Accurate short-term prediction of traffic conditions on freeways has recently become increasingly important because of its vital role in the basic traffic management functions and trip decision making processes. The objective of this research is to utilize traffic and weather data from multiple data sources to develop an integrated model to predict traffic conditions under different rainfall conditions. A set of prediction models are compared and their performances using data from case studies are investigated and reported. The model performance was valuated using prediction errors, which are measured by the relative length of the distance between the predicted state and the observed state.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    An Improved Model for Short-Term Traffic Forecasting Considering Weather Impacts


    Beteiligte:
    Chen, Xinchao (Autor:in) / Qin, Si (Autor:in) / Zhang, Jian (Autor:in) / Tan, Huachun (Autor:in) / Xu, Yunxia (Autor:in) / Dai, Guanchen (Autor:in) / Chen, Xiaoxuan (Autor:in)

    Kongress:

    17th COTA International Conference of Transportation Professionals ; 2017 ; Shanghai, China


    Erschienen in:

    CICTP 2017 ; 784-792


    Erscheinungsdatum :

    2018-01-18




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Does Information on Weather Affect the Performance of Short-Term Traffic Forecasting Models?

    Tsirigotis, Lykourgos / Vlahogianni, Eleni I. / Karlaftis, Matthew G. | Springer Verlag | 2011


    Short-Term Forecasting of Traffic Volume

    Lin, Lei / Wang, Qian / Sadek, Adel W. | Transportation Research Record | 2013


    Short-term Aircraft Trajectory Prediction Considering Weather Effect

    Feng, Shuai / Wang, Gang / Zhao, Peng et al. | IEEE | 2023


    Short-Term Traffic States Forecasting Considering Spatial–Temporal Impact on an Urban Expressway

    Chen, Peng / Ding, Chuan / Lu, Guangquan et al. | Transportation Research Record | 2016