1. Status Report: Evaluation of Private Sector Data in Minneapolis Shawn Turner shawn-turner@tamu.edushawn-turner@tamu.edu, 979-845-8829 Texas Transportation Institute Mobility Measurement Pooled Fund Study Baltimore, July 22, 2008

2. Average Speed Data Provided by Inrix 15-minute average speeds for each day of the week – from Jan 2006 through Feb 2008 (26 months) – Location – Day-of-week/time (672 bins) – Average speed – Number of data samples 60-minute average speeds for each day of the week and month of the year - from Jan 2006 through Feb 2008 (26 months) – Location – Day-of-week/time (168 bins) for 26 months – Average speed – Number of data samples 2

3. Reliability Statistics Provided by Inrix 15-minute by day-of-week statistics from speed distribution – Location – Day-of-week/time – Average speed – Speed percentiles: 10, 15, 25, 50, 85 – Failure rate – frequency of time below 30, 50, 60 mph 3

4. Other Data Provided by Inrix Location referencing tables – The decoder ring for locations – TMC: Traffic Message Channel, a location referencing method (de facto standard) that started in Europe and is popular among traffic information and navigation/map database companies 4

5. Analysis Steps Translate TMC linear referencing Aggregate data to longer road segments Visual checks of basic traffic patterns/trends Compare data with other sources – MnDOT freeway fixed-point detectors (I-494) – MnDOT manual floating car signal timing runs Sample size analysis (Confidential) Merge with HPMS/other DOT databases (TBD) Calculate performance measures (TBD) 5

6. Case Study Corridors 6 MN 65 US 61 US 10/US 61 MN 7 MN 55 US 169 US 10/US 169 CO HWY 14 I-494 HWY 13

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12. Comparison with MnDOT Fixed- Point Detectors along I-494 Not apples-apples comparison Fixed vs. mobile point speed measurement Population vs. GPS probe sample 12

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17. Comparison with MnDOT Test Vehicle Runs (Signal Retiming) Still not apples-apples comparison Segment level – not link-by-link Specific days vs. monthly average 17

18. 3 Comparison Corridors MN 7 MN 65 MN 55 18

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33. Preliminary Findings-1 Samples (actual numbers confidential) – More than you get from special studies, but spread over numerous days – Current probes are mostly fleet, but vehicle mix will evolve to include consumer vehicles – Samples have increased 2-3X since 2006 – Samples most dense during workday, few during overnight or weekends (but overnight/weekend not critical for congestion analysis) 33

34. Preliminary Findings-2 Average Speeds – Visual checks show reasonable data during workday hours – Inrix speeds consistently slower than MnDOT freeway detectors Vehicle bias – Inrix mostly commercial vehicles Speed measurement – Mn/DOT has fixed locations – Inrix speeds in range of arterial travel time runs – Larger month-to-month variation on arterial streets than freeways 34

35. Conclusions Applications – Segment level, maybe link (not intersection) – Historical average speeds Hourly values on quarterly or maybe monthly 15-minute values on at least annual basis – Reliability statistics Have not yet analyzed Inrix failure statistics Instinct says we need more samples to represent day- to-day variation 35

36. Conclusions Possible Applications – Identifying most congested routes or segments for further study and improvement – Ranking or prioritizing routes for funding based on average congestion levels – Monitoring and identifying routes with the highest increase in average congestion levels (on monthly or annual basis) – Using as a general indicator in before-after studies of mobility improvements – Developing performance measures on an aggregate basis. 36

37. Recommendations Proceed with more in-depth pilot/feasibility efforts – produce prototype reports – Have only taken preliminary look – Still some work needed before we get performance measures – Private sector data will improve over time 37

38. Implications for Future There will be a private sector market for historical speeds/travel times – How can DOTs take advantage of this? – How to integrate data and address VMT? Different data sources – Each measurement technique has bias/error (until we have GPS probes everywhere) – Use caution in combining/comparing data from different measurement techniques – Adjusting for method bias? 38