Abstract
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.
Original language | English |
---|---|
Pages (from-to) | 121-135 |
Number of pages | 15 |
Journal | Journal of Ambient Intelligence and Humanized Computing |
Volume | 12 |
Issue number | 1 |
DOIs | |
Publication status | Published - Jan 2021 |
Bibliographical note
Funding Information:This research has been conducted in the framework of the Impact Study North/Southline, funded in part by the Municipality of Amsterdam and the regional transportation authority of Amsterdam. We would gratefully like to acknowledge the Vrije Universiteit Amsterdam legal affairs office, IT department, and university library research data management team as well as the IT and Facilities department of CWI for support in the design and implementation of the computing infrastructure.
Publisher Copyright:
© 2021, The Author(s).
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
Funding
This research has been conducted in the framework of the Impact Study North/Southline, funded in part by the Municipality of Amsterdam and the regional transportation authority of Amsterdam. We would gratefully like to acknowledge the Vrije Universiteit Amsterdam legal affairs office, IT department, and university library research data management team as well as the IT and Facilities department of CWI for support in the design and implementation of the computing infrastructure.
Keywords
- Artificial neural nets
- Machine learning
- Public transportation network change
- Transportation mode choice
- Travel behaviour