Skip to Content

This browser is no longer supported.

For the best WWT.com experience, please use one of our supported browsers.

Safari Logo Safari Chrome Logo Chrome Firefox Logo Firefox Edge Logo Edge
Search wwt.com...
Top Page Results

See all search results

Featured Solutions
What's trending
Help Center
Home
Solutions & Services
Solutions
Automation & Orchestration
Cloud
Data Center
Digital
Digital Workspace
ESG
Mobility
Networking
Security Transformation
See all Solutions
See all Solutions
Services
Application Services
ATC Lab Services
Consulting Services
Customer Success
Infrastructure Services
Mergers & Acquisitions
Strategic Resourcing
Supply Chain & Integration
See all Services
See all Services
Industries
Utilities
Financial Services
Global Service Provider
Healthcare
Life Sciences
Manufacturing
Oil & Gas
Public Sector
Retail
See all Industries
See all Industries
Partners
Cisco
Dell Technologies
HPE
NetApp
VMware
f5
Intel
Microsoft
Palo Alto
See all Partners
See all Partners
Learning & Support
ATC
Communities
Events
Labs
Research
About
Footer Links
Careers
Contact Us
Diversity & Inclusion
Locations
News
Sustainability
WWT Research White Paper Data Analytics & AI Digital
WWT Research • Applied Research Report • April 19, 2020

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. 

"WWT Research reports provide in-depth analysis of the latest technology and industry trends, solution comparisons and expert guidance for maturing your organization's capabilities. By logging in or creating a free account you’ll gain access to other reports as well as labs, events and other valuable content."

Thanks for reading. Want to continue?

Log in or create a free account to continue viewing Machine Learning Models for Route Consolidation and access other valuable content.

© World Wide Technology. All Rights Reserved
  • About
  • Careers
  • Locations
  • News
  • Press Kit
  • Contact Us
  • Privacy Policy
  • Acceptable Use Policy
  • Quality
  • Information Security
  • Supplier Management
  • Cookies