# Train Station Pedestrian Monitoring Pilot Study Using an Artificial Intelligence Approach

## TL;DR

This study uses AI to monitor pedestrian movement in train stations to improve safety and efficiency.

## Contribution

The paper introduces two AI-based methods for tracking pedestrian positions and activities in crowded train stations.

## Key findings

- A method tracks individuals using bounding boxes to derive 3D kinematics like position and velocity.
- Another method infers pose and activity by analyzing body key points from video data.
- These measurements can help design better layouts for public transport spaces.

## Abstract

Pedestrian monitoring in crowded areas like train stations has an important impact in the overall operation and management of those public spaces. An organized distribution of the different elements located inside a station will contribute not only to the safety of all passengers but will also allow for a more efficient process of the regular activities including entering/leaving the station, boarding/alighting from trains, and waiting. This improved distribution only comes by obtaining sufficiently accurate information on passengers’ positions, and their derivatives like speeds, densities, traffic flow. The work described here addresses this need by using an artificial intelligence approach based on computational vision and convolutional neural networks. From the available videos taken regularly at subways stations, two methods are tested. One is based on tracking each person’s bounding box from which filtered 3D kinematics are derived, including position, velocity and density. Another infers the pose and activity that a person has by analyzing its main body key points. Measurements of these quantities would enable a sensible and efficient design of inner spaces in places like railway and subway stations.