Modeling Origin-Destination Uncertainty Using Network Sensor and Survey Data and New Approaches to Robust Control

Lee K. Jones, Nathan H. Gartner, Michael Shubov, Chronis Stamatiadis, David Einstein

Research output: Contribution to journalArticlepeer-review


This study develops new methods for network assessment and control by taking explicit account of demand variability and uncertainty using partial sensor and survey data while imposing equilibrium conditions during the data collection phase. The methods consist of rules for generating possible origin-destination (OD) matrices and the calculation of average and quantile network costs. The assessment methodology leads to improved decision-making in transport planning and operations and is used to develop management and control strategies that result in more robust network performance. Specific contributions in this work consist of: (a) Characterization of OD demand variability, specifically with or without equilibrium assumptions during data collection; (b) Exhibiting the highly disconnected nature of OD space demonstrating that many current approaches to the problem of optimal control may be computationally intractable (c) Development of feasible Monte Carlo procedures for the generation of possible OD matrices used in an assessment of network performance; and (d) Calculation of robust network controls, with state-of-the-art cost estimation, for the following strategies: Bayes, p-quantile and NBNQ(near-Bayes near-Quantile). All strategies involve the simultaneous calculation of controls and equilibrium conditions.

Original languageEnglish
Pages (from-to)887-900
Number of pages14
JournalTransportation Research Procedia
StatePublished - 2017
Externally publishedYes


  • OD uncertainty
  • disconnected OD space
  • near-Bayes near-Quantile strategy
  • robust optimization
  • signal control


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