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

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.

Automated summary

This paper presents a new perspective for in-station transfer flow estimation using data collected by a WiFi sensor system. The proposed method is critical for path choice modeling and pedestrian management. It utilizes a 'seed matrix' constructed based on the identification of inter-platform transfer activities to estimate the full in-station transfer flow. The main challenge is handling the missing elements in the 'seed matrix' caused by sensor failures. The proposed self-measuring multi-task Gaussian process (SM-MTGP) framework addresses this problem by capturing the heterogeneous correlations in temporal features.