Abstract With the rapid construction of urban rail transit systems in China, the operating mileage and the passenger volume of urban rail transit system have been growth rapidly, as well as huge energy consumption has been generated. How to accurate evaluate and predict the energy consumption has attracted more and more attention of researchers, operating enterprise, and policy-makers. Therefore, this paper is devoted to establishing a traction energy consumption model to accurate evaluate and predict the traction energy consumption which is the main energy consumption in urban rail transit systems. On the basis of dynamics analysis of urban rail transit, four categories variables are adopted into the model, including train running state, line attribute, operation indexes and environment variable factors. Then, two multiple regression models of the traction energy consumption factor for underground line and overground line are proposed. Based on the urban rail transit statistics database of Beijing, the models are calibrated by the least square method. The results show a good-fit between the proposed model estimates and the field measurements, and the proposed model has satisfied prediction accuracy. Furthermore, to further improve the prediction accuracy, two extend models involving the season factor for underground line and overground line are proposed in this paper. The calibrated results show that the extended model is more effective to evaluate the traction energy consumption factor than the previous model, and the MAPEs of the extended model for underground line and overground is 4.7 and 5.1%.


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    Titel :

    Modeling the Traction Energy Consumption for Urban Rail Line Considering Operation Characteristics


    Beteiligte:
    Sun, Xun (Autor:in) / Ma, Zhankui (Autor:in) / Yao, Enjian (Autor:in) / Wu, Xu (Autor:in)


    Erscheinungsdatum :

    2017-07-12


    Format / Umfang :

    17 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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