Analyzing Wifi signal to understand how passengers move in the station.

More than 500 million passengers use train stations in Paris every year. Around 100 million of them pass through Gare de Lyon. When connected to Wi-Fi at the station, passengers leave behind their digital traces.

Using a sample of 57,000 users collected during one week, this ongoing collaboration between SNCF Gares & Connexions and MIT Senseable City Lab studies the footprint that people leave in space to understand passenger behavior and enhance user experience at train stations.

One Week in Gare de Lyon

Explore passenger trajectories and lengths of stay at Gare de Lyon.

Click on the buttons below to change the type of visualization and on the types of user to filter the data.









Network of trajectories among zones in the station

Length of stay

Heatmap of lengths of stay per zone in the station

Users per hour of the day

Evolution of the length of stay during the day

Mouse over the zones in the map to compare the lengths of stay in each area of the station. Data in minutes.

Show map of:

Ground level

Level -1

Mouse over the circles to see ranges of durations for each zone.

Mouse over the circles to see the zones connected to that area of the station

Trajectories from

Total users:


Zone overview

Total users:

Mean duration: minutes



Carlo Ratti, Director
Fábio Duarte, Project Manager
Bill Cai, Researcher
Lenna Johnsen, Researcher
Qianhui Liang, Researcher
Zhoutong Wang, Researcher
Yuji Yoshimura, Post-doctoral researcher
Sarah Campbell, Visualization, Web
Louis Charron, Researcher, Designer
Irene de la Torre Arenas, Visualization
Snoweria Zhang, Designer


Laurent Papiernik, Chief Data Officer
Etienne Burdet, Smart City Officer - AREP

download press release

The material on this website can be used freely in any publication provided that:
1. It is duly credited as a project by the MIT Senseable City Lab
2. A PDF copy of the publication is sent to

additional material

Quantifying Legibility of Indoor Spaces Using Deep Convolutional Neural Networks: Case Studies in Train Stations

Zhoutong Wang, Qianhui Liang, Fabio Duarte, Fan Zhang, Louis Charron, Lenna Johnsen, Bill Cai, Carlo Ratti
Building and Environment, 2019.

The Senseable Guide to Paris 2 — Gare de Lyon

An exploration of how digital technologies can enhance passenger experience.

Trains of Data

This Senseable project, in collaboration with SNCF, shows changes in the perceptual shape of France based on how fast passengers can reach different parts of the country.

MIT Senseable City Lab SNCF

In the collaboration with