Road Closure Graph Inconsistency Resolution
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Solution Overview
Problem
Current traffic service providers face significant challenges in accurately reporting road closures, leading to inconsistencies in road closure states between adjacent road segments, which can impact trip planning and estimated time of arrival, and require excessive computational resources to maintain incorrect data.
Innovation Solution
A system that uses a roadway graph to evaluate the entire road network around a closure, calculating closure likelihood scores and verifying road closure reports through journalistic and automatic detection methods, resolving inconsistencies by changing closure states to ensure consistency within the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If road closure reports are reported with respect to individual road segments or links, then detailed road closure information can be provided, but inconsistencies occur where adjacent or neighboring road segments may have different road closure states when such differing states are not likely to occur or is otherwise not possible
Solution Approach 1:
The system implements feedback by continuously monitoring road closure states across adjacent road segments and automatically detecting inconsistencies. When a inconsistency is detected (e.g., adjacent segments showing different closure states), the system resolves it by updating the states to be consistent, thereby maintaining data reliability while preserving detailed reporting capabilities
Solution Approach 2:
The system performs self-service by automatically resolving road closure inconsistencies without requiring manual intervention. The automated detection and resolution mechanism maintains data consistency across the road network independently, eliminating the need for manual verification and correction of closure states
2Reliability
If road closure inconsistencies are manually verified and resolved, then data accuracy can be maintained, but excessive computational resources and time are required
Solution Approach 1:
The system replaces manual verification processes with an automated computational mechanism that efficiently detects and resolves road closure inconsistencies. This substitution eliminates the need for excessive computational resources and time by implementing smart detection algorithms that identify inconsistencies and apply resolution rules automatically
Solution Approach 2:
The system maintains data accuracy through self-service automation, where the inconsistency resolution process operates independently without manual intervention. This approach improves productivity by resolving inconsistencies efficiently through automated rules rather than consuming excessive computational resources on manual verification
Data Source
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AI summary
An approach is provided for resolving an inconsistency in road closure data stored in a mapping platform. The approach, for example, involves processing map data to generate a roadway graph representing a spatial relationship between a first road segment and a second road segment. The spatial relationship indicates that a first closure state of the first road segment cannot differ from a second closure state of the second road segment. The approach also involves determining that the inconsistency in the road closure data for the first road segment and the second road segment indicates that first closures state and the second closure state do not match. The approach further involves changing the road closure data stored in the mapping platform either to match the first road closure state with the second road closure state, or to match the second road closure state with the first road closure state in response to the inconsistency.