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Estimating Time-Dependent Origin-Destination Demand in a Large-Scale Congested Network Using Multi-Source Traffic Data

posted on 13.12.2018, 03:23 by MOHAMMAD SAJJAD SHAFIEI
Traffic simulation models are capable of replicating traffic network conditions and providing accurate forecasts. These models involve a number of parameters that need to be calibrated based on the available archived traffic data. Time-Dependent Origin-Destination (TDOD) demand is a crucial input for traffic simulations. The reliability of the traffic simulations is highly dependent on the accuracy of the TDOD demand. Since the demand data cannot be observed directly, a common approach to obtain TDOD demand is to use a priori demand matrix and traffic count data in some parts of the network. The broad aim of this research is to develop a TDOD estimation model using multi-source traffic data applied to a large-scale congested urban network (Melbourne).


Campus location


Principal supervisor

Hai Vu

Additional supervisor 1

Meead Saberi

Year of Award


Department, School or Centre

Civil Engineering

Additional Institution or Organisation

Department of Civil Engineering, Institute of Transport Studies


Doctor of Philosophy

Degree Type



Faculty of Engineering