Machine learning-based geocoding & intelligent geofencing

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By FarEye | October 13, 2022

Imagine a situation - You have 1,000 parcels to deliver to unique addresses and your truck has a capacity of 100 parcels. The number of possible combinations from one address to other could be {C(1000,100)}

63850511926305130236698511142022274281262900693853331776286816221524376994750901948920974351
797699894319420811933446197797592213357065053890 (A whopping 140 digits!)

A computer would take some days and a human - some years with a lot of coffee to check for the most optimized sequence based on distance and travel time for these many numbers of possibilities.

FarEye’s dynamic auto-routing is an answer to this huge challenge!

Routing requires accurate geocoding for both route planning and ETA calculations. FarEye uses sophisticated algorithms to self-learn the ‘correct’ address from attempted delivery addresses, success and failures. FarEye transforms a description of a location—such as a pair of coordinates, an address, or a name of a place—to a location on the earth's surface. The address is spatially displayed on the locations and patterns are recognized within the information. It converts English addresses into geographic coordinates as latitude & longitudes, which are used to mark positions on a map.

No personal information (PII) is tracked, which makes FarEye fully compliant with regulations.

As next steps, an intelligent geofencing is created in order to improve efficiency.

  • The manager can create different geofences of any shape or size, as a guide for auto-routing

  • This is typically used to mark out areas that should not be clubbed when creating routes

  • Routes are automatically recommended as per the load and segregation in the fences

  • The manager can combine multiple geo fences dynamically to optimize for that day’s volume and load

  • The manager can monitor unlimited locations, override and tweak the individual points on the route and reassign to different parcels

  • FarEye supports manual sticky routing where the manager can assign riders familiar with specific parts of the city/area or zone

All this and a million more algorithms and processes are run simultaneously in the back-end while fetching the most optimized routes for the complete fleet.

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