Semantic-Free Traffic Prediction Using Tokenized Speed Patterns
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Solution Overview
Problem
Current traffic prediction systems rely heavily on historical data with embedded knowledge of significant events, making them inefficient and non-scalable due to the need for detailed semantics, such as holidays or accidents, which can be missing or difficult to correlate.
Innovation Solution
A method using tokenized travel-speed patterns to predict traffic by dividing travel-speed data into patterns, representing them with tokens from a dictionary of templates, and matching sequences to generate predictions without requiring event semantics, allowing for semantic-free traffic forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical traffic data with detailed event semantics is used for traffic prediction, then prediction accuracy may be improved, but device complexity and computational resources required increase significantly
Solution Approach 1:
The patent extracts and removes the semantic interpretation layer from the traffic prediction system. Instead of requiring detailed event semantics (holidays, accidents, weather), the system uses only raw travel-speed data patterns. This extraction of unnecessary semantic complexity enables accurate predictions while significantly reducing system complexity and computational requirements.
Solution Approach 2:
The patent creates a simplified copy of the traffic prediction approach that replicates prediction accuracy without the original complex semantic processing. By copying only the essential pattern-matching functionality from historical data rather than the full semantic annotation system, the patent achieves a lightweight system that maintains predictive power.
2Reliability
If detailed event semantics are annotated with historical traffic data, then prediction reliability improves, but loss of time and computational resources increase
Solution Approach 1:
The patent removes the time-consuming semantic annotation process from historical data processing. By using only raw travel-speed patterns without semantic tagging, the system achieves reliable predictions while dramatically reducing the time required for data processing and correlation analysis.
Solution Approach 2:
The system performs self-service pattern recognition where the travel-speed data itself contains the predictive information without requiring external semantic annotations. The data automatically reveals traffic patterns and event impacts through its inherent structure, eliminating the need for manual or automated semantic tagging processes.
3Measurement precision
If semantic knowledge of events is integrated into traffic prediction, then prediction accuracy improves, but ease of operation decreases due to data correlation requirements
Solution Approach 1:
The patent extracts the operational complexity of semantic data correlation from the system. By using only raw travel-speed data without semantic attributes, the system maintains prediction accuracy while making operationally simple data processing and system operation, eliminating complex correlation requirements between semantic event data and traffic patterns.
4Measurement precision
If historical traffic data is processed with event semantics, then prediction accuracy improves, but productivity decreases due to resource requirements
Solution Approach 1:
The patent removes the resource-intensive semantic processing steps from historical data analysis. By working with raw travel-speed patterns only, the system achieves accurate predictions with significantly improved productivity, reducing computational resources and processing time required for historical data analysis.
Data Source
AI summary
An approach is provided for semantic-free traffic prediction. The approach involves dividing a travel-speed data stream into a plurality of travel-speed patterns. The travel-speed data stream represents vehicle travel speeds occurring in a road network. The approach also involves representing each of the plurality of travel-speed patterns by a respective token. The respective token is selected from a dictionary of tokens representing a plurality of travel-speed templates determined from historical travel-speed data. The approach further involves matching a sequence of the respective tokens corresponding to said each of the plurality of travel-speed patterns to a best-fit sequence of tokens determined from the historical travel-speed data. The approach further involves determining a predicted sequence of tokens based on the best-fit sequence of tokens, and generating a traffic prediction for the road network based on the predicted sequence of tokens.