Scientists just revealed a strange quirk in how we exit train stations

An analysis of data collected by a pedestrian tracking system at the Eindhoven Centraal Railway Station in the Netherlands found that, after exiting a train, individuals tend to follow the same walking path as the person directly in front of them. This happens even when they do not know that person and even when such a choice leads to a longer travel time. The research was published in the *Proceedings of the National Academy of Sciences*.

When walking in crowded spaces such as busy streets, train or bus stations, airports, or mass gatherings, people generally try to reach their destination while avoiding obstacles, delays, collisions, and discomfort. Their route is shaped by physical features such as walls, doors, stairs, kiosks, corridors, signs, and bottlenecks. They also respond to crowd density, often avoiding areas that look too congested or slow.

Walking paths are likely influenced by perceived travel time, not only by actual distance, because a shorter route may feel worse if it is crowded. Also, being in a crowd forces people to continuously adjust their speed and direction in response to others moving around them. In such situations, they often follow visible flows of pedestrians because other people’s movement gives information about where a usable path may be. Social groups, such as friends or family members, also shape walking paths because members tend to stay together and follow the same route.

Study author Ziqi Wang and his colleagues used a large-scale, high-resolution dataset of pedestrian paths collected at tracks 3 and 4 of Eindhoven Centraal Railway Station using an advanced overhead pedestrian tracking system based on 3D stereoscopic imaging.

These sensors covered about 1400 m² of the station, capturing data at 10 frames per second using overhead depth sensing without recording identifiable images of pedestrians. The system also provided very high spatial resolution, being able to detect changes of around 1 millimeter. In total, between March 2021 and March 2024, the system captured over 30 million pedestrian movement trajectories. This included people disembarking from the trains and the people already present on the platform.

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In this analysis, the study authors focused on a subset of pedestrian trajectories where individuals, after getting off a train, had to choose between taking a direct, shorter path to the exit and a longer path that involved circumventing a kiosk in the middle of the platform. The authors analyzed the paths of passengers who exited the train from three specific door zones, including approximately 100,000 passengers.

To ensure they were studying the interactions between strangers rather than people traveling together, the researchers developed a mathematical algorithm to detect social groups. This system analyzed how close people were to each other, how much they matched each other’s speed, and if they moved in the same direction. Once these groups were identified and filtered out, the researchers could focus solely on independent pedestrians.

For each passenger included in the analysis, the study authors recorded their choice of route after exiting the train and the relative order in which they exited. This allowed them to study how individuals and crowds decide what path to take in the presence of congestion, differences in how the space is organized, and how local social dynamics—especially among strangers—affect those choices.

The results showed that, after exiting the train, passengers demonstrated a strong tendency to follow the same path as the person directly in front of them. This “stranger-following effect” happened even in the absence of any social ties, and even when following the stranger led to a longer travel time.

The study authors note that this tendency creates “avalanches” of choices, where sequences of people make identical decisions about their walking paths in succession, leading to strong patterns in collective movement.

To confirm these findings, the researchers built a theoretical routing model to simulate pedestrian behavior. They tested various factors, such as the natural randomness of walking speeds and the tendency of people to follow the majority (herding). However, they found that only by including the “stranger-following effect” could the model accurately reproduce the real-world patterns observed at the station. This indicates that local imitation behavior is the dominant driver of collective route choices in this scenario.

“These findings highlight how brief, low-level interactions between strangers can scale up to influence large-scale pedestrian movement, with strong implications for crowd management, urban design, and the broader understanding of social behavior in public spaces,” the study authors concluded.

The study contributes to the scientific understanding of how people choose their paths in crowded areas. However, it should be noted that the study was based on data concerning the movements of passengers exiting trains at three relatively fixed positions and moving towards the station exit. This situation greatly simplified and constrained the routing choices people could make. Results in environments with wider routing and end-goal options might differ.

The paper, “Avalanches of choice: how stranger-to-stranger interactions shape crowd dynamics,” was authored by Ziqi Wang, Alessandro Gabbana, and Federico Toschi.