This report reviews recent advances in harnessing big data to understand travel behavior and inform travel demand models. Analyzes the existing data-mining methods that enable these collected mobility traces to inform transport demand models. The authors note that new challenges call for a multidisciplinary collaboration between transport modelers and data scientists. 1
Referencing Page:
- 1Cuauhtémoc, A., Fourie, P., & Erath, A. (2016). Transport Modelling in the Age of Big Data. Zurich: Future Cities Laboratory. Retrieved June 4, 2020, from https://ethz.ch/content/dam/ethz/special-interest/baug/ivt/ivt-dam/vpl/reports/1101-1200/ab1164.pdf
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