Commuters Helping Commuters:Commuters Helping Commuters: Findings from a Field Test of a Probe-based, Findings from a Field Test of a Probe-based, Wireless, Traveler Information SystemWireless, Traveler Information System

William Wallace, Earl E. Lee, Jeffrey WojtowiczRensselaer Polytechnic Institute

George F. List, Alixandra DemersNorth Carolina State University

January 24, 2006Transportation Research Board Annual MeetingWashington, DC

Outline Problem Perspective

Prior Efforts in ATIS

Project Background Objectives Team Experiment details In-vehicle device Server activities Watching vehicles

Some User Perspectives

Path-seeking Process

Project Results – Did It Work?

Advances in the Future

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Problem Perspective

DataCollection

InformationDissemination

DataIntegration

PathChoices

NetworkFlows

System Feedback

ControlDecisions

Issues

•Observability

•Controllability

•Reachability

• Roadside sensors• Video cameras• News helicopters• AVI detectors• AVL probes

Data Sources

• HAR• VMS/ DMS• 511• Web pages• User messages

Techniques

ProblemPerspecti

ve

PriorEfforts

ProjectBackgro

und

Some UserPerspectiv

es

Path-seekin

gProce

ss

Project

Results

FutureAdvanc

es

Prior Efforts in Real-time ATIS

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

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

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

The Team Academic partners:

Rensselaer Polytechnic Institute Cornell University Polytechnic University CUBRC (CALSPAN / University of Buffalo

Research Center)

Public partners: FHWA NYS DOT CDTC CDTA

Corporate partners: ALK Technologies, Inc. Annese and Associates Consensus Systems Technologies Sprint

Rensselaer County Economic and Community Development

Hudson Valley Community College

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

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

I 90

I 787

US

HW

Y 4

STATE

HW

Y 32

STATE HWY 2

STATE HWY 151US HW

Y 9

STATE HWY 378

2ND

ST

STATE HWY 155

WASHINGTON AVE

WINTER ST

15T

H S

T

STATE HWY 43

8TH

ST

STA

TE

HW

Y 3

77B

RO

AD

WAY

AV

E

CAMPBELL AVE

ACCESS RD

1ST ST

STATE HWY 136

COUNTY HWY 130

TIBBITS AVE

LIN

CO

LN A

VE

RA

MP

LOU

DO

N R

D

STATE HWY 43

RAMP

RA

MP

RA

MP

RAMP

RA

MP

HVCC

Rensselaer Technology

Park

TroyColonie

North Greenbush

Albany

Experiment Details

The In-Vehicle “Device”

Pocket PC with 256 MB SD card: Software platform and audio

communication with the driver

CoPilot GPS unit:Determines location

Sprint PCS Vision card and battery pack: Communication

with the server

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Server Activities Receives vehicle messages: new trip,

route change, GPS location, monument passage

Updates travel times Sends data to vehicles: monument-to-

monument travel times Archives history Responds to operator inputs

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

1

2

3

Watching Vehicles

Usage over Time: Trips by Cohort

Usage by Period

0

100

200

300

400

500

600

700

Beginning Middle End

Period

Nu

mb

er

of

Tri

ps

HVCC Staff HVCC Students Tech Park Empl.

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

What We HeardI 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.

Feel more secure in making changes and using alternate

routes, since I'm confident that CoPilot will get me to work!

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.

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.

I'm very impressed with the CoPilot program thus far. The

directions are accurate and it adapts quickly to route changes.

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Interview Responses

Estimated Driving Time from Home to Work

0

10

20

30

40

50

HVCC Staff Students Tech Park HVCC Staff Students Tech Park

Presurvey Postsurvey

Min

ute

s

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Travel Time Trends: Driver 1 Early:

Scatter of travel times

Later: More consistent travel times

Travel Time (TECH190, 6-9 a.m.)

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80 90

Day in the Experiment

Trav

el T

ime

(min

ute

s)

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Travel Time Trends: Driver 2 Early: Scatter

of travel times

Later: Similar to Driver 1 with more consistent travel times. New: upper bound lowered

Travel Time (HVCC132, 6-9 a.m.)

0

5

10

15

20

25

30

35

40

45

0 10 20 30 40 50 60 70 80 90

Day in the Experiment

Trav

el T

ime

(min

ute

s)

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Probe Path-seeking ProcessProbe Path-seeking Process1. Monitor current location2. Activation, destination, and route3. Download current travel times table4. Update route: send new route message5. Send messages: current location and “monument” passing6. Server is constantly updating link travel times7. Download updated travel times8. Re-route evaluation, cycle back to step #3 or #4

6

Destination

1

2, 43 5

7

8

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Step 2.1 Activation

Evidence exists that vehicles joined the network when they started a trip.

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Percentage of Connections1st Half of Experiment

(2/28/05 - 4/8/05)

successful connection, 53%

reported server problems, 14%

other problems*, 33%

* Could be due to: user not attempting to fully connect, or the follow ing problems: softw are, hardw are, Sprint, user error

Percentage of Connections2nd Half of Experiment

(4/11/05 - 5/13/05)

successful connection, 69%

reported server problems, 9%

other problems*, 22%

* Could be due to: user not attempting to fully connect, or the follow ing problems: softw are, hardw are, Sprint, user error

Second half had 19% more successful connections

Step 2.2 Destination & RouteThe network condition was downloaded reliably.

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Distribution of Route Announcements (M-F 6-9 am)

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

0005

00

0010

00

0015

00

0020

00

0025

00

0030

00

0035

00

0040

00

0045

00

0050

00

0055

00

0100

00

Time Difference (hhmmss)

Fre

qu

ency

1st Half %

2nd Half %

Step 3. Travel Time Requests and Updates

Vehicles send monument-to-monument travel times

Server updates link travel times using exponential smoothing, posts an updated travel time table

Vehicles query server every minute for new travel times within “update region”

Vehicles change routes if a new route’s travel time is significantly faster

Region for which updated travel times are obtained

TTnew = TTprevious * 0.9 + TTmost recent * 0.1

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Step 4. Update Route

Vehicle received 1st NRC message 1 min 32 seconds after being logged onto the server

Additional NRC messages as the trip progressed

The current status of network was downloaded when a trip commenced.

Step 5. Send locationsVehicles transmitted data.

114712280305-73748328424961206781140174954

114642280305-73748563424952107045140174951

114608280305-73748971424949814650140174949

113649280305-7374931042494886941768140174910

113619280305-7374896042495941245984140174907

113607280305-73750670424961813221009140174906

113537280305-7375666542496666365955140174903

113507280305-7376277142496336122853140174900

113222280305-73758225425193082411883140174888

113152280305-73759323425233614721931140174885

113122280305-73757945425278742771948140174883

113052280305-73756448425311302541966140174880

113022280305-73754815425345403312029140174876

112952280305-73752948425387305392052140174873

112922280305-73751866425447616942078140174870

112852280305-73746771425517235672105140174869

112822280305-73745733425583255821863140174865

112752280305-73742193425644585742044140174862

112722280305-73738053425711565601818140174860

112652280305-73738746425782966341696140174856

112622280305-73740260425855435951728140174853

112407280305-73746671426166835501932140174844

112337280305-73745185426219514171864140174841

112307280305-73744350426267853721889140174838

112237280305-73743946426283661841935140174835

112207280305-73746320426305944561668140174832

112137280305-73748836426363033561854140174828

112105280305-73745305426406711571996140174826

112035280305-7374816042643518234925140174824

112005280305-73752261426441613322347140174822

111932280305-73749038426481855071859140174820

111902280305-73745986426541315842217140174818

111832280305-73739531426591996022184140174816

111802280305-73734998426652095772062140174814

111732280305-73729393426710936032178140174813

111702280305-73724973426774546391973140174811

111632280305-73720953426842456042145140174809

111602280305-73714608426897666022207140174807

111532280305-73709411426960286091925140174806

111502280305-73707320427032516161917140174805

UTCTime

UTCDateLonLatSpeedHeading

ActiveUser ID

ObservationID

114712280305-73748328424961206781140174954

114642280305-73748563424952107045140174951

114608280305-73748971424949814650140174949

113649280305-7374931042494886941768140174910

113619280305-7374896042495941245984140174907

113607280305-73750670424961813221009140174906

113537280305-7375666542496666365955140174903

113507280305-7376277142496336122853140174900

113222280305-73758225425193082411883140174888

113152280305-73759323425233614721931140174885

113122280305-73757945425278742771948140174883

113052280305-73756448425311302541966140174880

113022280305-73754815425345403312029140174876

112952280305-73752948425387305392052140174873

112922280305-73751866425447616942078140174870

112852280305-73746771425517235672105140174869

112822280305-73745733425583255821863140174865

112752280305-73742193425644585742044140174862

112722280305-73738053425711565601818140174860

112652280305-73738746425782966341696140174856

112622280305-73740260425855435951728140174853

112407280305-73746671426166835501932140174844

112337280305-73745185426219514171864140174841

112307280305-73744350426267853721889140174838

112237280305-73743946426283661841935140174835

112207280305-73746320426305944561668140174832

112137280305-73748836426363033561854140174828

112105280305-73745305426406711571996140174826

112035280305-7374816042643518234925140174824

112005280305-73752261426441613322347140174822

111932280305-73749038426481855071859140174820

111902280305-73745986426541315842217140174818

111832280305-73739531426591996022184140174816

111802280305-73734998426652095772062140174814

111732280305-73729393426710936032178140174813

111702280305-73724973426774546391973140174811

111632280305-73720953426842456042145140174809

111602280305-73714608426897666022207140174807

111532280305-73709411426960286091925140174806

111502280305-73707320427032516161917140174805

UTCTime

UTCDateLonLatSpeedHeading

ActiveUser ID

ObservationID

monument passing data from vehicle m2m table

location data from vehicle position table

0-1112708280305131961124181226714023800

113196112418280305122671120399413214023798

112267112039280305941321118469412314023797

094132111846280305941231116599413514023796

094123111659280305941351115599413814023795

094135111559280305941381113321005514023794

RouteCode

NextMonID

CurrUTCTime

CurrUTCDate

CurrMonID

PrevUTCTime

PrevMonID

ActiveUser ID

ObsID

0-1112708280305131961124181226714023800

113196112418280305122671120399413214023798

112267112039280305941321118469412314023797

094132111846280305941231116599413514023796

094123111659280305941351115599413814023795

094135111559280305941381113321005514023794

RouteCode

NextMonID

CurrUTCTime

CurrUTCDate

CurrMonID

PrevUTCTime

PrevMonID

ActiveUser ID

ObsID

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Step 6. Server Updates Travel Times

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Travel Time Algorithm Verification

0

500

1000

1500

2000

2500

3000

3500

4000

0 500 1000 1500 2000 2500 3000 3500 4000

Manually Computed Travel Time

Ser

ver

Cal

cula

ted

Tra

vel T

ime

Step 7. Download Travel Times Table

Vehicles requested updates Snapshot of LOG messages on server:

MSG_UserIDs:: Old ID: 0 New ID: 542  Time: 5:13PMMSG_Login:: Veh: 129911 Login: rpidemo Screenname: TECH198  Time: 5:13PMMSG_Login:: Veh: 129940 Login: rpidemo Screenname: TECH190  Time: 5:13PMMSG_Login:: Veh: 467 Login: rpi storage Screenname: RPI LiveDB  Time: 5:13PMMSG_Login:: Veh: 129881 Login: rpidemo Screenname: TECH075  Time: 5:13PMMSG_Login:: Veh: 446 Login:  Screenname:   Time: 5:20PMMSG_TTReq::  Lon: -73691473 Lat: 42788138 DestLon: -73668992 DestLat: 42785812 Time: 5:20PMTT_GetMonuments::  VehID: 129874 Mons: 50 Time: 5:20PMMSG_TTSend::  VehID: 129874 Mons: 50 Arcs: 12  Time: 5:20PMMSG_TTReq::  Lon: -73822043 Lat: 42692266 DestLon: -73928752 DestLat: 42743160 Time: 5:20PMTT_GetMonuments::  VehID: 129914 Mons: 38 Time: 5:20PMMSG_TTSend::  VehID: 129914 Mons: 38 Arcs: 5  Time: 5:20PMMSG_UserIDs:: Old ID: 0 New ID: 864  Time: 5:21PMMSG_Login:: Veh: 446 Login:  Screenname:   Time: 5:21PMMSG_POS:: Lat: 42662345 Long: -73736841 Heading: 2054 Speed: 680 Time: 5:21PM

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Results Normal Days Incident Days Vehicle Rerouting

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Response to IncidentsMore reroute messages were generated on days with incidents

versus non-incident days

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Distribution of New Route Cost messages - Incident vs.Non-Incident days

0%

10%

20%

30%

40%

50%

60%

70%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Number of New Route Cost Messages Received

Per

cen

tag

e o

f D

rive

rs

11-Mar

8-Mar

9-Mar

An Incident Happens, what happens to ATIS drivers?

Day of Experiment

Count

of Path

Choic

es

30

20

10

0

88858481807978777472717067666564636059585756535150494645444342393837363531302824232221181615141110987432

30

20

10

0

Mill St.

Morrison St.

Route Choice: Mill St. vs. Morrison St.EB Commute to Work - Morning Peak 6 a.m. - 9 a.m.

Based on Vehicle Mon2Mon Table for 94139 => xxxx => 94136 monument string.939 Total Trips: 154 via Mill St., 685 via Morrison St.

Day 86 Menands Bridge Fire

Bridge closed, so no traffic here.

Where is it?

Day 18 Car AccidentOn Morrison

Slight volume shift visible

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Incidents – Car Accident on Morrison St. (Day 18)

Comparing Speeds for a Typical Day vs. an Incident Day on Morrison Street

0

5

10

15

20

25

30

35

40

7:30

7:38

7:46

7:54

8:02

8:10

8:14

8:18

8:22

8:26

8:30

8:34

8:38

8:42

8:46

8:50

8:54

8:58

Time of Day (a.m. peak)

Ob

serv

ed S

po

t S

pee

ds

(mp

h)

Typical Day Incident Day

Incident OccursPolice arrive at 8:20Tow Truck arrives at 8:41Incident Clears at 8:51

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Incidents – Bridge Fire (Day 86)

Route 378 Bridge is indeed closed 

I-787 (SB backed up) south of Watervliet Exit

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Comparison of Network Loading5/04/05 – Typical Day

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

5/11/05 – Fire Incident

Advances in the Future Large scale deployment in a region, for a

major employer, etc. Use other data in travel time estimation,

from loops, video cameras, etc. Create TMC interface for data exchange,

information dissemination, etc. Create system of multiple service providers,

like the cell phone system Add features: congestion pricing, road

conditions, road restrictions (e.g. school zone speed limits), etc.

Try other designs, e.g., vehicle-to-vehicle network without a server

FutureAdvan

ces

ProjectResults

Path-seekingProcess

Some UserPerspectiv

es

ProjectBackgro

und

PriorEffort

s

ProblemPerspecti

ve

Thank you.

Questions?

Contact Us: George F. List, gflist@ncsu.edu William (Al) Wallace, wallaw@rpi.edu Alain Kornhauser, alainpu@alk.com Earl (Rusty) Lee, leee7@rpi.edu Alixandra Demers, ademers@unity.ncsu.edu Jeffrey Wojtowicz, wojtoj@rpi.edu Brian Menyuk, bmenyuk@dot.state.ny.us