Missing data imputation for transfer passenger flow identified from in-station WiFi systems
Document Type
Journal Article
Publication Date
2023
Subject Area
mode - rail, infrastructure - station, infrastructure - interchange/transfer, technology - passenger information
Keywords
WiFi data, missing data, transfer flow, multitask Gaussian process
Abstract
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.
Rights
Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.
Recommended Citation
Jiang, W., Zheng, N., & Kim, I. (2023). Missing data imputation for transfer passenger flow identified from in-station WiFi systems. Transportmetrica B: Transport Dynamics, 11(1), 325-342.
