Traffic Congestion Monitoring Using Link Clustering
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Solution Overview
Problem
Current traffic control methods rely on experience and are inefficient and inaccurate in identifying abnormal congestion, which can be caused by unpredictable events like traffic accidents or bad weather, leading to suboptimal traffic management.
Innovation Solution
A system that uses a processor to analyze real-time traffic data, employing algorithms like DBSCAN and Dijkstra to identify congested areas and differentiate between normal and abnormal congestion by clustering links and analyzing historical data, providing accurate and timely information for traffic control departments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traffic control department relies on experience to determine abnormal congestion, then the method is simple to implement, but the accuracy and efficiency of traffic control deteriorates
Solution Approach 1:
The system segments the traffic network into multiple links and clusters congested links to identify congested areas. By dividing the complex traffic monitoring task into smaller components (link-level congestion detection followed by area-level clustering), the system achieves accurate abnormal congestion identification without requiring monolithic complex infrastructure
Solution Approach 2:
The system introduces an intermediary processing layer that collects traffic data from multiple sources, processes it through congestion determination algorithms, and produces structured congestion information. This intermediary layer abstracts the complexity from both data collection and decision-making processes, enabling accurate abnormal congestion detection while maintaining manageable system complexity
2Measurement precision
If real-time traffic data analysis is implemented across the entire network, then the accuracy of congestion detection is improved, but the processing time and computational resources increase
Solution Approach 1:
The system processes traffic data at the link level first, determining congestion status for individual road segments. By segmenting the analysis into link-level and area-level stages, the system achieves comprehensive network-wide detection accuracy while processing smaller data units sequentially, reducing overall processing time compared to analyzing the entire network simultaneously
Solution Approach 2:
The system performs congestion determination on a partial set of links at each iteration, using clustering to identify congested areas from representative congested links. This partial action approach achieves sufficient detection accuracy for traffic control purposes while significantly reducing computational burden and processing time compared to exhaustive analysis of all links
Data Source
AI summary
The present disclosure relates to systems and methods for monitoring traffic congestion. The systems may perform the methods to obtain traffic data associated with speeds or locations of a plurality of vehicles at a first time point; determine a plurality of congested links based on the traffic data; determine one or more congested areas by searching for congested links that are topologically close and clustering the congested links generated by the search; for each of the one or more congested areas, determine whether the congested area is a normal congested area or an abnormal congested area; and display congestion information associated with at least one of the one or more congested areas, wherein the congestion information may include a designation indicating whether the at least one of the one or more congested areas is the normal congested area or the abnormal congested area.