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In this work, deterministic and stochastic optimization methods are tested for solving the dynamic demand estimation problem. All the adopted methods demonstrate difficulty in reproducing the correct traffic regime, especially if the seed matrix is not sufficiently close to the real one. Therefore, a new and intuitive procedure to specify an opportune starting seed matrix is proposed: it is a two-step procedure based on the concept of dividing the problem into small problems, with a focus on specific origin–destination (O-D) pairs in different steps. Specifically, the first step focuses on the optimization of a subset of O-D variables (the ones that generate the higher flows or the ones that generate bottlenecks on the network). In the second step the optimization works on all the O-D pairs, with the matrix derived from the first step as starting matrix. In this way it is possible to use a performance optimization method for every step; this technique improves the performance of the method and the quality of the result with respect to the classical one-step approach. The procedure was tested on the real-world network of Antwerp, Belgium, and demonstrated its efficacy in combination with different optimization methods.

Performances of the traffic models in reproducing congestion phenomena are strongly related to quality of the information about the demand. The problem of calibrating the demand for traffic assignment models is known as Demand Estimation Problem. This paper focuses on improving the reliability of the demand matrix, dealing with the problem of having a robust solution with respect to the input parameters and the used real measurements. Authors introduce a Two Steps procedure, which separates the problem in two sub-optimization problems. Through this procedure, authors correct sequentially generations and distributions in the demand matrix, reducing solution space size and the variance in the solutions of the calibration process. The proposed approach is then applied to a real network, the ring of Antwerp, and results are presented.

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