We develop a deep learning model to predict traffic flows. The main contribution is development of an architecture that combines a linear model that is fitted using \ell_1 regularization and a sequence of \tanh layers. The challenge of predicting traffic flows are the sharp nonlinearities due to transitions between free flow, breakdown, recovery and congestion. We show that deep learning architectures can capture these nonlinear spatio-temporal effects. The first layer identifies spatio-temporal relations among predictors and other layers model nonlinear relations. We illustrate our methodology on road sensor data from Interstate I-55 and predict traffic flows during two special events; a Chicago Bears football game and an extreme snowstorm event. Both cases have sharp traffic flow regime changes, occurring very suddenly, and we show how deep learning provides precise short term traffic flow predictions.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Learning for Short-Term Traffic Flow Prediction


    Contributors:


    Publication date :

    2016




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    BKL:    55.84 Straßenverkehr




    Short Term Traffic Flow Prediction Based on Deep Learning

    Li, JiaWen / Wang, JingSheng | ASCE | 2019


    Short-Term Traffic Flow Prediction Based on Deep Learning Models

    Yuan, Bo / Li, Wanda / Li, Lin et al. | IEEE | 2024


    Short-term traffic flow prediction method based on deep learning

    LI ZHUANGZHUANG / GUI ZHIMING / GUO LIMIN et al. | European Patent Office | 2020

    Free access

    Short-term traffic flow prediction method based on deep learning

    XIAO HONGBO / XIAO JIANHUA / DING LIMING et al. | European Patent Office | 2025

    Free access