Teaching the travel demand flow estimation models: a new deep-learning approach using multi-source data

The amount of data that transportation systems collect and store today has reached a new level compared with the previously traditional collection methods. For example, as one of pioneering Metropolitan Planning Organizations (MPO) in the United States in transportation planning, Maricopa Association of Governments (MAG) can provide the regional household travel survey data of 60GB, 1-year TMC-based speed data of 26GB, and 1-year link-based speed data of 3.1TB. Facing those unstructured and structured data streaming in from heterogeneous sensor sources at an unprecedented rate, it is critical to quickly manage and mine useful information under control. Also, it should be always aware that the value of those big data is reflected not just by its high volume but also by what specific goals/problems the data are used for. With the development of new computing technologies, machine learning has currently evolved as a powerful tool to learn from data with independent adaptions to generate reliable, repeatable decisions and results in a variety of application areas. However, compared with the areas in automotive, financial services, healthcare and etc., the open-source learning materials, studies and applications of machine learning on transportation system planning and operations are still relatively weak and need to be enhanced and taught to students, researchers and practitioners in time. Focusing on the Traffic Demand Flow Estimation (TDFE) problem, which infers the number of persons/vehicles travelling between a particular origin and destination via a particular route/link, this project aims to support the education and training of relative students, researchers and practitioners to understand and learn the knowledge of deep learning and its application procedure by developing a step-by-step tutorial and open-source software packages and providing a number of well-organized workshops. This project will develop a step-by-step tutorial and a self-study software package and organize several workshops for students, researchers and practitioners to learn deep-learning approaches and how to apply it to estimate and calibrate the travel demand flow and behavioral models with available multi-source sensor data.

    Language

    • English

    Project

    • Status: Active
    • Funding: $75000
    • Contract Numbers:

      69A3551747116

    • Sponsor Organizations:

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590
    • Managing Organizations:

      Center for Teaching Old Models New Tricks

      Arizona State University
      Tempe, AZ  United States  85287
    • Performing Organizations:

      Center for Teaching Old Models New Tricks

      Arizona State University
      Tempe, AZ  United States  85287
    • Principal Investigators:

      Zhou, Xuesong

    • Start Date: 20191001
    • Expected Completion Date: 20211001
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers Program

    Subject/Index Terms

    Filing Info

    • Accession Number: 01755252
    • Record Type: Research project
    • Source Agency: Center for Teaching Old Models New Tricks
    • Contract Numbers: 69A3551747116
    • Files: UTC, RiP
    • Created Date: Oct 21 2020 8:07PM