Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in architecture; 2) hand engineered in features; and 3) separate in learning. In this paper we propose a deep architecture that consists of two parts, i.e., a deep belief network (DBN) at the bottom and a multitask regression layer at the top. A DBN is employed here for unsupervised feature learning. It can learn effective features for traffic flow prediction in an unsupervised fashion, which has been examined and found to be effective for many areas such as image and audio classification. To the best of our knowledge, this is the first paper that applies the deep learning approach to transportation research. To incorporate multitask learning (MTL) in our deep architecture, a multitask regression layer is used above the DBN for supervised prediction. We further investigate homogeneous MTL and heterogeneous MTL for traffic flow prediction. To take full advantage of weight sharing in our deep architecture, we propose a grouping method based on the weights in the top layer to make MTL more effective. Experiments on transportation data sets show good performance of our deep architecture. Abundant experiments show that our approach achieved close to 5% improvements over the state of the art. It is also presented that MTL can improve the generalization performance of shared tasks. These positive results demonstrate that deep learning and MTL are promising in transportation research.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning


    Beteiligte:
    Huang, Wenhao (Autor:in) / Song, Guojie (Autor:in) / Hong, Haikun (Autor:in) / Xie, Kunqing (Autor:in)


    Erscheinungsdatum :

    01.10.2014


    Format / Umfang :

    936387 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    MSAE: A Multitask Learning Approach for Traffic Flow Prediction Using Deep Neural Network

    Yang, Di / Yang, Hua-Min / Wang, Peng et al. | Springer Verlag | 2019


    traffic flow prediction model based on deep belief network and genetic algorithm

    Zhang, Yaying / Huang, Guan | Wiley | 2018

    Freier Zugriff

    A Spatial–Temporal Multitask Collaborative Learning Model for Multistep Traffic Flow Prediction

    Tang, Kun / Chen, Shuyan / Khattak, Aemal J. | Transportation Research Record | 2018


    Deep Multitask Learning for Railway Track Inspection

    Gibert, Xavier | Online Contents | 2017


    traffic flow prediction model based on deep belief network and genetic algorithm

    Zhang, Yaying / Huang, Guan | IET | 2018

    Freier Zugriff