Investigating the Randomness of Passengers' Seating Behavior in Suburban Trains

Area of Science:

• Pedestrian dynamics
• Crowd simulation
• Transportation modeling

Background:

• Individual-based models are crucial for simulating crowd behavior in public transport.
• Current models often assume uniform passenger distribution, potentially misrepresenting capacity utilization and passenger flow.
• Limited research exists on specific passenger seating behavior within trains.

Purpose of the Study:

• To investigate passenger seating preferences in real-world train environments.
• To challenge the assumption of uniform passenger distribution in simulation models.
• To develop and validate a new model for passenger seating behavior in trains.

Main Methods:

• Data collection on seating behavior in Munich's suburban trains.
• Statistical analysis of observed seating patterns.
• Development of a new simulation model based on empirical data.

Main Results:

• Passenger seating is not uniformly distributed; clear preferences were observed.
• Empirical data contradicts the assumption of random or thermodynamically driven distribution.
• The developed model accurately reflects the observed probability distributions.

Conclusions:

• Passenger behavior in trains exhibits non-uniform patterns, necessitating refined simulation approaches.
• The new model enhances the accuracy of pedestrian dynamics simulations for public transport.
• The model is integrated into the open-source Vadere framework for broader application.