Big Data-Driven Transport Simulation

Big Data delivers on-demand traffic information

For the first time in transport planning, we have near perfect and on-demand information to help us predict transportation needs with an unprecedented confidence. Cell phones, smart cards, and soon an internet of moving things will monitor your every move. Each move leaves a trace and makes your future movements more predictable. In this public lecture, Swiss educated computer scientists and engineers will show why this type of data tracking is not as “creepy” as it sounds as society moves towards a state where only machines will see your data. They will also illustrate just how much information is contained, not just in the data points, but also in the spaces between them.
 

Speakers

Alexey Pozdnukhov, Smart Cities Research Center, UC Berkeley
Alexey works on scalable data analytics methods for semantically rich modelling of urban dynamics. His research links advanced machine learning, complex networks and spatial interaction models into a general framework to quantify the emergence and evolution of spatio-temporal patterns in everyday dynamics and socio-economic structure of cities.

Pieter Fourie, Future Cities Laboratory, ETH Zurich
Pieter is a simulation modeler at ETH Zurich’s Singapore-ETH Centre. Specializing in transportation, his work focuses on using transit smart card data to produce precise simulations of current and future transport system networks. He is responsible for population synthesis and transport demand assignment for the agent-based transport demand model implemented at the Future Cities Laboratory.

The evening will close with a networking reception.