A Kalman filter approach to dynamic OD flow estimation for urban road networks using multi-sensor data

Zhenbo Lu, Wenming Rao, Yao-jan Wu, Li Guo, Jingxin Xia

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

Considerable efforts have been devoted to the development of dynamic origin-destination (OD) estimation models, which are a key step to realizing self-adaptive traffic control systems for urban traffic management. However, most of the models proposed to date estimate OD flows based on a single traffic data source, and their performance is limited by the coverage and accuracy of traffic sensors. The inherent difficulty in estimating the dynamic traffic assignment matrix means that dynamic OD estimation remains a challenge for real-life applications. This paper proposes the use of a Kalman filter for dynamic OD estimation using multi-source sensor data. The dynamic characteristic of changing OD flow over time is analyzed, and the problem of dynamic OD estimation is converted to a problem of estimating OD structural deviation. The resulting dynamic relationship between traffic volume and OD structural deviation is then used to establish the Kalman filter model. An improved traffic assignment approach is developed and embedded into the measurement equation of the Kalman filter model to enable dynamic updating of the traffic assignment matrix. A dual self-adaptive mechanism based on the Kalman filter is used to calibrate the model. The proposed method was implemented on a real-life traffic network in the downtown area of Kunshan City, China. The results show that the proposed method is more accurate than, and outperforms, the traditional link-volume-based and turning-movement-based methods.

Original languageEnglish (US)
Pages (from-to)210-227
Number of pages18
JournalJournal of Advanced Transportation
Volume49
Issue number2
DOIs
StatePublished - Mar 1 2015

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Kalman filters
Sensors
Road network
Destination
Sensor
Kalman filter
Traffic control
Control systems

Keywords

  • dynamic OD estimation
  • dynamic traffic assignment
  • Kalman Filter
  • traffic sensors
  • traffic simulation
  • urban network

ASJC Scopus subject areas

  • Strategy and Management
  • Economics and Econometrics
  • Mechanical Engineering
  • Computer Science Applications
  • Automotive Engineering

Cite this

A Kalman filter approach to dynamic OD flow estimation for urban road networks using multi-sensor data. / Lu, Zhenbo; Rao, Wenming; Wu, Yao-jan; Guo, Li; Xia, Jingxin.

In: Journal of Advanced Transportation, Vol. 49, No. 2, 01.03.2015, p. 210-227.

Research output: Contribution to journalArticle

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