Stochastic Multimodal Network Modeling

A broad range of transit data are available that can be mined beyond performance analysis for the current state of the system. Such data will enhance the current models of transit and multimodal systems, and make possible the application of innovative models in long-term planning of regional networks and in real-time operations management. The research team is proposing a data-intensive approach to model transit and multimodal systems using existing and new intelligent transportation systems (ITS) data. It includes stochastic transit network representation, user behavior modeling under uncertainty, reliability-based routing, assignment, and network design, and lastly, application of the models towards long-term planning and operational management. Powerful map-matching algorithms for processing the network data will be the first step taken toward more realistic and detailed modeling of transit and multimodal networks. Moreover, statistical modeling tools will be developed to process historical automatic passenger counting (APC) and automatic vehicle location (AVL) data and to develop a stochastic transit network. This underlying stochastic network will facilitate system modeling and decision making for the varying levels of reliability over the course of a day, week or year, as opposed to modeling a typical day. Moreover, the impact of external conditions such as weather or incidents on transit performance can be built into the methodological framework, improving existing models and positioning for future applications.

  • Supplemental Notes:
    • Contract to a Performing Organization has not yet been awarded.


  • English


  • Status: Completed
  • Funding: $95000
  • Contract Numbers:


  • Sponsor Organizations:

    Center for Advanced Multimodal Mobility Solutions and Education

    University of North Carolina, Charlotte
    Charlotte, NC  United States  28223
  • Managing Organizations:

    University of North Carolina, Charlotte

    Department of Civil and Environmental Engineering
    9201 University City Boulevard
    Charlotte, NC  United States  28223-0001
  • Project Managers:

    Fan, Wei

  • Performing Organizations:

    University of Connecticut, Storrs

    Department of Civil and Environmental Engineering
    Storrs, CT    06268-5202
  • Principal Investigators:

    Konduri, Karthik

  • Start Date: 20170120
  • Expected Completion Date: 20180930
  • Actual Completion Date: 20180930

Subject/Index Terms

Filing Info

  • Accession Number: 01625947
  • Record Type: Research project
  • Source Agency: Center for Advanced Multimodal Mobility Solutions and Education
  • Contract Numbers: 69A3551747133
  • Files: UTC, RIP
  • Created Date: Feb 14 2017 3:53PM