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Assessment of Road Condition over a Route Based on Ride Route Averaging Disclosure Number: IPCOM000246417D
Publication Date: 2016-Jun-06
Document File: 3 page(s) / 40K

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The Prior Art Database


Disclosed is a method to estimate the road conditions based on observed rider behavior. The user can then use roadway conditions as an input parameter for a route-planning system.

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Assessment of Road Condition over a Route Based on Ride Route Averaging

Cyclists make heavy use of Global Positioning System (GPS)-based ride-tracking services to plan routes. These services allow a user to view the routes travelled by other cyclists and accordingly plan a route. Information available to the user includes a leaderboard over defined segments showing the top times/speeds when traversing that segment, elevation based on topographical data, and the extent of usage (e.g., number of distinct riders and number of distinct rides over a specified segment, etc.).

Unfortunately, the available information omits the quality and/or condition of the roadways along the route, which is a factor in a cyclist's route planning. An individual planning a route has three available approaches:

 Travel the route either by bike or by car and record or remember the conditions of each road section for future use. This is time-consuming and does not help if the user has to bike the route to assess it.

 Rely on others' descriptions of the conditions; this may be recorded online (e.g. by tagging road sections, descriptions of routes), or simply shared via email or the like. This is subjective is unlikely to cover an entire multi-section route.

 Attempt to ascertain the condition using a tool that provides satellite imagery; however, satellite photos only allow the user to make gross distinctions (e.g., looks like a gravel road) and it do not provide the granularity required to determine the actual quality of the road

The novel contribution is a method to estimate the road conditions based on observed rider behavior. This behavior is reflected in recorded ride-route data; processing and filtering this data enables the application of rider patterns to the route and determination of conditions without actually having to traverse the route beforehand. This allows users to plan a route in advance and determine what equipment, if any, may be required, whether the route is appropriate for the group (based on size and skill of the group), and whether the route is suitable for the kind of ride (will it be technical and rough or smooth and fast).

The novel method applies known patterns of rider behavior (e.g., on a gravel road with ruts scraped and packed by automobile traffic, most riders follow the ruts) to ride-route data that is mined from various "social exercise" services. It then provides the route planner with an estimate of the road quality over each section of the route (assuming that the section has been previously traversed). This solution is a significantly more predictive method for determining roadway conditions, and it bases these on known patterns of rider behavior. Additionally, the method accommodates pattern changes
(i.e. tuning) over time for more accurate assessments. The method enables adjustments to a planned route or plan an entire route based on desired conditions (e.g., expressed as input parameters). The novelty lies...