Adventures in Crowdsourcing: Traffic Signal Applications (EDC5 Webinar Series)

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Start Date:

February 27, 2020 from 1:30 pm – 3:00 pm EST


Crowdsourcing turns transportation system users into real-time sensors on system performance, providing low-cost, high-quality data on traffic operations, roadway conditions, travel patterns, and more. This webinar will highlight innovative uses of vehicle probe and commercial mobile app data to improve traffic signal operations.

Target Audience

  • Traffic Signal Systems Managers

  • Transportation systems management and operations (TSMO) executives

  • Performance focused Operations Personnel

Learning Objectives

  • Understand how crowdsourced data simplify assessments of signal performance.

  • Learn how agencies are using free and vehicle probe crowdsourced data to shift from cyclical to performance-driven signal retiming.


W. Jared Wall, P.E., Traffic Signal Engineer, Austin Transportation Department, will share their use of crowdsourced data to evolve from cyclical signal retiming to performance-based retiming and their agency’s communications of improvements through their Transportation Data and Performance Hub.

Rahul Jain, District of Columbia Department of Transportation and Tom Knofczynski, Mead and Hunt will share how their agency applied multiple data sources (crowdsourced and traditional) through the RITIS suite to measure performance in downtown Washington DC.

Justin Effinger, Traffic Signal Engineer, Lake County Division of Transportation will share their use of Waze data to support traffic signal monitoring and traveler information as well as plans for use of this data to support weather responsive signal progression and project selection.


Greg Jones, FHWA EDC-5 Crowdsourcing for Operations National Team and FHWA Office of Operations

Vaishali Shah, AEM Corporation


Event Type:


Operations Area of Practice:

  • Regional Traffic Signal Operations & Program Management
  • Traffic Signal Timing
  • Data Acquisition, Support and Hosting
  • Regional environmental data sets and models