Road Abnormality Detection for Early Traffic Congestion Prediction
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Solution Overview
Problem
Existing road condition monitoring systems only provide information on existing traffic congestion, leaving drivers with insufficient time to avoid it.
Innovation Solution
A system where vehicles detect abnormalities on the road and transmit notification data to a server, which calculates an analysis value for traffic congestion influence and predicts potential congestion, sending preliminary and prediction data to drivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the system only transmits information after traffic congestion has already occurred, then the information processing is simple, but the driver cannot secure sufficient time to avoid the traffic congestion
Solution Approach 1:
The system performs preliminary actions by transmitting notification data about detected abnormalities (accidents, obstacles, road conditions) before traffic congestion actually occurs. The server calculates an analysis value based on detection frequency and transmits prediction data when the analysis value indicates high congestion risk, providing drivers with advance warning to take preventive action.
2Measurement precision
If the system transmits prediction data based on complex analysis values, then the accuracy of congestion prediction improves, but the processing load and system complexity increases
Solution Approach 1:
The server changes parameters by calculating an analysis value that quantifies congestion risk based on detection frequency of abnormalities. This parameter transformation converts raw detection data into a meaningful metric that triggers prediction data transmission only when necessary (when analysis value indicates high congestion possibility), balancing accuracy with processing efficiency.
3Reliability
If the system monitors and analyzes all abnormality detections from all vehicles, then the congestion prediction accuracy improves, but the communication data volume and processing load increases
Solution Approach 1:
The system applies local quality by transmitting different types of data (notification data, preliminary report data, prediction data) to different vehicles based on their location and the calculated analysis value. Not all vehicles receive all types of data - only vehicles in affected areas receive prediction data when the analysis value indicates high congestion risk, reducing overall data transmission volume while maintaining monitoring reliability.
Data Source
AI summary
A road condition monitoring system includes vehicles and a server configured to communicate with the vehicles. Each of the vehicles is configured to, when an abnormality on a road is detected, transmit, to the server, notification data including a type of the abnormality, a detection position at which the abnormality is detected, and a detection time at which the abnormality is detected. The server includes a processor. The processor calculates an analysis value indicating a degree of influence on traffic congestion at the detection position based on the detection time. When a condition that the analysis value indicates a high degree of influence on the traffic congestion is satisfied, the processor transmits, to the vehicles, prediction data indicating that a possibility of occurrence of the traffic congestion at the detection position is high.