This paper presents a new perspective for in-station transfer flow estimation, utilising data collected by WiFi sensor system, which is critical for path choice modelling and pedestrian management. The full in-station transfer flow can be estimated by scaling up a ‘seed matrix’, which is constructed based on the identification of inter-platform transfer activities. Due to sensor failures, the main problem comes from handling the missing elements in the constructed ‘seed matrix’. We address this problem with a novel kernel-based framework, named self-measuring multi-task Gaussian process (SM-MTGP). The heterogeneous correlations in temporal features are captured by the designed task-based and input-based kernels separately. Moreover, a self-measuring kernel is designed for learning the correlations carried by the observations. The performance of the proposed method is validated with data from a busy railway station. The results show that the proposed algorithm achieves the best imputation accuracy in both accuracy and robustness, especially at high missing rates.


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    Title :

    Missing data imputation for transfer passenger flow identified from in-station WiFi systems


    Contributors:
    Jiang, Wenhua (author) / Zheng, Nan (author) / Kim, Inhi (author)

    Published in:

    Publication date :

    2023-12-31


    Size :

    18 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





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