Toward Understanding and Reducing Errors in Real-Time Estimation of Travel Times Sirisha Kothuri, Kristin Tufte, Enas Fayed, Josh Crain, Robert L. Bertini

1/16/ Objectives Project Goals  Verify accuracy of current travel time estimates  Understand sources of error  Identify cost-effective solutions Travel time estimates currently displayed on 3 VMS on I-5 in Portland; ODOT wants to expand to other VMS (18 total), 511, and the Internet Over 500 ground truth probe runs collected  Probe travel times compared with travel time estimates using graphical and statistical analysis  Identified 3 primary sources of error  Analyzed alternative algorithms and detector spacing

1/16/ Study Area Studied 14 directional segments on Portland-area freeways  I-5 divided into North of Downtown (NoD) and South of Downtown (SoD) 671 loop detectors (including Vancouver, WA); 195 stations  Portland freeway detectors placed for ramp metering operations Speed, volume, occupancy reported at 20-second granularity  Data received and archived by Portland State ITS Lab (PORTAL) OR-217 I-5 South of Downtown (SoD) I-5 North of Downtown (NoD) US-26 I-84 I-205 Downtown Portland I-405

1/16/ Ground Truth Data Collection Garmin iQue® 544 probe runs collected with GPS- enabled Garmin iQue® devices (~160 hours of driving)  Vehicle location and speed recorded every 3 seconds Data collection  January – May 2007; peak periods  Data collected for all freeways; extra collection on I-5 and OR-217 GIS used to divide data files into individual runs; probe trajectory data stored in PORTAL database

1/16/ Overall Estimation Error N = 544 runs Error Threshold 20%

1/16/ Error Threshold and Metrics Error threshold set at 20% (absolute error) Metrics:  Mean Absolute Error Percent (MAPE)  Standard Deviation of Error Percent (SDPE)  Mean Percent Error (MPE)  Percent of runs with Absolute Percent Error < 20%  Percent of runs with Absolute Percent Error < 30%

1/16/ Segment-by-Segment Analysis Segment Name Len (mi) Avg Spac- ing (mi) MAPESDPEMPENum Runs Pct < 20% Pct < 30% I-5 NB (SoD) I-5 SB (SoD) I-5 NB (NoD) I-5 SB (NoD) OR 217 NB OR 217 SB

1/16/ Large Detector Spacing – I-5 SB SoD Estimated and Probe TrajectoriesDetector and Probe Speeds Run 217, I-5 SB SoD, April 16, :19 PM, 32% under-estimation error

1/16/ Change in Conditions Estimated and Probe TrajectoriesDetector and Probe Speeds Run 307, I-5 NB NoD, April 24, :47 PM, 29% over-estimation error

1/16/ Sources of Error Change in Conditions  Travel time calculated using ‘instantaneous speeds’; however, conditions may change  Tested 1-, 3-, 6-, 9-minute averages Non-functioning Detectors  50% of probe runs had one or more non-functioning detector stations  ODOT considering temporary detection for use during construction or medium-term outages Large Detector Spacing  Detectors placed for ramp metering, placement not suitable for travel time estimates  Identified and analyzed locations where additional detection would be most beneficial

1/16/ Addition of Detectors Analyze benefits of addition of detection  Prioritize locations of additional detection  Understand implications of detector location Detectors simulated using probe vehicle speeds at the location of the ‘virtual detector’ Compared ‘real-time’ travel time estimates with and without the addition of detectors

1/16/ Addition of Detectors

1/16/ Algorithm Comparison Standard Midpoint San Antonio WSDOT (Real-time portion) Mn/DOT MAPE SDPE MPE SE

1/16/ Conclusions Large amount of ground truth data collected and analyzed  Overall average absolute error 11% (SDPE 18%)  15% of runs had absolute errors larger than 20% Three sources of error identified  Changing conditions  Malfunctioning detectors  Large detector spacing Investigated alternative algorithms and high- priority locations for additional detectors

1/16/ Acknowledgements Galen McGill, Dennis Mitchell, Hau Hagedorn, Jack Marchant, Amy Mastraccio (ODOT) ODOT and OTREC Questions?