Pedestrian Patterns at Railway Platforms during Boarding: Evidence from a Case Study in Switzerland

Abstract

The boarding/alighting process at railway platforms is an important determinant of the railway system performance and depends on the characteristics of passengers, the layout of the platform, and the rolling stock. This research aims to increase the understanding of the process, providing a methodological approach to model the passengers’ behaviour when boarding at railway platforms. Adequate criteria were selected to define the so called “boarding cluster” and an easy mechanism was developed to select the boarding clusters. Passenger flow data collected at Bern railway station in Switzerland was used to test the proposed approach. The results show that (a) the clusters near the doors grow in the longitudinal direction with a rate of 6:1 between the length and width of clusters, and that (b) the growth curves rise quickly when clusters are still small, i.e., at the beginning of the boarding/alighting activity. Further research is needed to extend the validation of the model, considering other variables, such as critical pedestrian densities which occur at specific hot spots near obstacles at platforms.

Analysing passengers’ behaviours when boarding trains to improve rail infrastructure and technology

“…Manufacturing research can provide modern techniques to facilitate data collection for mapping human movements [49]. Automated passenger tracking and counting systems could be mounted on the station ceiling to provide this data [50]. Human-centred manufacturing techniques had been used to improve work environments using diverse types of motion capture [51].…”

Exploring the Effect of Boarding and Alighting Ratio on Passengers’ Behaviour at Metro Stations by Laboratory Experiments

“…However, according to Preston et al [13], in short commuter journeys crowding is based on stress and physical discomfort, while in long distance services the space to relax and use the journey productively is much more important for passengers. Other authors [14] have studied the concentration of passengers boarding at railway platforms, in which bigger clusters are not necessarily related to a higher density due to crowding at the station (e.g., passengers move along the platform to avoid contact with other passengers). Passengers tend to wait for the train near the entrance of the platform, especially closer to staircases and ramps [15].…”

Identifying the Indoor Space Characteristics of an Urban Railway Station Based on Pedestrian Trajectory Data

“…These studies proposed the algorithm for detecting and tracking the vehicles and pedestrians using advanced methodologies such as LiDAR and VISION sensors. In addition, several studies were performed to assess the congestion of the indoor spaces using pedestrian characteristics data collected from various sensors, video, and Bluetooth [7][8][9][10]. In this study, the pedestrian trajectory collection system based on the LiDAR sensor was used to collect pedestrian trajectory data.…”