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.


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    Title :

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


    Contributors:
    Chen, Xinchao (author) / Qin, Si (author) / Zhang, Jian (author) / Tan, Huachun (author) / Xu, Yunxia (author) / Dai, Guanchen (author) / Chen, Xiaoxuan (author)

    Conference:

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


    Published in:

    CICTP 2017 ; 784-792


    Publication date :

    2018-01-18




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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