Data-Driven Mobility Strategies for Multi-Modal Transportation

By using various modes (e.g., walking, cycling, automobile, public transit, etc.), multi-modal transportation systems are effective in increasing people’s travel flexibility and reducing congestion. Hence, it is critical to understand how roadway speed management strategies would affect people’s mode choices. Additionally, with advanced technology, such as connected autonomous vehicle systems, we are now facing a transition from traditional urban planning to developing smart cities. To support multimodal transportation planning, this project will pave a bridge to connect speed management strategies of conventional signalized arterial to connected vehicle corridor. The research outcomes will help decision-makers understand the data and infrastructure needs in supporting future multimodal planning tasks and speed management. Multiple data resources, such as Pems and ATSPM from UDOT and traffic sensor data from PCDOT, will be used for this study. The research team, from U of Utah and U or Arizona, will develop data-driven approaches to achieve three primary objectives. The first objective of this project is to evaluate arterial speed management plans and investigate the impact of deploying speed feedback signs. The team will explore the relationship among speed feedback signs, posted speed limit enforcement, and intersection capacity, and investigate how these features may impact multi-modal transportation mobility and safety. Particularly, the team will study how buses, vehicles, pedestrians, and bicyclists are affected by the current speed management strategies. The second objective is to understand the role of speed management strategies in supporting smart city operational functions. Starting from 2016, UDOT has launched a project to build a full Dedicated-Short-Range-Communications (DSRC) corridor for CV technology testing. In this project, the team will work closely with UDOT for studying the impact of multi-modal speed management plans on the CV corridor. The last objective is to utilize big data to understand the interrelations among speed management, safety, congestion, travelers’ route choice. The research findings will help the cities be prepared for the coming of shared self-driving cars.

Language

  • English

Project

  • Status: Active
  • Funding: $303397
  • Contract Numbers:

    NITC-1298

  • Sponsor Organizations:

    University of Arizona, Tucson

    PO Box 210072
    Tucson, AZ  United States  85721

    University of Utah

    Department of Civil and Environmental Engineering
    110 Central Campus Drive Suite 2000
    Salt Lake City, UT  United States  84112

    Portland State University

    Department of Civil and Environmental Engineering
    Engineering Bldg, 301D, 1930 SW 4th Ave.
    Portland, OR  United States  97201

    Pima County Public Works Administration

    130 W. Congress, 10th floor
    Tucson, Arizona  United States  85701

    Office of the Assistant Secretary for Research and Technology

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

    TREC at Portland State University

    1900 SW Fourth Ave, Suite 175
    P.O. Box 751
    Portland, Oregon  United States  97201
  • Performing Organizations:

    University of Arizona

    College of Engineering
    1209 East 2nd Street
    Tucson, AZ  United States  85721

    University of Utah, Salt Lake City

    College of Engineering, Department of Civil Engineering
    Salt Lake City, UT  United States  84112-0561

    Portland State University

    Department of Civil and Environmental Engineering
    Engineering Bldg, 301D, 1930 SW 4th Ave.
    Portland, OR  United States  97201
  • Principal Investigators:

    Wu, Yao-Jan

    Yang, Xianfeng

    Kothuri, Sirisha

  • Start Date: 20190901
  • Expected Completion Date: 20201130
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01710783
  • Record Type: Research project
  • Source Agency: National Institute for Transportation and Communities
  • Contract Numbers: NITC-1298
  • Files: UTC, RiP
  • Created Date: Jul 11 2019 6:35PM