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Leveraging Cell and GPS Data for Effective Transportation Management

This presentation by D. Kyle Ward discusses the integration of cell and GPS data in managing congestion and travel demand modeling for MPOs (Metropolitan Planning Organizations). It outlines the technology behind cell-tower triangulation and GPS tracking, compares their accuracy and applications, and details the impact on congestion management processes. Key findings include speed and origin-destination analyses based on real-time data. Privacy concerns are addressed alongside the potential for future advancements in data collection methods, enhancing transportation efficiency in urban areas.

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Leveraging Cell and GPS Data for Effective Transportation Management

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  1. Implementing Cell & GPSData SetsD. Kyle WardMay 3, 2012

  2. Outline • Intro to Tech • Application to MPO Business Model • Congestion Management Process • Travel Demand Modeling • Results • Speed • Origin-Destination • Concerns • Looking Forward

  3. Review of Cell Tech • Uses cell-tower triangulation to locate cellular devices • Similar to GPS, but less accurate • Ubiquitous cell phone use compared to GPS • Numerous metrics can be estimated • Speeds • Origin – Destination • Demographics

  4. Review of GPS Tech • Targets mobile GPS devices • Smart Phones • In-vehicle systems like OnStarTM • Benefit/Cost • More accurate • Smaller sample size

  5. Application to MPO • Congestion Management Process • Yearly data collection program • Before and After Metrics • Congestion Monitoring • Project Identification • Project Programming

  6. Application to MPO • Travel Demand Modeling • Validation of Speeds and Travel Time • Volume-Delay Functions • Accurate Free-Flow Speeds? • Speed data matched to hourly counts • Origin-Destination Validation • In the future, this data could be used to support non-recurring congestion models

  7. Cell Speed Pilot • Contracted with AirSage • 834 centerline miles of coverage • Significant arterial coverage • 24/7 for March, 2010 • Data scrubbed of personal information • Fraction the cost of traditional methods

  8. 4:15 pm 4:30 pm 4:45 pm 5:15 pm 5:30 pm 5:00 pm • Queue backs up along I-40 • First signs of slowing • I-40 at Wade Avenue • US 70 between Lynn and Millbrook • Bottleneck at Wade • Delay peaks on all routes • US 70 bottlenecks • I-440 begins to slow • I-40 congestion worsens 2/6 1/6 3/6 4/6 5/6 6/6 Speed Results Source: AirSage Cell Data

  9. Speed Results • Speed profiles • Peak vs All Day Congestion • Weekend Congestion Source: AirSage Cell Data

  10. Origin-Destination • Contracted with AirSage to perform OD • Research Project • Describe travel among ~200 Census Tracts

  11. OD Comparison • Comparing three data sets • Expanded Household Survey • AirSage OD • Triangle Regional Model (version 5)

  12. Target Area Downtown Raleigh, NC

  13. Trip Totals • Total Trips to Downtown Raleigh

  14. Trips to Raleigh, NC Expanded Household Survey

  15. Trips to Raleigh, NC AirSage Expanded Survey

  16. Trips to Raleigh, NC Triangle Regional Model (V5)

  17. OD Conclusions • Discrepancies in total number of trips • AirSage seems to capture travel pattern • Want to re-run with new Verizon sample • Regional model “smears” trips • Other areas were tested where the three datasets aligned much closer

  18. Concerns • Number 1 Concern: Privacy • Three key points • No personal data • Aggregated Data • Liability is with data provider – not user

  19. Speed Concerns • Handling of Signal Delay • Cell data appears to capture • GPS does not (possibly too accurate?) • Speed Capping • Default datasets capped at posted speeds • INRIX global cap of 65 mph • Poor posted speed data impacts results

  20. Looking Forward • Work continues with data companies • Remove speed caps • Capture signal delay • New cell products tested in other regions • Select link • Sub-area cordon surveys • Corridor surveys

  21. Questions? • D. Kyle Ward, EI • Transportation Engineer • (919) 996-4395 • Kyle.Ward@campo-nc.us

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