A Behind the Scenes Look at How Graph Networks Help Trip Scheduling and Planning
Using Graph Neural Networks to Improve Paratransit Scheduling
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Transit systems rely on a huge amount of data coming from a lot of different sources. This data is generally stored in well-organized tabular structures. A tabular approach for data storage, processing, and presentation works great in most cases.
However, tabular structures fail to provide the user with any information on the data interactions and interdependencies. Graphs can become powerful tools given the number and complexity of algorithms that can be applied to them. The Data Science team at Trapeze is working on using these algorithms to improve the way our core scheduling algorithm plans trips for our paratransit, PASS, product.
In this article, you'll learn:
- Why graph networks are better than tabular structures in terms of data interactions
- How graph networks provide a better representation of customer data
- Sneak peek into Trapeze's use of Neural Graphs to improve paratransit
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