Traffic Surprise Factor Visualization System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current navigation systems lack sufficient context in providing traffic data, often reporting congestion on a large scale with minimal detail, failing to alert users to unexpected traffic events or 'traffic surprises' that deviate from expected patterns.
Innovation Solution
A system and method for calculating a 'surprise factor' based on the ratio of historical and real-time traffic data, which compares traffic conditions to a threshold to generate a surprise traffic message, allowing users to visualize anomalies such as changes in speed or volume on routes, displayed on maps or origin-destination matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traffic data is aggregated on a large scale with minimal context, then data collection complexity is reduced, but information completeness and user awareness of unexpected traffic events deteriorates
Solution Approach 1:
The patent segments traffic data into expected (historical) and unexpected (real-time anomaly) components. By dividing traffic information into baseline patterns and deviations from those patterns, the system maintains comprehensive information about traffic conditions while managing data complexity through structured organization of historical versus real-time data streams.
Solution Approach 2:
The patent introduces an intermediary processing layer that compares real-time traffic data against historical patterns to generate surprise factors. This intermediary analysis layer transforms raw complex traffic data into simplified surprise metrics that indicate unexpected conditions, thereby reducing the complexity presented to users while preserving complete information about traffic anomalies.
2Reliability
If detailed real-time traffic monitoring is implemented, then awareness of unexpected traffic events is improved, but computational requirements and processing time increase
Solution Approach 1:
The patent extracts only the essential comparison between historical and real-time traffic patterns, focusing specifically on calculating surprise factors that indicate unexpected events. By extracting and highlighting only the anomalous deviations rather than processing all traffic data details, the system maintains high reliability in detecting unexpected events while reducing overall computational burden.
Solution Approach 2:
The patent transforms detailed traffic monitoring data into a derived parameter called the 'surprise factor' that quantifies unexpected conditions. By changing the parameter representation from raw traffic volumes and speeds to a normalized surprise metric, the system achieves reliable detection of traffic anomalies with reduced computational complexity in the comparison and analysis stages.
3Ease of operation
If comprehensive traffic context is provided to users, then decision-making quality is improved, but data transmission and processing load increases
Solution Approach 1:
The patent applies local quality by providing detailed surprise factor information specifically at locations where unexpected traffic events occur, rather than uniformly processing and transmitting comprehensive traffic data for entire routes. Users receive targeted contextual information about anomalies at specific road segments, improving route planning decisions at critical points while minimizing overall data processing energy requirements.
Data Source
AI summary
Systems, methods, and apparatuses are described for monitoring and visualizing traffic surprises. Traffic data is received for a region of interest comprising one or more routes. A surprise factor may be calculated for one or more routes in the region of interest based on a ratio of historical traffic data and real-time traffic data. A comparison may be performed of the surprise factor to at least one threshold. A surprise traffic message may be generated based on the comparison.