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Maisha Mahmud ITS Lab Meeting Wednesday, March 19, 2008

Using Existing ITS Commercial Vehicle Operation (ITS/CVO) Data to Develop Statewide (and Bi-state) Truck Travel Time Estimates and Other Freight Measures. Maisha Mahmud ITS Lab Meeting Wednesday, March 19, 2008. Project Objective.

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Maisha Mahmud ITS Lab Meeting Wednesday, March 19, 2008

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  1. Using Existing ITS Commercial Vehicle Operation (ITS/CVO) Data to Develop Statewide (and Bi-state) Truck Travel Time Estimates and Other Freight Measures Maisha Mahmud ITS Lab Meeting Wednesday, March 19, 2008

  2. Project Objective • Study the feasibility of using transponder data from commercial vehicles to predict corridor travel times with existing infrastructure • Retrospectively study truck transponder data in key corridors to determine the feasibility of producing freight corridor performance measures. WINTER PASS DRIVING UPDATES Thursday, Jan. 31, 11:45 a.m. DESCRIPTION: I-84, one of the primary east-west routes through northern Oregon, is closed from Pendleton, Oregon to Ontario, Oregon, which are both east of the interchange of I-82 and I-84.

  3. ProjectBackground Oregon Green Light • 20 active reporting WIM stations • 4,013 trucking companies • 40,606 trucks equipped with transponders enrolled in the preclearance program (March 08) • These WIM stations provide: • Gross Vehicle Weight • Axle Weight • Spacing • Transponder Tags

  4. Stations

  5. Preliminary analysis • Why we need preliminary analysis? • Large data set (20 GB) • Data from 2005-current • Wealth of information like GVW, Speed, axle load, axle spacing, etc, need close observation. • Find how many trucks have tag, of them % valid match,% outside match. • How many times a single tag repeated within a single station. • Find how truck choose their possible routes within a corridor and estimate their travel time. • Finally set a logic for possible matching.

  6. Preliminary Analysis, Feb 2007 • Test corridor– I-84 WB • Stations: Farewell Bend, Emigrant Hill, Wyeth • Analysis duration: February 2007 • Methodology: • First checked how many trucks have tag in each station. • Clean data for without tag • Make them unique by adding Tag_Day_Month. • Remove duplicate tags at each station • Matched the tag between adjacent stations and all three stations. • Calculated travel time.

  7. Preliminary Analysis, Feb 2007 • Highest number of observations of an individual tag at a station: • 82 times in Wyeth for a month. • Duplicate tags between stations Farewell Bend-2% Emigrant Hill -2% Wyeth-5%

  8. Preliminary Analysis, Feb 2007 Wyeth Emigrant Hill Farewell Bend

  9. Preliminary Analysis, Feb 2007

  10. Preliminary Analysis, Feb 2007

  11. Preliminary Analysis, Feb 2007

  12. Preliminary Analysis, Feb 2007 ?

  13. Probable Algorithm • Check if truck have tag/transponder within a station • If truck have tag make it “tag_day_month” • Check if tag_day_month (station1) match with tag_day_month (station 2) • If count (match)=1 • Get travel time= station 2 (travel time)-station1(travel time) • If count(match)>1 • Exclude(count) • End.

  14. Some Freight Measures Gross vehicle weight variation and loading distribution between these three station with matched tag.

  15. Questions?

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