Abstract
This paper presents a decision-tree based model to forecast rail transit ridership at the station level according to the surrounding land-use patterns. The canonical correlation analysis (CCA) method is used to identify key land use variables by evaluating their degrees of contribution to the rail transit station demand, which can effectively reduce dimensionality and complexity of the decision tree. A full month of Smart Card data and detailed regulatory land use plan from Chongqing, China are collected for model development and validation. The proposed model offers the capability of targeting key lane use patterns and associating them with rail transit station boarding and alighting demand at a high level of accuracy. The proposed model can reveal underlying rules between rail transit station demand and land use variables, and can be used to assist in developing the Transit Oriented Development (TOD) plans to improve land use and transit operational efficiency.
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Published In
Journal of Urban Planning and Development
Volume 142 • Issue 4 • December 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Aug 19, 2015
Accepted: Dec 23, 2015
Published online: Mar 30, 2016
Discussion open until: Aug 30, 2016
Published in print: Dec 1, 2016
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