Investigating passenger behaviour on the metro platform with Wi-Fi location tracking data: a case study of Singapore

Abstract

Utilising the existing infrastructure in railway transit to tackle overcrowding requires more understanding of how people use spaces at stations. This study investigated passenger behaviour while waiting for a train on the platform using the data of the Wi-Fi location tracking systems. The trajectories of 129,354 devices were observed in two weeks at two MRT Circle Line stations in Singapore, which have the escalator/stair landings in different positions. A data cleaning process was proposed to overcome the drawbacks of Wi-Fi-based position data. A decomposition method was further developed to separate the walking and staying phases based on data processing. The boarding passengers’ on-platform behaviour was analysed from four aspects: the number of staying phases, the location distributions of different kinds of stays, the location distribution of in-between stays by hour and duration, and the distance and walking speed of the first walking phase. Our results suggested that many passengers (44% and 37% of passengers at the two case study stations) had multiple staying phases, meaning that they did not go directly to their final boarding points after coming to the platform but rather made stops or walkarounds before coming to boarding points. The distributions of locations of the last and in-between stays were significantly different and may influenced by the width, length and layout (such as landing locations) of stations. In addition, the walking speeds of passengers observed on the metro platform were slower than those observed on the streets. These findings indicated that some commonly used assumptions in most simulation models are not true according to the empirical observation. The obtained knowledge would deepen the understanding of the passengers’ on-platform behaviour and thus provide implications for designing railway stations and planning station operations.