Transport modelling in the age of big data
International Journal of Urban Sciences, 21 (S1): 19-42, Abington: Routledge, 2017.
Pieter J. FOURIE
Main content
Senior Researcher and Project Coordinator of Engaging Mobility
Pieter Fourie is a simulation modeller specializing in transportation. Previously he developed integrated transport/land-use models for decision support to South African metropolitan planning authorities at the South African Council of Scientific and industrial Research (CSIR). He is responsible for population synthesis and transport demand assignment for the agent-based transport demand model implemented at the Future Cities Laboratory.
Research
Pieter’s research deals with the generation of more realistic synthetic populations of households and individuals from limited and incomplete information, developing an integrated approach to modelling the coordinated activity scheduling and ride-sharing behaviour within households, and speeding up agent-based transport simulations through meta-modelling techniques.
Education
BEng (Metallurgical Engineering) University of Pretoria
BEng (Hons) (Industrial Engineering) University of Pretoria
MEng (Industrial Engineering) (with distinction) University of Pretoria
Publications
Using smartcard data for agent-based transport simulation
In Public Transport Planning with Smart Card Data, edited by Kurauchi, Fumitaka, and Jan-Dirk Schmöcker, 133-160, Boca Raton: CRC Press, 2016.
Interactive analysis and decision support with MATSim
In The Multi-Agent Transport Simulation MATSim, edited by Horni, Andreas, Kai Nagel and Kay W. Axhausen, 253-258, London: Ubiquity Press, 2016.
Multi-modeling in MATSim: PSim
In The Multi-Agent Transport Simulation MATSim, edited by Horni, Andreas, Kai Nagel and Kay W. Axhausen, 263-266, London: Ubiquity Press, 2016.
MATSim Singapore: Synthetic population and work locations
Singapore: URA Planning Analytics Symposium, 2016.
Synthesizing high-dimensional, agent-based transport demand data from two-dimensional aggregates with iterative multiple histogram matching
Arbeitsberichte Verkehrs- und Raumplanung, 1165, Zürich: ETH Zürich, 2016.
Synthesizing high-dimensional, agent-based transport demand data from two-dimensional aggregates with iterative multiple histogram matching
Singapore: 2016 Autonomous Agents and Multiagent Systems International Conference (AAMAS 2016), 2016.