Christopher M. Monsere TAC Meeting 5.21.08 3:30-5:00PM

1. Using Existing ITS Commercial Vehicle Operation (ITS/CVO) Data to Develop Statewide (and Bi-state) Truck Travel Time Estimates and Other Freight Measures Christopher M. Monsere TAC Meeting 5.21.08 3:30-5:00PM

2. Agenda

3. Objectives • 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.

4. Status • Task 1: Literature Review – 100% • Review key issues including sampling, travel time prediction algorithms and issues related to motor carrier travel. • Task 2: Assemble Relevant Data – 95% • Gather existing data from Green Light sites in Oregon and CVISN sites in Washington. • Task 3: Preliminary Data Analysis- 80% • Select test corridor and identify metrics (e.g., the number of transponders read as a percentage of total truck traffic, potential matches at adjacent stations, weather events, etc.) . • Task 4: Experimental Design – 70% • Design potential field experiment that will seek to validate the concept of using the truck transponder data as predictors for travel times and as performance measures. • Task 5: Select Corridors for Field Study and Validation -80% • In consultation with the TAC select corridors to conduct a field study. • Task 6: Conduct Field Studies – 0% • Conduct field studies in the corridors identified in Task 5. • Task 7: Data Analysis – 50% • Develop an algorithm that can filter, match, and estimate link travel times. • Study the data from the field studies to validate the idea of using truck transponder information as travel time probes. • Focus on developing a methodology using the historical and archived Green Light data to develop corridor performance measurements. • Task 8: Reporting - 0% • PSU will prepare a draft final report documenting the results.

5. Literature Review • Focused on four areas • Review of electronic screening programs and truck transponders • Tag matching algorithms (trucks and toll tags) • Signature matching (weight and vehicle) • Freight performance metrics

6. PrePass NORPASS State operated/developed; compatible with NORPASS Electronic screening • Three types of tags • Heavy Vehicle Electronic License Plate (HELP)’s PrePass program • North American Pre-clearance and Safety System (NORPASS) • Oregon Green Light Program • All RF tags J. Lane, Briefing to American Association of State Highway and Transportation Officials (AASHTO), 22 February 2008 freight.transportation.org/doc/hwy/dc08/scoht_cvisn.ppt

7. Washington TRAC • Tags from WIM I-5, I-90, and ports (Seattle, Tacoma, sb Canadian border) • Promising but challenges • Implemented additional tag readers, not yet operational • Discussions with TRAC

8. Tag matching algorithms • Toll transponders • TranStar in Houston, TransGuide in San Antonio, and Transmit in New York / New Jersey • Urban setting, some logic applicable to trucks • Cell phone • License plate

9. Signature matching • Vehicle inductive loop • Freeways or signals • “wave” signal matching • Weight, spacing, other parameters • Christiansen and Hauer (1998) created an algorithm was developed to detect and track freight vehicles with “irregular” axle configurations or axle weights. • Nichols and Cetin(2007) explored the use of axle spacing and axle weight data to re-identify commercial trucks at two WIM stations in Indiana separated by one mile.

10. Freight performance metrics • Not really focus of this study (other ODOT research being conducted) • ODOT PMs

11. Freight performance metrics Using Federal Highway Administration (FHWA) / American Transportation Research Institute (ATRI) proprietary truck satellite data.

12. Freight performance metrics • Average travel times on key corridors • Ton-miles on each corridor by various temporal considerations • Overweight vehicles on corridors by temporal variation (measuring change) • Enforcement effect (i.e. station is open) • Empty vehicles • Seasonal variability in loading, routes, and volumes • Percent trucks with tags on each corridor • Potentially estimating an origin-destination matrix • Average weight for various configurations

13. Preliminary Analysis • 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 • Vehicle class • Speed • Axle weight • Spacing • Transponder tags numbers

14. Stations

15. Data Assembly • April 2005- March 2008 available WIM files • PORTAL - Postgresql database • Raw data files from motor carrier, monthly • OSU text strip program • PSU tag strip • PSU join, upload to database python script • 2007 loaded • 12,054,552 trucks • Intermittent data outages and problems • Data quality

16. Preliminary analysis • Test corridor– I-84 WB • Stations: Farewell Bend, Emigrant Hill, Wyeth • February 2007 • Methodology • Used Excel, limited functionality • Remove trucks without tag • Remove duplicate tags at upstream station from downstream • Matched the tag between adjacent stations and all three stations • Calculate travel time

17. Preliminary analysis Wyeth Emigrant Hill Farewell Bend

18. Preliminary analysis • Duplicate tags between stations • One truck observed 82 times at Wyeth • Farewell Bend-2% • Emigrant Hill -2% • Wyeth-5%

19. Trucks observed at stations Total trucks in sample 12,164 that were observed at Farewell Bend Note: This only includes trucks that left and arrived on the day so actual numbers are likely slightly higher.

25. Truck # 000604262308

26. Preliminary Analysis, Feb 2007 ?

27. Percent of Trucks w/ Tag

28. Candidate Algorithm • Real time or archived? • For j to n • Get tag/transponder from a station • Determine next station • Determine time window • Does tag (station1) match have match at station 2 in time window? • If yes, calculate and build tree • If no, get next tag number • Loop

30. Some measures

31. Proposed Data Collection • Use motor pool fleet customers as probes • Approvals • Approved by PSU human subjects • Approved by DAS

32. Data collection • Need probe vehicles to gather ground truth • Questions • Can trucks estimate car travel times? • How accurate are the “system generated” times • Low power GPS logger • Battery lasts about 1-2 days • 8MB storage Sample Data Tag,$GPGGA,UTC(hhmmss.sss),Latitude,N/S,Longitude,E/W,Fix quality,Number Of Satellites,Horizontal dilution of position,Altitude,Height of geoid,,Checksum Tag,$GPRMC,UTC(hhmmss.sss),A,Latitude,N/S,Longitude,E/W,Speed(knots),Course(degrees),Date(ddmmyy),,Checksum ---,$GPGGA,162807.000,3205.5748,S,11548.6228,E,1,46,226.6,7990.0,M,00.0,M,,*73 ---,$GPRMC,162807.000,A,3205.5748,S,11548.6228,E,0.00,46.00,080800,,*2B ---,$GPGGA,162807.000,3205.5749,S,11548.6228,E,1,46,226.6,8502.0,M,00.0,M,,*7A

34. Sample Mapped GPS data

35. Next steps • Analysis • Load remaining WIM data, get Washington data • Continue to develop and tune archived algorithm • Generate sample performance measures • Probe data • Finishing final tests on use of devices • Develop instructions • Begin small scale collection very soon • Move to large scale soon • Sample size to be determined

36. Questions? • Thank you