Using neural nets to predict transportation mode choice: Amsterdam network change analysis
In the Amsterdam metropolitan area, the opening of a new metro line along the north–south axis of the city has introduced a significant change in the region’s public transportation network. Mode choice analysis can help in assessment of changes in traveler behavior that occurred after the opening of the new metro line. As it is known that artificial neural nets excel at complex classification problems, this paper aims to investigate an approach where the traveler’s transportation mode is predicted through a neural net, trained on choice sets and user specific attributes inferred from the data. The method shows promising results. It is shown that such models perform better when it is asked to predict the choice of mode for trips which take place on the same underlying transportation network as the data with which the model is trained. This difference in performance is observed to be especially high for trips from and to certain areas that were impacted by the introduction of the north–south line, indicating possible changes in behavioural patterns, entailing interesting possible directions for further research.
|Artificial neural nets, Machine learning, Public transportation network change, Transportation mode choice, Travel behaviour|
|Journal of Ambient Intelligence and Humanized Computing|
|Organisation||Centrum Wiskunde & Informatica, Amsterdam, The Netherlands|
Buijs, R.J.W, Koch, T, & Dugundji, E.R. (2021). Using neural nets to predict transportation mode choice: Amsterdam network change analysis. Journal of Ambient Intelligence and Humanized Computing. doi:10.1007/s12652-020-02855-6