Active Traffic Risk Control Using Real-Time Flow Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing road traffic safety management systems are passive and ineffective in preventing accidents and congestion, relying on post-accident measures, and historical data-based strategies fail to account for dynamic traffic conditions.
Innovation Solution
A method and system for active management and control that utilizes real-time traffic data, predictive models, and optimization algorithms to proactively adjust traffic strategies, including speed, lane, and vehicle type management, to minimize accidents and enhance safety and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional passive management methods are used, then response time after accidents is reduced, but accident prevention capability is insufficient
Solution Approach 1:
The system performs preliminary actions by predicting traffic flow and identifying high-risk sections before accidents occur. The risk prediction model continuously evaluates traffic data to proactively detect potential hazards, enabling preventive management actions to be taken before incidents happen, thus improving accident prevention capability while maintaining rapid response capability through the same predictive framework.
2Adaptability or versatility
If historical management and control strategies are used, then implementation simplicity is maintained, but adaptability to dynamic traffic conditions deteriorates
Solution Approach 1:
The system implements continuous feedback mechanisms where traffic flow data is constantly collected, analyzed by the prediction model, and used to dynamically adjust management strategies. The risk prediction results feed back into the decision-making process, enabling the system to adapt to changing traffic conditions in real-time. This feedback loop integrates seamlessly with existing infrastructure, achieving high adaptability without proportionally increasing system complexity.
3Measurement precision
If real-time traffic data analysis is implemented, then traffic safety prediction accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The system applies partial action by focusing computational resources on analyzing only the most critical traffic parameters and high-risk sections identified by preliminary filtering. Rather than processing all traffic data with equal depth, the model concentrates analytical power where it is most needed - on sections with elevated risk indicators - thus achieving high prediction accuracy while minimizing unnecessary computational resource consumption.
Data Source
AI summary
A method, device and system for active management and control of road traffic safety are disclosed. The method includes acquiring traffic data of a target road in real time, wherein the target road includes management and control sections; for each management and control section, judging whether there is a traffic accident according to the acquired traffic data; if so, formulating an emergency management and control strategy; if not, extracting traffic flow data from the traffic data, and generating predicted traffic flow data according to the traffic flow data by a traffic flow prediction model; generating a risk level according to the predicted traffic flow data by a risk prediction model; determining the current active management and control strategy of the management and control section according to the risk level and the predicted traffic data; and issuing the corresponding management and control strategy of each of the management and control sections.