Highlights A multi-objective heterogeneous genetically optimized surrogate modeling approach is proposed. An application of the proposed framework to short-term travel speed forecasting in presented. Support Vector Machines, Radial Base Functions and Neural Networks are jointly optimized. Results are discussed in both a univariate and multivariate framework. Surrogate models may accurately predict speeds with limited sampling attempts.

    Abstract Transportation modelers are frequently faced with several optimization challenges related to model selection and parameter optimization for forecasting. The concept of surrogate modeling is discussed in order to tackle some limitations related to the practice of developing short-term forecasting algorithms. An automated meta-modeling technique is presented that uses heterogeneous information from multiple types of statistical and computationally intelligent models, along with multi-objective evolutionary strategies to optimize the model and parameter selection. A number of different models from the family of Support Vector Machines, Radial Base Functions and Neural Networks are jointly considered and optimized with the aim to improve the short-term predictability of travel speed. Results are presented and discussed in both a univariate and multivariate framework.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Optimization of traffic forecasting: Intelligent surrogate modeling


    Contributors:


    Publication date :

    2015-03-10


    Size :

    10 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Optimization of traffic forecasting: Intelligent surrogate modeling

    Vlahogianni, Eleni I | Online Contents | 2015


    Surrogate Modeling to Enable Higher Fidelity Optimization

    Vegh, Julius M. / Alonso, Juan J. / Ilario da Silva, Carlos R. et al. | AIAA | 2017


    TRAFFIC FORECASTING SYSTEM, TRAFFIC FORECASTING DEVICE, AND TRAFFIC FORECASTING METHOD

    IWAE TOMOHIDE / OBA YOSHIKAZU / NARUSE KOSUKE et al. | European Patent Office | 2021

    Free access