1 / 13

Using Cell Phone Technology to Collect Travel Data D. Kyle Ward May 11, 2011

Using Cell Phone Technology to Collect Travel Data D. Kyle Ward May 11, 2011. Outline. Intro to Tech and Pilot Program Application to MPO Business Model Congestion Management Process Travel Demand Modeling Pilot Results Future of the Technology Origin-Destination Estimation

roanna-barnett
Download Presentation

Using Cell Phone Technology to Collect Travel Data D. Kyle Ward May 11, 2011

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Using Cell Phone Technology to Collect Travel DataD. Kyle WardMay 11, 2011

  2. Outline • Intro to Tech and Pilot Program • Application to MPO Business Model • Congestion Management Process • Travel Demand Modeling • Pilot Results • Future of the Technology • Origin-Destination Estimation • Viability vs. Competing Technology

  3. Intro to 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 • Demographics

  4. 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

  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 counts

  7. Pilot Results 5:30 pm

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

  9. Pilot Results • Speed profiles • Peak vs All Day Congestion • Weekend Congestion

  10. Pilot Results • Concerns • Validation of Results? • Purchase from different vendor/tech • Default speed data capped at speed limit • Poor posted speed data impacts results • Uncapped speeds prone to outliers • Solution: establish reference speeds

  11. Future Applications • Modeling of Non-Recurring Congestion • 5-minute interval data reporting • Origin-Destination Estimation • Currently being piloted by the MPO/Airsage • Requires enormous amount of observations • Currently not possible with GPS • Estimation of all trips, regardless of purpose

  12. Competing Tech • Infrastructure-based monitoring • Example: Bluetooth • Expensive • Limited Coverage • GPS • More accurate • Less samples (but growing) • GPS-enabled devices doubling every year?

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

More Related