Samenvatting
The node-place model is an analytical framework that was devised to identify spatial development opportunities for railway stations and their surroundings at the regional scale. Today, the model is predominantly invoked and applied in the context of ‘transit-oriented development’ planning debates. As a corollary, these model applications share the pursuit of supporting a transition towards increased rail ridership (and walking and cycling), and therefore assumingly a transition to more sustainable travel behavior. Surprisingly, analyses of the importance of node and place interventions in explaining rail ridership remain thin on the ground. Against this backdrop, this paper aims to integrate the node-place model approach with current insights that derive from the trip end modeling literature. To this end, we apply a series of regression analyses in order to appraise the most important explanatory factors that impact rail ridership in Flanders, Belgium, today. This appraisal is based on both geographical and temporal data segmentations, in order to test for different types of railway stations and for different periods of the day. Additionally, we explore spatial nonstationarity by calibrating geographically weighted regression models, and this for different time windows. The models developed should allow policy and planning professionals to investigate the possible demand impacts of changes to existing stations and the walkable area surrounding them.
| Originele taal-2 | English |
|---|---|
| Artikelnummer | 102796 |
| Tijdschrift | Journal of Transport Geography |
| Volume | 87 |
| Status | Published - 2020 |
Bibliografische nota
Funding Information:We would like to thank NMBS for their provision of data in the context of this research. Thanks as well to Ben Waterson from the University of Southampton who gave helpful feedback during the modeling process. The research reported in this article was supported by FWO (Fonds Wetenschappelijk Onderzoek, file number 1S09616N ).
Funding Information:
We would like to thank NMBS for their provision of data in the context of this research. Thanks as well to Ben Waterson from the University of Southampton who gave helpful feedback during the modeling process. The research reported in this article was supported by FWO (Fonds Wetenschappelijk Onderzoek, file number 1S09616N).
Publisher Copyright:
© 2020 Elsevier Ltd
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.