Intelligent simulation and prediction of traffic flow dispersion

Fengxiang Qiao, Hai Yang, Hing Keung William Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

45 Citations (Scopus)


Dispersion of traffic flow on urban road segments is often described by some typical statistical models such as the normal distribution model and the geometric distribution model. These probability-based models can fit traffic flow well under ideal physical environments but may not work satisfactory in certain complex cases because of their strict mathematical assumptions. A neural network-based system identification approach is used to establish an auto-adaptive model for simulating traffic flow dispersion. This model, being feasible to a wide variety of traffic circumstances, can be calibrated and used for on-line traffic flow forecasting. Data simulation and field-testing show reliable performance of the proposed intelligent approach.
Original languageEnglish
Pages (from-to)843-863
Number of pages21
JournalTransportation Research Part B: Methodological
Issue number9
Publication statusPublished - 1 Jan 2001


  • Flow measurement
  • Identification
  • Neural networks
  • Simulation
  • Traffic control

ASJC Scopus subject areas

  • Transportation
  • Management Science and Operations Research

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