Traffic network design is the core field in transportation planning, and it is also the key point and difficulty. The traditional traffic network design model assume that demand and supply are deterministic which can result in unreasonable planning scheme. This paper assumes that the traffic supply and trip production-attraction are random variables which aims to build new transport network design method with uncertainty theory. A bi-level model is established with combined trip distribution/traffic assignment model to improve the accuracy of predictions. The essence of combinatorial optimization for network design problems has been solved with Monte Carlo simulation, genetic algorithm, and traffic assignment algorithm. The results of the Nguyen Dupuis network show that the parameter of the combined trip distribution/traffic assignment model and degrees of uncertainty in trip production-attraction have an important impact on the decision-making of network design.


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    Titel :

    Traffic network design model with trip production-attraction and supply uncertainty


    Beteiligte:
    Wu, Jinsong (Herausgeber:in) / Ma'aram, Azanizawati (Herausgeber:in) / Yu, Xinxin (Autor:in) / Bian, Changzhi (Autor:in) / Han, Xianke (Autor:in) / Tang, Guoyi (Autor:in) / Wang, Yu (Autor:in) / Liu, Heling (Autor:in) / Liu, Ying (Autor:in) / Liu, Yi (Autor:in)

    Kongress:

    Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024) ; 2024 ; Guilin, China


    Erschienen in:

    Proc. SPIE ; 13251


    Erscheinungsdatum :

    28.08.2024





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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