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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

Agenda

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Item Length

Introductions, agenda review 5

Discussion of literature review 15

Some results of the preliminary analysis 25

Proposed data collection method 20

Next steps 15

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.

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.

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.

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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

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Electronic screening

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• 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

State operated/developed; compatible with NORPASS

PrePassNORPASS

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

Washington TRAC

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• 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

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

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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.

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Freight performance metrics

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• Not really focus of this study (other ODOT research being conducted)

• ODOT PMs

Freight performance metrics

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

Freight performance metrics

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– 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

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

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Stations

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

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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

Farewell Bend

Emigrant HillWyeth

Preliminary analysis

Preliminary analysis

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

Stations Truck with Tag

Non Tag

Farewell Bend 38% 62%

Emigrant Hill 44% 56%

Wyeth 48% 52%

Trucks observed at stations

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FB to EH 9,075 53%

EH to WT 2,834

FB to WT 3,028 23%

Note: This only includes trucks that left and arrived on the day so actual numbers are likely slightly higher.

Total trucks in sample 12,164 that were observed at Farewell Bend

Truck # 000604262308

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S timestamp3 WYT 2007-01-24 23:47:59.05-084 CSL 2007-01-25 12:03:32.80-085 LGR 2007-01-25 15:58:16.80-086 ODF 2007-01-25 17:35:16-082 EMH 2007-01-27 02:07:53.22-085 LGR 2007-01-27 21:01:29.64-086 ODF 2007-01-27 22:35:30.64-082 EMH 2007-01-30 19:19:41.52-083 WYT 2007-01-30 22:12:21.95-084 CSL 2007-01-31 09:25:34.92-085 LGR 2007-01-31 13:18:04.80-086 ODF 2007-01-31 14:48:09.74-081 FWB 2007-02-01 21:39:58.96-085 LGR 2007-02-03 00:07:47.14-086 ODF 2007-02-03 01:37:59.62-081 FWB 2007-02-04 22:04:19.98-082 EMH 2007-02-05 00:13:53.86-085 LGR 2007-02-06 00:47:01.86-086 ODF 2007-02-06 02:21:25.46-081 FWB 2007-02-07 00:05:55.78-082 EMH 2007-02-07 02:20:30.72-083 WYT 2007-02-07 05:53:34.98-084 CSL 2007-02-07 18:35:45.66-08snip6 ODF 2007-02-15 12:15:53.74-084 CSL 2007-03-03 03:22:14.20-085 LGR 2007-03-03 07:24:06.32-086 ODF 2007-03-03 08:59:05.90-082 EMH 2007-03-05 07:14:37.46-085 LGR 2007-03-06 05:49:08.14-086 ODF 2007-03-06 07:20:45.56-087 ASP 2007-03-11 16:51:50.08-078 BOR 2007-03-11 19:56:55.18-079 WDN 2007-03-11 22:43:21.54-0710 WDS 2007-03-12 00:29:46.76-07

Preliminary Analysis, Feb 2007

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Percent of Trucks w/ Tag

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Months (2007)1 2 3 4 5 6 7 8 9 10 11 12 Row

1 FWB 37% 27% 32% 43% 39% 38% 37% 38% 38% 41% 42% 47% 38%2 EMH 47% 45% 43% 44% 44% 43% 43% 46% 29% 45% 43%3 WYT 49% 48% 47% 47% 48% 46% 47% 46% 47% 50% 51% 48%4 CSL 49% 48% 47% 47% 47% 47% 46% 47% 47% 49% 47%5 LGR 45% 45% 44% 43% 43% 43% 43% 43% 43% 43% 45% 47% 44%6 ODF 41% 38% 33% 29% 26% 24% 32% 22% 23% 25% 27% 31% 29%7 ASP 42% 45% 45% 46% 46% 45% 44% 47% 49% 48% 48% 47% 46%8 BOR 37% 37% 36% 36% 36% 36% 36% 32% 39% 39% 41% 37%9 WDN 43% 44% 42% 43% 43% 32% 34% 35% 37% 37% 39% 39%10 WDS 43% 43% 34% 28% 23% 40% 36% 40% 41% 44% 30% 45% 37%11 BRE 5% 26% 30% 29% 34% 36% 34% 27%12 BRW 38% 37% 38% 33% 32% 32% 29% 27% 32% 37% 40% 33%13 JBS 36% 38% 34% 33% 33% 32% 30% 30% 30% 33% 36% 37% 33%14 LWL 18% 30% 28% 28% 26% 25% 24% 26% 25% 25% 15% 31% 24%16 ASS 40% 42% 42% 42% 42% 42% 41% 39% 40% 40% 41% 41% 41%17 KFP 35% 35% 33% 33% 33% 33% 31% 34% 34% 34% 36% 36% 34%18 BND 33% 33% 27% 22% 26% 28% 31% 28%19 JBN 38% 40% 36% 35% 77% 31% 26% 27% 33% 36% 36% 41% 36%20 KFS 31% 30% 31% 32% 33% 23% 31% 31% 30% 32% 30%Column 42% 42% 40% 39% 36% 38% 38% 38% 38% 38% 38% 42% 39%

Stations

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

Farewell Bend POE I-84 WB, MP 353.31

Emigrant Hill, I-84 WB, MP 226.95

Wyeth, I-84 WB, MP 54.3,

Juniper Butte, US-97 SB, MP 108.2

Klamath Falls (SB), US-97 SB, MP 271.41

Brightwood, US-26 WB, MP 36.31

Lowell, US-58 WB, MP 17.17

2h

Wilbur, I-5 SB, MP 130

Ashland, I-5 SB, MP 18.08

Woodburn, I-5 SB, MP 274.18

Wilbur, I-5 SB, MP 130

Ashland, I-5 SB, MP 18.08

Woodburn, I-5 SB, MP 274.18

Wilbur, I-5 SB, MP 130

Ashland, I-5 SB, MP 18.08

1h20m

2h30m

2h

1h10m

2h

1h

2h30m

2h

4h2h50

1h40m

2h30m

1h10m

Washington

2h50m

Some measures

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Proposed Data Collection

• Use motor pool fleet customers as probes• Approvals

– Approved by PSU human subjects– Approved by DAS

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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

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Tag,$GPGGA,UTC(hhmmss.sss),Latitude,N/S,Longitude,E/W,Fix quality,Number Of Satellites,Horizontal dilution of position,Altitude,Height of geoid,,ChecksumTag,$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

Sample Data

Sample Mapped GPS data

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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

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Questions?

• Thank you

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