On the variance of recurrent traffic flow for statistical traffic assignment

Wei Ma, Zhen (Sean) Qian

Research output: Journal article publicationJournal articleAcademic researchpeer-review

23 Citations (Scopus)


This paper generalizes and extends classical traffic assignment models to characterize the statistical features of Origin-Destination (O-D) demands, link/path flow and link/path costs, all of which vary from day to day. The generalized statistical traffic assignment (GESTA) model has a clear multi-level variance structure. Flow variance is analytically decomposed into three sources, O-D demands, route choices and measurement errors. Consequently, optimal decisions on roadway design, maintenance, operations and planning can be made using estimated probability distributions of link/path flow and system performance. The statistical equilibrium in GESTA is mathematically defined. Its multi-level statistical structure well fits large-scale data mining techniques. The embedded route choice model is consistent with the settings of O-D demands considering link costs that vary from day to day. We propose a Method of Successive Averages (MSA) based solution algorithm to solve for GESTA. Its convergence and computational complexity are analyzed. Three example networks including a large-scale network are solved to provide insights for decision making and to demonstrate computational efficiency.

Original languageEnglish
Pages (from-to)57-82
Number of pages26
JournalTransportation Research Part C: Emerging Technologies
Publication statusPublished - Aug 2017
Externally publishedYes


  • Data driven
  • Demand variance
  • Probability distribution
  • Route choice variance
  • Statistical traffic assignment
  • Variance decomposition

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications


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