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Bus line classification using neural networks
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DOI：10.1016/j.trd.2014.05.008.png)
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Abstract
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Grouping urban bus routes is necessary when there are evidences of significant differences among them. In Jimenez et al. (2013), a reduced sample of routes was grouped into clusters utilizing kinematic measured data. As a further step, in this paper, the remaining urban bus routes of a city, for which no kinematic measurements are available, are classified. For such purpose we use macroscopic geographical and functional variables to describe each route, while the clustering process is performed by means of a neural network. Limitations caused by reduced training samples are solved using the bootstrap method. (C) 2014 Elsevier Ltd. All rights reserved.
Keywords:
Cluster
Urban buses
Neural network
Bootstrap method
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