WWT Research • Applied Research Report
• 15 minute read

Machine Learning Models for Route Consolidation

We develop a generalizable machine learning method for route consolidation. The developed method is compared against a more traditional ad-hoc method. The machine learning method uses a deep autoencoder, K-means clustering and Procrustes distance. The machine learning method is shown to produce similar results to the more traditional method with the advantage of using a more generalizable approach.

Abstract

Finding common routes from a large set of individual trips is a difficult problem due to the natural complexity involved with nontrivial trips. Much of the current research for route consolidation has relied on clustering- or distance-based methods, along with ad-hoc rules for combining routes. We compare a more traditional physical-based method that uses clustering, graph theory and ad-hoc rules with a machine-learning method. In particular, the machine-learning method uses an autoencoder to reduce the number of trips in the dataset and find common or standard routes. The routes identified using the autoencoder are then post-processed using K-means clustering and Procrustes distance. We apply both methods to a mine haul truck trip dataset and show how the machine-learning method can largely replicate the results produced by the physical modeling method, thus providing a more generalizable alternative for route consolidation. 

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