Short time traffic forecasting is the prediction of traffic parameters like traffic flow into the future to avoid congestions and accidents. A variety of short-time traffic flow forecasting methods have been developed in the last two decades but most of the literatures focus on predicting traffic flow on a single road, not on junctions. However, Junctions are responsible for 80–90% of congestion and 40–60% of accidents on traffic systems. In this paper, Recurrent Multilayer Perceptron (RMLP) trained with Levenberg-Marquardt algorithm is used to forecast traffic flow at a junction found in Addis Ababa, Ethiopia; locally known as Teklehaimanot junction fifteen minute in to the future. The result is then compared with Multilayer Perceptron (MLP) trained with gradient descent back-propagation algorithm. Forecasting results show that RMLP trained with Levenberg-Marquardt algorithm on the junction has approximately three times better training Mean Square Error (MSE). In addition to this, the RMLP has a correlation value of 93.48% for a completely new set of validation data but the MLP's correlation value for the same new set of validation data is 82.9% when used to predict traffic flow on the junction.


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    Titel :

    Short time traffic flow identification and prediction at road junctions using Recurrent Multilayer Perceptron


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    01.09.2017


    Format / Umfang :

    422619 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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