M. Giacobini et al. (Eds.): EvoWorkshops 2007, LNCS 4448, pp. 668–677, 2007. © Springer-Verlag Berlin Heidelberg 2007 Simultaneous Origin-Destination Matrix Estimation in Dynamic Traffic Networks with Evolutionary Computing Theodore Tsekeris 1,2 , Loukas Dimitriou 2 , and Antony Stathopoulos 2 1 Center of Planning and Economic Research, Amerikis 11, 10672 Athens, Greece tsek@kepe.gr 2 Department of Transportation Planning and Engineering, School of Civil Engineering, National Technical University of Athens, Iroon Polytechniou 5, 15773 Athens, Greece lucdimit@central.ntua.gr, astath@transport.ntua.gr Abstract. This paper presents an evolutionary computing approach for the estimation of dynamic Origin-Destination (O-D) trip matrices from automatic traffic counts in urban networks. A multi-objective, simultaneous optimization problem is formulated to obtain a mutually consistent solution between the resulting O-D matrix and the path/link flow loading pattern. A genetically augmented microscopic simulation procedure is used to determine the path flow pattern between each O-D pair by estimating the set of turning proportions at each intersection. The proposed approach circumvents the restrictions associated with employing a user-optimal Dynamic Traffic Assignment (DTA) procedure and provides a stochastic global search of the optimal O-D trip and turning flow distributions. The application of the model into a real arterial street sub-network demonstrates its ability to provide results of satisfactory accuracy within fast computing speeds and, hence, its potential usefulness to support the deployment of dynamic urban traffic management systems. Keywords: Evolutionary Computing, Transportation Networks, Origin- Destination Matrices, Traffic Flows, Microscopic Simulation. 1 Introduction The time-varying (dynamic) Origin-Destination (O-D) trip matrices provide a crucial input for the simulation, management and control of urban road transportation networks. A dynamic O-D matrix specifies the aggregate demand for trip interchange between specific traffic zones of the network over a series of time intervals. The dynamic O-D matrix estimation is typically based on two sources of information, i.e. measured traffic flow time series (counts) at selected network links and a prior O-D matrix to guide the solution procedure. The resulting O-D matrices are mostly used as input to a Dynamic Traffic Assignment (DTA) procedure for mapping the estimated trip demand into a set of path and link traffic flows. In most of the traditional approaches that have been proposed in the literature (see [1]), such a mapping is considered as fixed during the estimation process, based on the assignment of the