Chair: Jean-Michel Poggi (Univ. Paris-Saclay)
The new age of rolling stock and ticketing is to collect more and more data on passengers and railway operations. These new sources of information are opportunities to rethink the way we operate a public transport network. Dense traffic areas are very sensitive to disturbance as trains and passengers are in number. We first give some insights on how passengers impact stopping time and delay. We show how to go from an analytical model to a forecasting model which aims at helping decision makers and passenger information systems. We conclude with an in-depth modeling of passenger movement onboard to improve real-time crowding information per rolling stock zone.
Rémi Coulaud has done his Ph.D both at SNCF-Transilien and Laboratoire de Mathématiquesd'Orsay (LMO) of Paris-Saclay University. His main research interest is to model passenger flow interaction with train flow in dense traffic areas. Rémi obtained his BSc in mathematics and another one in economics at Toulouse School of Economics, France. He then moved to do his master's degree in randomness mathematics at Paris-Saclay University, France. He won a Sophie Germain excellency scholarship for his second year of master. He is now head of SNCF-Transilien DataLab' making the bridge between the transportation world and statistical learning community.