Digital Map Dissimilarity Detection Using Predicted and Trace Routes
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Solution Overview
Problem
Dissimilarities between digital maps and actual road networks cause transportation service coordination issues, as suggested routes may not be traversable, and updating map data to reflect changes in the road network is technically challenging.
Innovation Solution
A network system uses predicted route data and executed trace data to quantify differences, determining dissimilarities by generating paths, calculating expectation ratios and state scores, and modifying guidance instructions to improve transport service coordination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital maps are used for route guidance, then transportation services can be coordinated, but the maps become stale due to road network changes leading to incorrect routes
Solution Approach 1:
The system implements feedback by collecting actual trace data from transportation vehicles and comparing it with predicted route data from digital maps. This feedback loop enables the system to detect dissimilarities between map data and actual road network conditions, allowing for continuous improvement of route guidance accuracy through iterative map updates.
Solution Approach 2:
The system performs preliminary actions by proactively detecting dissimilarities between digital maps and actual road networks before they cause significant routing errors. By continuously monitoring and comparing trace data with predicted routes, the system can identify and flag map inaccuracies in advance, allowing for preventive map updates.
2Measurement precision
If map data is continuously updated to reflect road network changes, then route guidance accuracy improves, but the complexity of maintaining and verifying map accuracy increases
Solution Approach 1:
The system implements self-service by automatically collecting trace data from transportation vehicles and using it to detect and verify map dissimilarities without requiring manual intervention. The automated comparison between predicted and actual routes enables the system to self-update map accuracy information, reducing the complexity of manual map maintenance and verification.
Solution Approach 2:
The system merges the map maintenance function with the existing transportation service coordination system. By integrating trace data collection and dissimilarity detection into the routine transportation operations, the system leverages existing infrastructure and data flows to maintain map accuracy, avoiding the need for separate complex map maintenance systems.
3Measurement precision
If trace data from multiple sources is aggregated to detect dissimilarities, then detection accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system applies segmentation by dividing the trace data from multiple sources into individual data streams that are processed separately before being aggregated for dissimilarity detection. This segmentation approach allows for more manageable data processing, where each source's data can be pre-processed and filtered independently, reducing the overall computational complexity while maintaining detection accuracy.
Data Source
AI summary
A network system determines dissimilarities between a digital map and trace data of a road network in an area as service providers and service requestors coordinate service using the road network in the area. To determine dissimilarities the network system can determine a suggested route, determine a predicted route, receive executed trace data, and compare the predicted route data to the executed trace data for the suggested route. The network system may aggregate trace data when determining a dissimilarity. The network system can quantify the differences between traces to determine dissimilarities. Quantifications can include, ratios, bounds, and scores. The network system can determine and alternate route if a dissimilarity indicates that the state of a road segment has changed (e.g., from “open” to “closed”). The network system can modify guidance instructions if a dissimilarity indicates that a guidance instruction is misleading.