Accuracy in Real-Time Estimation of Travel Times

Galen McGill, Kristin Tufte, Josh Crain, Priya Chavan, Enas Fayed

15th World Congress on Intelligent Transportation Systems

November 18, 2008

Project Summary• Initial Project Phase

– Collected 500 ground truth runs– Analyzed travel time estimation accuracy

• Second Phase– Addressing primary causes of error– Algorithmic adjustments– Analysis of actual DMS message accuracy

Study Area and Data Collection

• 544 Ground truth probe runs

• GPS-enabled vehicles (Garmin iQue ®)

• Detector data from 500 dual loop detectors on Portland-area freeways

I-5 North of Downtown

Map of Study Area

I-84

I-205

I-5 South of Downtown

OR-217

US-26

Downtown Portland

Initial Project Results• Overall average absolute percent error 11%

(SDPE 18%)– 15% of runs had absolute percent errors

larger than 20%• Accuracy varied between segments• Primary causes of error

– Malfunctioning detectors– Large detector spacing– Changing traffic conditions

Overall Estimation Accuracy

3.1%6.1%

14.0%

28.9%31.3%

11.0%

2.8% 2.9%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

< -30% -30% to-20%

-20% to-10%

-10% to0%

0% to10%

10% to20%

20% to30%

> 30%

% Error

% o

f R

un

s

Average Percent Error – All Runs

Outline• Malfunctioning Detectors

– Critical detectors• Large Detector Spacing

– Prioritizing addition of detectors• How significant are changing traffic

conditions?• Is congestion correlated with error?• Historical average period• DMS message accuracy

Malfunctioning Detectors

• 50% of the runs had one or more detector stations malfunctioning

• High error when certain critical detectors failed (i.e. near recurrent bottlenecks)

• Identify set of critical detectors– Prioritize detector maintenance– May not provide travel time when a

critical detector has failed

Critical Detectors on I-5 NB

I-5 and I-405 Junction I-5 NB Columbia River Crossing Bottleneck~

Critical Detectors

Bottlenecks

Large Detector Spacing• Where should detection be added?

– Prioritize locations of additional detection– Understand implications of detector

location• Detectors simulated with ‘virtual detectors’

using probe vehicle speeds• Compared ‘real-time’ travel time estimates

Additional Detection Locations

Terwilliger Curves (mp 298)

Marquam Bridge (mp 300.5)

I-5 NB

I-5 SB/OR 217 Junction (mp 292)

Addition of Detectors

0

5

10

15

20

25

I-5 NB Terwilliger Curves I-5 SB I-5 SB/OR 217Junction

I-5 NB Marquam Bridge

Midpoint

Real-Time

Added Detector (Real-Time)

MA

PE

Additional Detection - Conclusions• Developed methodology for prioritization of

new detector locations• Detection recommended at several

locations on I-5

Changing Traffic Conditions• Travel time estimate provided at start of

segment (DMS), but traffic conditions may change as a vehicle drives through segment

Changing Traffic Conditions

Traffic flow

DMS

End of segment (DMS predicts travel time to this location)

Vehicle (60 mph)Congestion Wave

(?? mph)

Location where vehicle encounters congestion

Travel time estimation incorrect in this section

Estimation error depends on speed of congestion wave (faster wave = more error).

Congestion Wave Speed and Error

• Analyzed four bottlenecks– Average congestion wave speed ranged

from 6.5 mph to 9.7 mph• Effect on error

– 7.5 mph congestion wave; 25 mph speed during congestion gives max error of 13.5%

Traffic flow

Traffic Speed 60 mph

Congestion Wave (7.5 mph)

Traffic Speed 25 mph

Maximum Error by Wave Speed

Is Congestion Correlated with Error?

• Little to no correlation for All Runs• Some correlation on I-5 SB SoD

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70

Average Loop Speed (mph)

Ab

solu

te P

erce

nt

Err

or

(%)

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70

Average Loop Speed (mph)

Ab

solu

te P

erce

nt

Err

or

(%)

Loop Speed vs. Error – All Runs Loop Speed vs. Error – I-5 SB SoD

Is Congestion Correlated with Error?

• Tried to correlate variables with error– Average loop speed– Average probe speed– Standard deviation probe speed– Estimated travel time– Minimum loop speed

• No significant correlation pattern found– Some segments correlated; no pattern

across all runs

Effect of Historical Average Period• Travel time estimation algorithms use a

speed average (i.e. 3-minute, 5-minute) for travel time calculation

• 5-minute had lowest error, but was slightly biased towards underestimation

• 3-minute also had low error and was less biased

• Conclusion: 3-minute or 5-minute average is reasonable

DMS Travel Time Accuracy• Ground truth vs. posted DMS travel times• Expected to be fairly accurate, but…

Carman DMS Prediction

Ground Truth Travel Time

< 10 min

10-12 min

12-15 min

> 15 min

< 10 min 6 6 610-12 min 2 3 6 512-15 min 1 3> 15 min

Potential problem??

DMS Travel Time Accuracy• Study showed ODOT’s estimation algorithm

was fairly accurate • DMS travel time messages were much less

accurate– No messages “> 15 minutes” ever posted

• Issue reported to ODOT Staff• Configuration error in the ATMS database

was discovered and corrected

Conclusions• Current algorithm accuracy relatively good• Critical detectors and additional detection

to address high error• Effect of changing conditions may not be

significant• 3-5 minute average window is reasonable• Need to verify actual DMS messages

Acknowledgments• Oregon Department of Transportation

– Dennis Mitchell, Jack Marchant• At Portland State University

– Robert L. Bertini, Sirisha Kothuri• Oregon Transportation, Research and

Education Consortium (OTREC)

Questions?

Thank You!portal.its.pdx.eduwww.its.pdx.edu

Thank You!portal.its.pdx.eduwww.its.pdx.edu