Title page for ETD etd-08232007-112620

Type of Document Dissertation
Author Özbay, Kaan
URN etd-08232007-112620
Title A framework for dynamic traffic diversion during non-recurrent congestion :models and algorithms
Degree PhD
Department Civil Engineering
Advisory Committee
Advisor Name Title
Hobeika, Antoine G. Committee Chair
Sherali, Hanif D. Committee Co-Chair
Al-Qadi, Imadeddin L. Committee Member
Balci, Ooman Committee Member
Kachroo, Pushkin Committee Member
Sivanandan, R. Committee Member
  • expert systems
  • diversion
  • dynamic traffic routing
  • feedback control
  • incident management
  • incident duration prediction
Date of Defense 1996-04-30
Availability restricted

Real-time control of traffic diversion during non-recurrent congestion continues to be a challenging topic. Especially, with the advent of Intelligent Transportation Systems (ITS), the need for models and algorithms that will control the diversion in real-time, responding to the current traffic conditions has become evident. Several researchers have tried to solve this on-line control problem by adopting different approaches such as, expert systems, feedback control, and mathematical programming.

In order to ensure the effectiveness of real-time traffic diversion, an implementation framework capable of predicting the impact of the incident on the traffic flow, generating feasible alternate routes in real-time, and controlling traffic in order to achieve a pre-set goal based on a system optimal or a user equilibrium concept is required. In this dissertation, a framework that would satisfy these requirements is adopted consisting of a "diversion initiation module", a "diversion strategy planning module", and a "control and routing module" which determines the route guidance commands in real-time. The incident duration data collected by the Northern Virginia incident management agencies is analyzed to determine major factors that affect the incident clearance duration. Next, prediction/decision trees are developed for different types of incidents. Based on the validation of these trees using the data that is not employed for the development of the trees, it is found that they perform well for the majority of the incidents. A simple deterministic queuing approach is used to predict the delays that will be caused by the incident for which the clearance duration is predicted using the prediction/decision trees.

The diversion strategy planning module, Network Generator, is developed as a knowledge based expert system that uses simple expert rules in conjunction with historical and realtime data to determine the incident impact zone, and to eliminate links that are not suitable for diversion. Finally, it generates alternate routes for diversion using this modified network. Network generator is tested using simulation on a small portion of the Fairfax network.

Finally, feedback control models for dynamic traffic routing models, both in distributed and lumped parameter settings, are developed. Methods for developing controllers for these models are also discussed. Two heuristic and analytic feedback controllers for the space discretized lumped parameter models are developed and their effectiveness for realtime traffic control is shown by simulating several scenarios on a simple network. An analytic feedback controller is also designed using a feedback linearization technique for the space discretized model.

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