Enhanced user efficiency in route planning using route preferences

Abstract

In various implementations, routing factors are identified based on a routing request associated with a user, where the routing factors include route preferences of the user. Routes are generated based on the routing request. Preference weights are determined for the route preferences, where the preference weights correspond to machine learning models based on sensor data provided by one or more sensors in association with the user. Route scores are determined for the routes based on the preference weights. A suggested route is provided to a user device associated with the user, where the suggested route corresponds to a selected route of the routes and is provided based on the route score of the selected route.

Description

Background technique [0001] Route planning applications are typically used to provide users with directions to points of interest, such as specific buildings, addresses, geographic coordinates, and the like. In an example scenario, a user provides a route planning application with at least a start location and an end location (eg, start and end addresses). Route planning applications can analyze representations of roads and intersections to output travel routes. Conventional route generation algorithms attempt to minimize or optimize one of the following: the total distance traveled or the total travel time in the route provided to the user. [0002] Modern route planning applications are built around assumptions of constancy and generality such that the routes offered to users cannot be optimized for user preferences. While some have basic configurability, they fail to account for the trade-offs in preferences that a user might consider to arrive at an optimal route. An ex...

AI Technical Summary

Problems solved by technology

Using conventional methods, enabling users to implement alternative routes requires a significant investment

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