Anomalous Trajectory Detection in Real-Time Traffic Monitoring
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Solution Overview
Problem
Current systems for detecting abnormal traffic events in geographical locations are inefficient, relying on human intervention and limited to individual vehicles, making it cumbersome and expensive to accurately identify and mitigate traffic abnormalities across a broader area.
Innovation Solution
A computer-implemented method and system that captures real-time traffic data using multiple capture nodes with cameras, proximity sensors, and air quality sensors, processing images through convolutional layers and neural networks to track traffic objects and detect anomalous trajectories, utilizing Generative Adversarial Networks (GANs) for feature identification and auto-encoder networks for anomaly detection, and communicating with mitigation agencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized traffic control systems with human intervention are used, then traffic data can be stored and processed, but the system becomes cumbersome and inefficient for real-time abnormality detection
Solution Approach 1:
The system employs automated machine learning models and neural networks that self-process traffic data to detect abnormalities without human intervention. The algorithms automatically classify traffic patterns, identify anomalies, and generate alerts, enabling the system to serve itself in real-time detection tasks while maintaining high reliability through continuous learning from historical data.
Solution Approach 2:
The patent replaces manual human analysis with computational algorithms and neural networks. The system uses automated image processing, trajectory analysis, and pattern recognition algorithms to substitute human operators in detecting and classifying traffic abnormalities, thereby dramatically improving detection efficiency while maintaining reliable processing through systematic computational methods.
2Measurement precision
If decision support systems are provided to individual vehicles, then abnormal events can be detected in the vehicle vicinity, but the system becomes expensive and cumbersome for broader geographical coverage
Solution Approach 1:
The patent merges multiple detection approaches by combining centralized server-based processing with edge computing capabilities on vehicles. The system integrates server-side machine learning models with on-vehicle sensors and processors, creating a hybrid architecture that achieves high detection accuracy through coordinated multi-level processing while reducing overall system complexity through distributed intelligence.
Solution Approach 2:
The system creates a universal detection framework that serves multiple functions: it can detect abnormalities individually for each vehicle while simultaneously providing comprehensive geographical coverage. The standardized algorithmic approach can be deployed across any number of vehicles and locations, making the system scalable and adaptable to various deployment scenarios without proportionally increasing complexity.
3Measurement precision
If manual classification of traffic abnormalities is performed, then accurate identification of accidents and violations is possible, but the process becomes time-consuming and less efficient
Solution Approach 1:
The system performs preliminary classification by pre-training machine learning models with extensive historical traffic data before actual deployment. The neural networks are预先 trained to recognize patterns of normal and abnormal traffic behavior, enabling them to rapidly classify new abnormalities in real-time without requiring manual analysis. This preliminary preparation allows the system to achieve both high accuracy and fast response times during operational use.
Solution Approach 2:
The patent replaces manual classification processes with automated machine learning algorithms. The system uses supervised learning models that have been trained to automatically distinguish between different types of traffic abnormalities (accidents, violations, congestion) by analyzing patterns in traffic data, thereby eliminating time-consuming manual classification while maintaining or improving accuracy through consistent algorithmic application.
Data Source
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AI summary
A method of detecting abnormal traffic events (525) in a geographical location is disclosed. The method comprises determining traffic objects in real time based on a traffic environment in the geographical location. The traffic environment comprises vehicular traffic and pedestrian traffic. The method also includes determining a traffic activity by tracking the traffic objects in the geographical location. The traffic activity comprises plurality of object trajectories including location, position, and object boundaries associated with the traffic objects in real time. The method further includes detecting abnormal traffic events (525) in the geographical location by determining one or more anomalous object trajectories in the traffic activity.