Pedestrian dynamics, a field that studies pedestrian movement and behavior, is gaining much importance as passenger numbers increase in many areas. Previous studies on passenger waiting behavior at railway platforms have revealed that the distribution of passengers in a space is not uniform but influenced by entryways and train stops. While these studies have been conducted using field observations, a computational approach to the question could help to better plan public spaces and minimize wait times. In a recent study, Mira Küpper and Armin Seyfried investigated, via experiments, the impact of obstacles, passenger count, and waiting time on the distribution of waiting passengers, using the findings to develop a computational model for the problem.
The study used a mock-up train station platform with 40 and 100 participants at different times, each with varying obstacles. Participants entered the platform through stairs and waited for a train to arrive. The waiting time varied between 2 and 4 minutes for 40 participants and 2 minutes for 100 participants. After the waiting time, movable stairs were positioned as train doors, and participants left the platform. Participants' mood was recorded using a mood button terminal and questionnaires. The experiments showed uniform interpersonal distances, irrespective of global density, passenger count, and waiting area complexity. Even with obstacles, waiting pedestrians maintained equal distances, demonstrating the preference for equal space coverage in public environments. The mood button terminals showed that increasing waiting time and participant count negatively impacted participants' perception of the mock-up train platform experiment.
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