Road Obstruction Intensity Detection Using Time-Space Diagrams
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Solution Overview
Problem
Mapping and navigation service providers face challenges in determining and quantifying the intensity of road obstructions within road networks, which affects traffic flow and requires more detailed data for efficient routing and diversion decisions.
Innovation Solution
A method and system that utilize probe data to generate a time-space diagram (TSD) to determine the intensity of road obstructions and calculate diversion confidence for routing decisions, incorporating machine learning to predict obstruction intensity and confidence levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional presence-only detection methods are used, then the system is simple to operate, but the measurement precision of road obstruction intensity is insufficient
Solution Approach 1:
The patent transforms probe data from traditional spatial-temporal representation into a Time-Space Diagram (TSD) dimensionality, where traffic flow patterns are visualized in a transformed coordinate system. This dimensional transformation enables quantitative intensity measurement by analyzing patterns in the TSD that are not apparent in conventional representations, thereby improving measurement precision without requiring additional physical sensors.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw probe data into TSD representations, which then serve as the basis for intensity determination. This intermediary TSD layer acts as a mediator between raw data and final intensity measurements, enabling sophisticated analysis while keeping the overall system architecture manageable through modular processing steps.
2Measurement precision
If detailed probe data processing is performed to generate TSD, then the measurement precision improves, but the loss of time increases
Solution Approach 1:
The patent performs preliminary transformation of probe data into TSD representations in advance, creating a standardized intermediate format that can be quickly analyzed for intensity determination. By pre-processing data into this structured TSD format, the system reduces the computational burden during real-time intensity assessment, thereby minimizing time loss while maintaining high measurement precision.
Solution Approach 2:
The patent changes the parameter representation of probe data by transforming it into a different coordinate system (TSD) with distinct mathematical properties. This parameter transformation enables more efficient computation of intensity metrics, as the TSD representation reveals patterns and relationships that allow for faster quantitative analysis compared to processing raw probe data directly.
3Reliability
If quantitative intensity determination is implemented, then the reliability of routing decisions improves, but the device complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where TSD patterns are continuously analyzed and used to determine obstruction intensity, which then feeds back into routing decisions. This feedback loop enables reliable, data-driven routing recommendations by systematically processing probe data through TSD generation and intensity determination, providing consistent and trustworthy outputs despite the increased computational complexity.
Data Source
AI summary
An approach is provided for detecting road obstruction intensity for location-based applications and services. The approach involves, for instance, collecting probe data associated with a road segment. The approach also involves processing the probe data to generate a time space diagram (TSD). The TSD plots the probe data according to distance from an origin point on the road segment over time. The approach further involves determining an intensity of the road obstruction based on the TSD. The approach further involves determining a diversion confidence for diverting a route from the road segment based on the intensity and providing the diversion confidence as an output.