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

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

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 42.34.57n 75.43.12w 456289 42.39.37n 75.44.43w 459743 … 249,467 47.29.43n 94.27.43w 461674 April 12, 2006 Week 10

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

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

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

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

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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

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

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 42.39.374n 75.44.436w 65.3 134 06/12/02 041645 April 12, 2006 Week 10

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

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

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

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 e-mail 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

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

A B PROBLEM: How to get from A to B 3 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

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

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

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

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

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

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

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

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

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

(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

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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

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

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

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

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

Other Sat/Nav Platforms April 12, 2006 Week 10

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

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

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

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

also Can Watch Vehicles 1 2 3 April 12, 2006 Week 10

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

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

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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

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