Recognizing travel patterns of the urban traffic is crucial for improving the performance of urban traffic control, thus mitigating the negative impacts of traffic congestions on our daily life. In recent years, tons of license plate sensors are deployed on urban traffic networks to monitor motorized traffic by recording the license plate number of passing vehicles at intersections. The large-scale license plate sensing data (LPS) do contain valuable vehicle origin-destination (O-D) information, which can be utilized for unveiling travel patterns of urban traffic. This study developed the structure of travel pattern recognition, which consists of three components: data pre-processing, feature extraction, and pattern recognition modeling. The architecture was tested and validated using the LPS data sets from Nanjing, China. The results showed six clusters has low sum of square errors (SSE) and high Calinski-Harabasz (CH) index, which can explain the changes of travel behaviors the urban traffic especially in the morning O-D trips.


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    Title :

    Urban Travel Pattern Recognition Based on Clustering Techniques Using License Plate Sensing Data


    Contributors:
    Ng, Kean Jiun (author) / Li, Shuyang (author) / Pu, Ziyuan (author)

    Conference:

    International Conference on Transportation and Development 2022 ; 2022 ; Seattle, Washington



    Publication date :

    2022-08-31




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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