Assessment and Refinement of Real-Time Travel Time Algorithms for Use in Practice April 11, 2007

Outline Data Analysis Summary (40 min) Data Collection Update (20 min) OTREC Funding (30 min) ITS American Presentation (20 min) Next Steps (10 min)

Error – All Runs ODOT Adjusted Lengths for I-5 Segments # of Runs - 137

Error – Congested Runs Congested => Average Probe Speed <= 50 mph # of Runs - 86

Error By Segment

ODOT Adjusted Lengths vs. Standard Midpoint Lengths Freeway Segment Standard Midpoint ODOT Adjusted (S –TRUNCATED) ODOT Adjusted (S – SCALED) Avg Error Max Error Avg Error Max Error Avg Error Max Error I-5 N Carmen to Downtown 7.2 %31.7%7.0 %29.9 % I-5 S Terwilliger to Tualatin- Sherwood 6.7%29.6%5.6 %21.0 %4.9 %17.2 %

Adjustments I-5 N Carmen - Dwntn Station IdLocationMilepostMidpoint LenAdjusted Len 1005Lower Boones Upper Boones /Kruse Haines St Pacific Hwy Capital Hwy Spring Garden Multnomah Terwilliger Bertha Macadam Ave Total Len:

Adjustments I-5 S Terwill – T/S Station Id LocationMilepostStandard Midpoint Len Actual ODOT Len ODOT Adjust – Truncate ODOT Adjust - Scale 1036Hood Ave (2.95) Spring Garden Capital Hwy W Haines St Upper Boones Lower Boones Nyberg (2.23) Total Len:

Initial Algorithm Adjustments Vary range of minutes used for travel time estimation  Tested 1, 3, 6, 9, 12 minute averages Trending  Calculate two travel time estimates First uses data from 0-3 minutes before run start time Second use data from 3-6 minutes before run start time If 3-6 minute estimate is greater/less than than 0-3 minute estimate, adjust estimate by 1/3 of the difference

Evaluation of Algorithm Adjustments Segment1-min3-min6-min9-min12-min I-5 N SoD7.3%7.2%6.7%6.5%7.0% I-5 N SoD - ODOT6.5%7.0%6.6%6.5%6.9% I-5 S SoD5.9%6.7%6.8%7.3%7.7% I-5 S SoD - ODOT3.5%4.9%5.0%5.5%5.8% I-84 E13.6%11.6%9.5%9.3%7.2% I-84 W16.0%17.1%16.3%15.9%15.2% I-5 N NoD9.6%8.7%7.6%13.2%15.1% I-5 S NoD17.3%15.7%14.5%14.7%14.6% Table shows average absolute error percentage. Lowest errors indicated with yellow background. 3-6 min trend 8.1% 7.9% 6.0% 3.8% 15.1% 8.8% 9.4% 16.4%

Identifying High-Error in Real-Time Would like to assess in real-time when travel time estimates are likely to be inaccurate  Congestion is not a good indicator of error rates (low error in high congestion sometimes)  Try to find some measure that correlates with travel time error Attempted to Correlate Error with  Standard Deviation of Travel Time (past 3 minutes)  Average Loop Speed (past 3 minutes)  Maximum Difference in Travel Time (past 3 minutes)  Standard Deviation of Volume (past 3 minutes) No good correlation found

Error vs. Std Deviation of Travel Time

Detailed Analysis

I-5 NB – Downtown to Columbia River 15 uncongested runs with low error removed from table

I-5 SB – Columbia River to Downtown Selected Runs

Run :39 PM Speeds Wheeler Hood

Run :22 PM Speeds Wheeler Hood

Run :59 PM Speeds Wheeler Hood

I-84 WB I-205 to I-5

Run 80 2/14/07 8:31 AM

Loop Detectors On I-84 WB detectors EB detectors WB direction of travel

Run 97 2/14/07 8:01 AM

Run 88 2/14/07 5:32 PM

I-84 EB I-5 to I-205

Data Collection Update 150 runs, approx 80 hours of driving collected Have collected on I-5 (entire corridor) About 135 runs analyzed Initial planned spending ($5K) completed Additional Collection (approx $3K available)  Request for bids for a second round of collection sent out  TrafStats has lowest bid

Data Collection Summary CorridorAM/ PM # Round- Trips Phases 1 & 2 # Round-Trips Phase 3 (desired) Total I-5 S of DowntownAM83038 PM83038 I-5 N of DowntownAM PM I-84AM100 PM100 I-205 (212/224 – I-84)AM PM US 26AM000 ?? PM000 ?? HWY 217AM030 PM030 I-405AM01010 ?? PM01010 ??

Data Collection Questions Rough estimates based on FHWA averages show we need ~60 runs on each segment Specializing for actual variations (using data from PORTAL) indicates even more runs required I-405  2 detectors NB (Couch and Glisan)  7 detectors SB  Limited Congestion (some on I-405 S in afternoon peak) I-26  Lots of congestion, but few detectors in congested areas

I-405 South March 2007

US 26 East – March 2007

Detector Locations on US 26 EB detectors Skyline, mp , mp 73.62

US 26 WB

OTREC Funding FY01 (Oct Sept 2007)  $23,000 in funding received (Used $23,043 of match)  Extend this project for 3 months (July-Sept 2007)  Additional data collection ($2500) and analysis  Comments – Straightforward but worthwhile FY02 (Oct 2007 – Sept 2008)  Abstracts due April 27, Proposals due May 25  Have $34,000 in match left to use

OTREC – FY02 Ideas Reviewer Comments  One reviewer commented that what was missing from the proposal was “a good set of metrics for ‘accuracy’”  Was also interested in: “real-time quantification of the accuracy of travel time estimates.” Scope Extension  Integrate historical & real-time data to improve travel time estimates  Travel time prediction (near-term prediction)  Travel time reliability – Attempt to provide reliability information along with travel time estimates  Real-time travel time accuracy

Other Work at PSU OTREC-funded project to study filling in gaps in loop data using methods such as linear regression, artificial neural networks, etc.  PIs: Dave Maier, Kristin Tufte latte NSF-funded project  Study how to combine live and archived (historical) data to benefit traffic applications  Working on a demo of the system  PIs: Dave Maier, Robert Bertini, Kristin Tufte IEEE ITSC paper on detector spacing (Bertini)

ITS America Presentation Study Goals PORTAL Data Collection Analysis  What types of errors are we seeing  Where are we seeing the errors  What do we think is causing the errors Algorithm Refinement  Adjusting lengths of influence areas appears to make a significant improvement

Study Goals Verify Accuracy of Travel Time Estimation in Portland Methodology: Collect Probe Vehicle Runs and compare to data from PORTAL

PORTAL PORTAL – Portland Area Transportation Data Archive Archiving ODOT loop detector data since July 2004 Provides basis for a large travel time comparison study

Data Collection How much we collected and where GPS devices + software developed by the ITS lab – software records position & speed of vehicle every 3 minutes Data was retrieved off of GPS units, processed using GIS and inserted into database for automated processing

Analysis 2-3 slides of histograms, tables, plots – like in today’s talk

Results Haven’t yet found simple algorithmic changes that improve travel time Modifying segment lengths does appear to help Clearly areas where we need new detection What do you want to say? Final conclusions

Next Steps… Additional Collection (should start next week) Continue Analysis  New student just hired to help with analysis OTREC FY02 Proposal Task 5: Detailed Comparative Study  Original date: March 23  Proposed date: May 9 (next meeting) Draft Report: May 31 Final Report: June 30

Error – Highly Congested Runs Highly Congested => Average Probe Speed <= 40 mph # of Runs - 67