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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
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ces
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Path-seekingProcess
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es
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s
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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
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Path-seekingProcess
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es
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s
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ve
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Path-seekingProcess
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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
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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
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Path-seekingProcess
Some UserPerspectiv
es
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und
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s
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ve
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ces
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es
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s
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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.
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es
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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.
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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
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es
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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)
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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)
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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
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Step 2.1 Activation
Evidence exists that vehicles joined the network when they started a trip.
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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.
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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
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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
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Step 6. Server Updates Travel Times
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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
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Results Normal Days Incident Days Vehicle Rerouting
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Response to IncidentsMore reroute messages were generated on days with incidents
versus non-incident days
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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
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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
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Incidents – Bridge Fire (Day 86)
Route 378 Bridge is indeed closed
I-787 (SB backed up) south of Watervliet Exit
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Comparison of Network Loading5/04/05 – Typical Day
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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
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Thank you.
Questions?
Contact Us: George F. List, [email protected] William (Al) Wallace, [email protected] Alain Kornhauser, [email protected] Earl (Rusty) Lee, [email protected] Alixandra Demers, [email protected] Jeffrey Wojtowicz, [email protected] Brian Menyuk, [email protected]