Robert L. BertiniSirisha M. Kothuri Kristin A. TuftePortland State University

Soyoung AhnArizona State University

9th International IEEE Conference on Intelligent Transportation Systems Toronto, CanadaSeptember 20, 2006

Development of an ITS Data Archive Application for Improving Freeway

Travel Time Estimation

2

Outline

Introduction Study Area Data Sources Data Analysis Conclusions Next Steps

3

Project Goals

1. Evaluation of Oregon Department of Transportation (ODOT) travel time estimating and reporting capabilities

2. Identify travel time algorithms for real time applications and historical analysis

4

FHWA policy Variety of technologies

Inductive loop detectors Microwave radar Automatic vehicle tag matching Video detection License plate matching Cell phone matching

Past research General accuracy in free-flow conditions Recurring congestion & incidents more

challenging

Real-time Travel Time Estimates

5

Portland ATMS Freeway Surveillance

485 inductive loop detectors (175 stations)

Dual loop (act as single loop) Mainline lanes Upstream of on-ramps

135 ramp meters 98 CCTV

ATIS www.TripCheck.com

Real-time speed map Static CCTV images

18 dynamic message signs (DMS) 3 display travel times

6

15 directional freeway links I-5 (6) I-205 (3) I-84 (2) US-26 (2) OR-217 (2)

87 probe runs 516 miles driven by 12

drivers 15 hours of data collected

Travel Time Study Area

7

PORTAL

National ITS Architecture ADUS

Funded by NSF Direct fiber-optic

connection between ODOT and PSU

20-second data Occupancy Volume Speed

Customized travel time area Conforms to TMOC

(Portland Regional Transportation Archive Listing)

www.portal.its.pdx.edu

8

Ground Truth Data Hardware

Palm handheld computers

Magellan GPS devices Software

ITS-GPS Available at

www.its.pdx.edu Individual runs and groups

of probe vehicles Variety of traffic conditions

45 percent congested 2 notable incidents

9

Travel Time – Midpoint Algorithm

Influence

Area 4Travel Time 4

(at t = 0)

Travel Time 1Influence

Area 1

Travel Time 3

(at t = 0)

Influence

Area 3

Travel Time 2

(at t = 0)

Influence

Area 2

Link Travel Time

(TT1 + TT2 + TT3 + TT4)

10

Travel Time - Coifman Algorithm Coifman algorithm used upstream detector station speed

to estimate travel time.

c

j

jj

u

v

ht

1

hj = headway, vj = speed at detector, uc = speed of congested shock wave (assumed constant at 14 mph)

jjj tvx

tj and xj computed successively and added until the sum of all xj’s is greater than or equal to the link distance.

Ratio calculated so sum of distances Σxj is equal to link distance and travel time is multiplied by ratio to get link travel time.

11

Travel Time - Coifman Algorithm

Time

Dis

tanc

e

12

Analysis – Six Testing Scenarios

Coifman algorithm using speeds from upstream detectors only

Coifman algorithm using speeds from downstream detectors only

PP ttbttP VV , ,

PP ttattP VV , ,

2 while , , , d/ tVVV PttattbttP PPP

Coifman algorithm using speeds from both upstream and downstream detectors along with the midpoint influence areas 2 while , , , d/ tVVV PttattattP PPP

13

Analysis – Testing Scenarios Contd..

Coifman algorithm using speeds from upstream and downstream detectors weighted in the ratio of distance of the hypothetical vehicle from each detector

Midpoint algorithm (based on influence areas)

Midpoint algorithm using speed at time (t = 0) that is an average of the upstream and downstream detector readings

d

tVVtVdVV PttattbPttatta

ttPPPPP

P

)]([)]([ , , , , ,

PPPP ttbttPttattP VVVV , , , , or

2 , ,

,PP

P

ttbttattP

VVV

14

Analysis – Probe TT & TT Estimates

(a) Probe Vehicles

0

2

4

6

8

10

12

14

16

18

20

0 2 4 6 8 10 12 14 16 18 20

Probe Travel Time (min)

Es

tim

ate

d T

rave

l T

ime

(min

)

Coifman (u/s)

Coifman (d/s)

Midpoint

Coifman - Midpoint

Coifman - Distwt

Midpoint - Average

0

2

4

6

8

10

12

14

16

0 2 4 6 8 10 12 14

15

Analysis – Variance Comparisons

0

5

10

15

20

25

30

3 4 5 6 8 9 10 12 13

Link Number

Probe

Coifman (u/s)

Coifman (d/s)

Midpoint

Coifman - Midpoint

Coifman - Distwt

Midpoint - Average

Tra

vel

Tim

e (m

in)

+/-

On

e S

td D

ev

xxx

16

Analysis – Free Flow Travel Times

292.00

293.00

294.00

295.00

296.00

297.00

298.00

299.00

300.00

17:03 17:05 17:07 17:09 17:11 17:13

Time

Mile

po

st (

mi.)

Probe

Coifman u/s

Coifman d/s

Midpoint

17

Analysis – Incident Travel Times

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8:28 8:32 8:36 8:40 8:44 8:48 8:52 8:56 9:00

Time

Mil

ep

os

t (m

i.)

Probe

Coifman u/s

Coifman d/s

Midpoint

18

Analysis – Large Detector Spacing

293.00

294.00

295.00

296.00

297.00

298.00

299.00

300.00

8:11 8:13 8:15 8:17 8:19 8:21 8:23

Time

Mil

ep

os

t (m

i.)

Probe

Coifman u/s

Coifman d/s

Midpt

19

Analysis – Detector Spacing

Midpoint

R2 = 0.5077

R2 = 0.5915

0

20

40

60

80

100

120

140

0 0.5 1 1.5 2 2.5 3 3.5

Detector Spacing (mi.)

Tra

vel

Tim

e E

stim

atio

n E

rro

r (s

ec)

Uncongested

Congested

20

Bus Probes

21

Analysis - Bus TT & TT Estimates

0

2

4

6

8

10

12

14

16

18

20

0 2 4 6 8 10 12 14 16 18 20

(b) Bus

0

2

4

6

8

10

12

14

16

0 2 4 6 8 10 12 14

Bus Travel Time (min)

Estim

ated

Tra

vel T

ime

(min

)Coifman (u/s)

Coifman (d/s)

Midpoint

Coifman - Midpoint

Coifman - Distwt

22

Analysis – Variance Comparisons

0

5

10

15

20

25

30

5 6 7 12 13 14 19 20 21

Day

Bus

Coifman u/s

Coifman d/s

Midpoint

Coifman - Midpoint

Coifman - Distwt

Midpoint - Average

Tra

vel

Tim

e (m

in)

+/-

On

e S

td D

ev

xxx

23

Conclusions

Travel times estimated by Coifman algorithm are more accurate than midpoint travel times.

The accuracy of travel times depends on Location and density of detectors Location, formation and dissipation of

queue Both algorithms misestimate when

incidents are encountered. Coifman algorithm more suited for

historical analysis in its current form.

24

Next Steps

More probe data ITS data fidelity and its effect on

travel time estimates Assessment of performance of

algorithms with additional ground truth data

Sensitivity analysis Refinement of algorithms

25

Acknowledgements

Dr. Chris Monsere Stacy Shetler Aaron Breakstone Dean Deeter Galen McGill ODOT TriMet Castle Rock Consultants PORTAL Team Peter Bosa Volunteer Drivers