To enhance user experience in train stations we look at how passengers move in space and perceive that space.

team

MIT SENSEABLE CITY LAB

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

SNCF

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 senseable-press@mit.edu

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

AIT