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.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Two-Step Approach for Correction of Seed Matrix in Dynamic Demand Estimation


    Additional title:

    Transportation Research Record


    Contributors:


    Publication date :

    2014-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Effectiveness of the two-step dynamic demand estimation model on large networks

    Cantelmo, Guido / Viti, Francesco / Derrmann, Thierry | IEEE | 2017


    Distributed Approach for Estimation of Dynamic Origin–Destination Demand

    Etemadnia, Hamideh / Abdelghany, Khaled | Transportation Research Record | 2009



    A Two-Steps Dynamic Demand Estimation Approach Sequentially Adjusting Generations and Distributions

    Cantelmo, Guido / Viti, Francesco / Cipriani, Ernesto et al. | IEEE | 2015


    Dynamic Train Demand Estimation and Passenger Assignment

    Ou, Yuming / Mihaita, Adriana-Simona / Chen, Fang | IEEE | 2020