Dynamic origin-destination trip demand estimation for subarea analysis

Xuesong Zhou, Sevgi Erdoǧan, Hani S. Mahmassani

Research output: Chapter in Book/Entry/PoemConference contribution

24 Scopus citations


Subarea analysis capability is needed in conjunction with dynamic network analysis models to allow consideration and rapid evaluation of a large number of scenarios and to support transportation network planning and operations decisions for situations that may not require analysis on a complete network representation. With a focus on how to provide an up-to-date time-dependent origin-destination (O-D) demand matrix for the subarea network, a two-stage subarea demand estimation procedure is described. The first stage uses path-based traffic assignment results from the original network to generate an induced O-D demand matrix for the subarea network. The second stage incorporates an iterative bilevel subarea O-D updating procedure to find a consistent network flow pattern, using the induced O-D demand information and archived traffic measurements in the subarea network. An excessdemand traffic assignment formulation is adopted to model the external trips that traverse or bypass the subarea network. This formulation allows vehicular flow to respond to traffic conditions resulting from network and operational changes in the subarea, and it can be adequately interpreted in an entropy maximization framework. The proposed procedure is illustrated in a case study using the Los Angeles subarea network extracted from the Southern California Association of Governments regional transportation planning network.

Original languageEnglish (US)
Title of host publicationNetwork Modeling 2006
PublisherNational Research Council
Number of pages9
ISBN (Print)0309099730, 9780309099738
StatePublished - 2006
Externally publishedYes

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering


Dive into the research topics of 'Dynamic origin-destination trip demand estimation for subarea analysis'. Together they form a unique fingerprint.

Cite this