Predicting traffic incident duration is important for effective and real-time traffic incident management (TIM), which helps to minimize traffic congestion, environmental pollution, and secondary incident related to this incident. Traffic incident duration prediction methods often use more input variables to obtain better prediction results. However, the problems that available variables are limited at the beginning of an incident and how to select significant variables are ignored to some extent. In this paper, a novel prediction method named NCA-BOA-RF is proposed using the Neighborhood Components Analysis (NCA) and the Bayesian Optimization Algorithm (BOA)-optimized Random Forest (RF) model. Firstly, the NCA is applied to select feature variables for traffic incident duration. Then, RF model is trained based on the training set constructed using feature variables, and the BOA is employed to optimize the RF parameters. Finally, confusion matrix is introduced to measure the optimized RF model performance and compare with other methods. In addition, the performance is also tested in the absence of some feature variables. The results demonstrate that the proposed method not only has high accuracy, but also exhibits excellent reliability and robustness.


    Access

    Download


    Export, share and cite



    Title :

    A Hybrid Method for Traffic Incident Duration Prediction Using BOA-Optimized Random Forest Combined with Neighborhood Components Analysis


    Contributors:
    Qiang Shang (author) / Derong Tan (author) / Song Gao (author) / Linlin Feng (author)


    Publication date :

    2019




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Analysis of Regression Method on Traffic Incident Duration Prediction

    Wang, Xuanqiang / Chen, Shuyan / Zheng, Wenchang | ASCE | 2013


    Big data-driven highway traffic incident duration prediction method

    CHEN JIAONA / LI DAOFENG / TAO WEIJUN et al. | European Patent Office | 2022

    Free access

    Combined PCA and NB to Predict Traffic Incident Duration

    Lao, Yechun / Chen, Shuyan / Song, Ningning | Springer Verlag | 2017


    Prediction of Traffic Incident Duration Using Clustering-Based Ensemble Learning Method

    Zhao, Hui / Gunardi, Willy / Liu, Yang et al. | ASCE | 2022