1. A Preliminary Study: Applications of Smart Phone Truck Data to Develop Freight Performance Measures and Support Transportation Planning Katherine E. Bell, P.E. Dr. Miguel A. Figliozzi 2012 Annual Western District ITE Meeting Santa Barbara, CA Tuesday, June 26 th, 2012 1

2. Outline Project Background Literature Review Data Description & Preliminary Analysis Ancillary Applications & Future Research 2

3. No diesel fuel tax on heavy trucks in Oregon Weight-mile tax: trucks over 26,000 lbs Axle-based weight-mile tax:trucks over 80,000 lbs Mileage currently self-reported Oregon Trucking Online (25% in 2009) Audits using weigh station records Project Background 3 Oregon Commercial Truck Weight-Mile Tax

4. Simplify weight-mile tax collection Smartphone application that tracks satellite GPS data Automated taxing process; view mileage online Reduces administrative burden Reduces reporting errors and tax avoidance February 2010 – ODOT TRUE Pilot Project 3 Motor Carriers, 15 trucks Locations recorded every 5 minutes for over 1 year Project Background 4 Truck Road Use Electronics (TRUE)

5. 5 Proprietary Applications Smartphone Applications in Transportation Travel time estimates INRIX – Urban Mobility Report Xata Turnpike – Fleet management & optimization EROAD – New Zealand truck tax Emissions estimates Literature Review

6. 6 2008 Trip Identification Algorithm – Greaves & Figliozzi 2010 Validation of traffic models, off-peak deliveries – Holguin-Veras et al 2011 Multi-Criteria Performance Measures (Mobility, Emissions, Cost) – Wheeler & Figliozzi Combined GPS data & loop sensor data Emissions estimations using MOVES 2010 2012 Clean Trucks Program – You & Ritchie Identify truck depots Measure truck turnaround time at Ports Academic Research (not a comprehensive review)

7. 7 Project Background

8. 8 Literature Review

9. Data Description 9 Location & Time Data

10. Data Description 10 Link to License Plate

11. Data Description 11 Link to WIM Data

12. Data Description 12 WIM Data

13. Preliminary Analysis 13 Primary Users by Weight Class

14. Preliminary Analysis 14 Class 1030 Counts

15. Next Steps – Short Term 15 Mobility Travel Time Reliability & Speed Measures Compare to PORTAL (Portland Oregon Regional Transportation Archive) Planning & Modeling Emissions Estimations – MOVES (Motor Vehicle Emission Simulator) GreenSTEP Model – Greenhouse Gas Statewide Transportation Emissions Planning Model SWIM2 – ODOT Second Generation StateWide Integrated Model Oregon Freight Plan Data Improvements TT – Travel Time TTR – Travel Time Reliability TTI – Travel Time Index PTI – Planning Time Index

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17. Acknowledgements 17 Oregon Transportation Research and Education Consortium (OTREC) Oregon Department of Transportation (ODOT) Greg Dal Ponte & Gina Salang (MCTD) Becky Knudson (TPAU) & Myra Sperley (Research) Steve Ross, Chris Howell, Peter Douglas, Michael Bolliger (MCAD ) Questions Katherine Bell bell2@pdx.edu Miguel Figliozzi figliozzi@pdx.edu Civil and Environmental Engineering Portland State University

18. 18 Literature Review

19. 19 Future Research Analysis Framework

20. 20 Project Background

21. 21 Freight Performance Measures Mobility, Congestion & Reliability Travel Time Reliability Congestion at bottlenecks System Condition – Maintenance & Preservation Bridge wear – f(Gross Vehicle Weight) Pavement wear – f(axle weights) TT – Travel Time TTR – Travel Time Reliability TTI – Travel Time Index PTI – Planning Time Index cc Pvmt / Bridge Forensics Design Estimates Weight limit violations Applications

22. Freight Performance Measures 22 Environment – Emissions estimates (especially GHG) Safety Accessibility & Connectivity – ports, airports, highways Longer Combination Vehicle (LCV) Network Access Truck turnaround time at ports Equipment Cargo Delay Applications

23. 23 Applications Planning & Modeling Oregon Freight Plan – policy/investments vs. economic forecasts SWIM2 – ODOT Second Generation StateWide Integrated Model EPA MOVES – Motor Vehicle Emission Simulator GreenSTEP Model – Greenhouse Gas Statewide Transportation Emissions Planning Model Trip Generation Rates – Route, vehicle and mode choice