Behavioural data mining of transit smart card data: A data fusion approach

The aim of this study is to develop a data fusion methodology for estimating behavioural attributes of trips using smart card data to observe continuous long-term changes in the attributes of trips. The method is intended to enhance understanding of travellers’ behaviour during monitoring the smart card data. In order to supplement absent behavioural attributes in the smart card data, this study developed a data fusion methodology of smart card data with the person trip survey data with the naïve Bayes probabilistic model. A model for estimating the trip purpose is derived from the person trip survey data. By using the model, trip purposes are estimated as supplementary behavioural attributes of the trips observed in the smart card data. The validation analysis showed that the proposed method successfully estimated the trip purposes in 86.2% of the validation data. The empirical data mining analysis showed that the proposed methodology can be applied to find and interpret the behavioural features observed in the smart card data which had been difficult to obtain from each independent dataset

How has the Covid-19 pandemic affected wheelchair users? Time-series analysis of the number of railway passengers in Tokyo

Abstract The Coronavirus disease 2019 (COVID-19) has posed ‘new barriers’ to people with disabilities (PwDs) who have already experienced many barriers to using public transportation. However, there is limited quantitative knowledge of how PwDs have been affected by the COVID-19 pandemic. This study investigated the impact of the COVID-19 pandemic on the use of public transportation by PwDs over time. Specifically, we analysed time-series data on wheelchair rail passenger numbers and all rail passenger numbers in Tokyo from April 2012 to December 2021. The impact of COVID-19 was more accurately assessed by excluding seasonal variations in the time-series, and two key findings were obtained. First, the change point for the decline in the number of passengers owing to the COVID-19 pandemic was March 2020, one month earlier than the declaration of the state of emergency. Second, using the time-series model, the actual and estimated values were compared, and we found that wheelchair rail passenger numbers reduced by approximately 20 percentage points on average compared with all rail passengers. Wheelchair rail passengers were more severely affected by the COVID-19 pandemic than all rail passengers. Based on previous studies, these findings demonstrated that opportunities to participate in society were disproportionately reduced for PwDs during the COVID-19 pandemic. This study’s quantitative data and the resulting conclusions on wheelchair users are useful for inclusive planning for mitigating the pandemic’s impact by national administrations and public transport authorities