Dynamic OD estimation using three phase traffic flow theory

Seungjae Lee, Benjamin Heydecker, Yong Hoon Kim, Eui Young Shon

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Advanced Transportation Management and Information Systems (ATMIS) can use dynamic origin-destination (OD) demand models to make short-term predictions regarding developments in traffic states. However, existing dynamic OD prediction models do not achieve this reliably for two main reasons. First, this is a bi-level system that consists of a traffic flow process at the lower level and a dynamic OD process at the upper level. Due to the inherent non-convexity of bi-level systems, it is difficult to guarantee that any calculated solution is globally optimal. In this paper, we propose a new traffic flow model that uses real-time traffic data, such as traffic flows, speed and occupancy, collected from vehicle detectors, to address the difficulties that arise in existing bi-level programming formulations. Second, in order to estimate a dynamic OD demand between on and off-ramps on the freeways, a traffic flow model is needed to estimate the proportion of traffic moving between them. In this paper, we present a dynamic traffic estimation model based on Kerner's 1 three-phase traffic theory, which represents the complexity of traffic phenomena based on phase transitions between free-flow, synchronized flow and moving jam phases, and on their complex nonlinear spatio-temporal features. The present model explains and estimates traffic congestion in terms of speed breakdown, phase transition and queue propagation. We show how a genetic algorithm can be used to solve this to estimate dynamic OD flows and the associated link, on and off-ramp flows during each time interval using traffic data collected from vehicle detection systems implemented on Korean freeways.

Original languageEnglish
Pages (from-to)143-158
Number of pages16
JournalJournal of Advanced Transportation
Issue number2
StatePublished - Apr 2011


  • dynamic OD estimation
  • dynamic traffic flow model
  • flow breakdown


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