommerce
& Memory
& Robust OS
&
Mobile
Information
April 12, 2006
Week 10

14. Opportunities to Improve Quality of Life
Market Segments
Quality of Life
“17” year olds
Soccer Moms
Those that have never wanted to go anywhere
Dads who never ask for directions
Anxiety Relief
Feel Good
Safety, Security, Comfort
Mobile Professionals
Better Feed the Family
Solid Quantifiable ROI
April 12, 2006
Week 10

15. In November ‘04
1st to Launch Windows Mobile SmartPhone version of Sat/Nav in Europe AND North America
Live Demo
April 12, 2006
Week 10

16. Live Communications – Integrated Fleet Management
Track driver location in real-time
Via Internet at live.alk.eu.com
On the CoPilot Live Desktop
Driver can tracking invitations
Send two-way messages to the driver
Simple, timed-out responses for safety
Safe interface, integrated into CoPilot Live system
Send new locations to the driver
Send destinations from center, office or home directly to CoPilot|Live
TCP/IP
GPRS-enabled device running CoPilot Live
April 12, 2006
Week 10

17. Outline: Towards Stochastic Route Guidance Congestion forecasting
April 12, 2006
Week 10

18. A B PROBLEM: How to get from A to B3 2 6 1 6 1 3 2 A 9 B 8 2 5 5 6 8 3 3 1 6 1 4
PROBLEM: How to get from A to B
Many Paths, Each with a Different Value to the Decision Maker
Choose path with Most Value (Least Cost)
April 12, 2006
Week 10

19. Historical Expectation: Concepts
Patterns Differ over Days & Time of Day
Most Significant Difference is Between Weekdays and Weekends
Zoo Interchange – Hale Interchange (All Days)
April 12, 2006
Week 10

20. Good Hope - Zoo Zoo - Hale
April 12, 2006
Week 10
Downtown – Moorland Burleigh - Zoo

21. Historical Expectation: Solution
Travel Time
(In Seconds)
Seconds from Midnight
Downtown Interchange to Zoo Interchange – All weekdays during the month of June
April 12, 2006
Week 10

22. Using Real-Time Information to Improve our Estimate
April 12, 2006
Week 10

23. Including Real-Time Information: Concepts
“Since a desirable route needs to be given when the driver asks for it, but the computation of such a route requires travel times which occur later, we need to be able to forecast such travel times.”
DEFINITION: A real-time travel time is a data point that can be received or constructed and measures the time it takes to traverse a specific route from one location to another location ending now.
April 12, 2006
Week 10

24. Including Real-Time Information: Concepts
Peak Hour Characteristics & Return to Normalcy
Burleigh - Zoo
Moorland - Downtown
During Peak Hours, Traffic Patterns Remain at a relatively constant distance to Historical Estimate
There will be a time at which traffic patterns will return to free flow conditions
April 12, 2006
Week 10

25. Including Real-Time Information: Concepts
Exponential Smoothing
Method of “smoothing” a time series of observations
Most recent observations are given a high weight and previous observations are given lower weights that decrease exponentially with the age of the observation
Single
Double
Triple
April 12, 2006
Week 10

26. Including Real-Time Information: Solution
During Peak Periods:
Adaptation of Double Exponential Smoothing
Trend is the Trend of the Historical Estimate
Observation weighted with Most Recent Estimate + Slope for Smoothed Estimate
Forecast done by adding trend to most recent estimate
April 12, 2006
Week 10

27. Including Real-Time Information: Solution
During Non-Peak Periods
Adaptation of Double Exponential Smoothing
Trend is decay to free flow Conditions
April 12, 2006
Week 10

28. (a)
(b)
(c)
Figure: Empirical testing of forecasting algorithm. (a) Forecast from most recent observation. (b) Weighting on most recent observations. (c) Realization of travel time data.
April 12, 2006
Week 10

29. April 12, 2006
Week 10

30. April 12, 2006
Week 10

31. April 12, 2006
Week 10

32. April 12, 2006
Week 10

33. April 12, 2006
Week 10

34. April 12, 2006
Week 10

35. April 12, 2006
Week 10

36. April 12, 2006
Week 10

37. April 12, 2006
Week 10

38. April 12, 2006
Week 10

39. April 12, 2006
Week 10

40. April 12, 2006
Week 10

41. April 12, 2006
Week 10

42. April 12, 2006
Week 10

43. Real-Time Dynamic Minimum ETA Sat/Nav
“Advance” project
Illinois Universities
Transportation Research Consortium
The late 90s
&
Conducted its version of the abandoned “ADVANCE” (Advanced Driver and Vehicle Advisory Navigation ConcEpt )project
250 Volunteers using CoPilot|Live commuting to/from RPI
CoPilot continuously shares real-time probe-based traffic data
CoPilot continuously seeks a minimum ETA route
Link
April 12, 2006
Week 10

44. Project Objectives
Create: real-time data collection from vehicles and dissemination to vehicles of congestion avoidance information which is used to automatically reroute drivers onto the fastest paths to their destinations
Target locations: small to medium-sized urban areas
Aspects: operations, observability, controllability, users, information transfer to travelers
Concept of data sharing to improve everyone’s trips.
We are using the data collected for 1 purpose, but it can be used for many: planning, signal timing changes, others
Focus is on travelers, here drivers – helping them help themselves by enabling them to share information
Why small to medium urban areas? Because these areas tend to have pockets of congestion, unlike big cities which are congested everywhere. Also, better GPS reception (fewer urban canyons) and wireless connectivity (less callers).
April 12, 2006
Week 10

45. Experiment Details 3-month field test
Capital District (Albany), NY, USA
Journey-to-work
200 participants
80 Tech Park employees
120 HVCC staff & students
“Techy” travelers
Network:
Freeways & signalized arterials
Congested links
Path choices exist
Participants were volunteers we screened to ensure they drove to school or work during the morning peak time of 7 to 9 a.m.
April 12, 2006
Week 10

46. Basic Operational Architecture
Two-way cellular data communications between
Customized Live|Server
at ALK
Customized CoPilot|Live
In vehicles 6
Destination 1
2, 4 3 5 7 8
April 12, 2006
Week 10

47. The In-Vehicle “Device”
CoPilot GPS unit:
Determines location
Sprint PCS Vision card and battery pack: Communication with the server
Pocket PC with 256 MB SD card: Software platform and audio communication with the driver
April 12, 2006
Week 10

48. Other
Sat/Nav Platforms
April 12, 2006
Week 10

49. CoPilot|Live Determines “Where am I”,
Every Second
CoPilot|Live Determines “Where am I”,
Then…
CoPilot|Live
“Where Am I”,
Then…
ALK Server
Updates:
TT(mi, mj )
If Momument, mj , is passed
Send mi , mj , ttk(mi, mj )= t(mi) - t(mi)
(52 bytes)
Set i=j
April 12, 2006
Week 10

50. Send… Current Location & Destination,
Every “n” Minutes
CoPilot|Live …
Send… Current Location & Destination,
Last update time
(42 bytes)
CoPilot|Live
Sends: “Where am I”, Dest., Last update
Receives/Posts: updates
Computes: MinETA Updates route, if better
ALK Server
Builds: set Uk
Sends: TT(mi, mj ) for every (i,j) in Uk
ALK Server …
Determines Uk : set of TT(mi, mj ) within “bounding polygon” of (Location;Destination)k that have changed more than “y%” since last update.
ALK Server …
Send… New TT(mi, mj ) for every (i,j) in Uk
(280 bytes/100arcs)
CoPilot|Live …
Updates TT(mi, mj ) in Uk , ETA on current route, Finds new MinETA route, if MinETA “substantially” better then… Adopt new route
April 12, 2006
Week 10

51. When Available ALK Server … ALK Server
Updates:
TT(mi, mj )
ALK Server …
Receives: Other congestion information from various source, blends them in TT(mi, mj )
April 12, 2006
Week 10

52. What We Heard
I'm very impressed with the CoPilot program thus far. The directions are accurate and it adapts quickly to route changes.
I find it interesting how willing I am to listen to a machine tell me which route to take
I like using it for when I have no idea on how to get somewhere, and it is good for my normal route because it keeps me out of traffic on route 4.
This thing is awesome. I was a little skeptical at first but once i got the hang of it I don’t know how I went along without it. I think any student commuting to school will benefit from this.
It is great, it took a while to trust it telling me where to go, but i like it because i cant get lost! Thanks.
April 12, 2006
Week 10

53. also Can Watch Vehicles1 2 3
April 12, 2006
Week 10

54. Live Communications – enRoute Services
Integration of Route-Ahead real-time
traffic incidents (UK)
Straightforward integration for other
markets as communications are
enabled and interface developed
CoPilot’s Live Communications pipe
means that other location-based
services can be readily incorporated
April 12, 2006
Week 10

55. Real-Time Research Alternate Routing with Sparse real-time information
Real-Time weather information
Alternate route implications
data distribution
Vehicles as Probes
Building simulation using CoPilot tracks
Focus on how and how often to measure what
April 12, 2006
Week 10

56. April 12, 2006
Week 10

57. AHS (Automated Highways)
DARPA Grand Challenge 2005
“Barstow,CA to Las Vegas over desert “roads”
Prospect 11
9 UG +1 G + 5 Faculty Advisors
April 12, 2006
Week 10

58. PRT (Personal Rapid Transit)
New Jersey State-Wide System
Designed by ORF 467 F04/5 & F05/06
Objective: 95% O/D within 5 minute walk
April 12, 2006
Week 10