Updating of travel behavior model parameters and estimation of vehicle trip chain based on plate scanning

Treerapot Siripirote, Agachai Sumalee, David P. Watling, Hu Shao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)


This article proposes a maximum-likelihood method to update travel behavior model parameters and estimate vehicle trip chain based on plate scanning. The information from plate scanning consists of the vehicle passing time and sequence of scanned vehicles along a series of plate scanning locations (sensor locations installed on road network). The article adopts the hierarchical travel behavior decision model, in which the upper tier is an activity pattern generation model, and the lower tier is a destination and route choice model. The activity pattern is an individual profile of daily performed activities. To obtain reliable estimation results, the sensor location schemes for predicting trip chaining are proposed. The maximum-likelihood estimation problem based on plate scanning is formulated to update model parameters. This problem is solved by the expectation-maximization (EM) algorithm. The model and algorithm are then tested with simulated plate scanning data in a modified Sioux Falls network. The results illustrate the efficiency of the model and its potential for an application to large and complex network cases.
Original languageEnglish
Pages (from-to)393-409
Number of pages17
JournalJournal of Intelligent Transportation Systems: Technology, Planning, and Operations
Issue number4
Publication statusPublished - 1 Oct 2014


  • EM algorithm
  • Maximum-likelihood estimation
  • Plate scanning
  • Trip chaining

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Automotive Engineering
  • Aerospace Engineering
  • Computer Science Applications
  • Applied Mathematics

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