Merge Lane Traffic Jam Detection via Road Link Classification
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Solution Overview
Problem
Current mapping and navigation systems face challenges in accurately detecting and assessing the impact of merge lane traffic jams on highways, which can lead to congestion and accidents, as they struggle to differentiate the contribution of merging vehicles to overall traffic congestion.
Innovation Solution
A computer-implemented method and apparatus that classify road links near a merge point into specific classes and process probe data to determine vehicle speed, enabling the automatic detection of merge lane traffic jams and initiating alerts on devices traveling on affected road links.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If probe data is collected and processed to detect traffic congestion, then traffic management capability is improved, but the ability to differentiate merge lane congestion from other congestion causes deteriorates
Solution Approach 1:
The system segments the road network into distinct zones relative to merge points: upstream highway zones, downstream highway zones, upstream ramp zones, and downstream ramp zones. This segmentation allows independent analysis of traffic patterns in each zone, enabling precise identification of whether congestion originates from merge lane activities or other sources. Probe data is processed separately for each zone to detect speed differentials characteristic of merge-induced congestion.
Solution Approach 2:
The system introduces merge point topology data as an intermediary layer between raw probe data and congestion analysis. This intermediary structure captures the spatial relationships and connectivity patterns around merge points, allowing the system to contextualize probe data and distinguish merge-related congestion from other congestion types by analyzing traffic flow patterns through the merge point topology.
2Measurement precision
If detailed probe data processing is performed to identify merge lane traffic jams, then detection accuracy is improved, but computational complexity and data processing requirements worsen
Solution Approach 1:
The system applies local quality by focusing computational resources only on road segments in the vicinity of merge points rather than processing entire road networks uniformly. Probe data processing is selectively applied to upstream and downstream zones of identified merge points, reducing overall computational complexity while maintaining high detection accuracy for merge-related congestion events.
Solution Approach 2:
The system performs preliminary action by pre-identifying and storing merge point topologies and associated road segments before congestion detection occurs. This preprocessing step creates a ready-reference framework of merge point locations and their connected road networks, eliminating the need for complex real-time identification during congestion analysis and reducing computational burden during actual detection operations.
Data Source
AI summary
An approach is provided for automatically detecting a merge lane traffic jam. The approach involves, for example, determining a plurality of road links in proximity to a merge point comprising a highway and a ramp. The method also involves processing probe data collected from the plurality of road links to classify the plurality of road links, one or more sublinks of the plurality of road links, or a combination thereof into at least one of a highway upstream class, a merging area class, a highway downstream class, a ramp downstream class, and a ramp upstream class. The method further involves determining vehicle speed data for the highway upstream class, the merging area class, the highway downstream class, the ramp downstream class, the ramp upstream class, or a combination thereof. The method further involves automatically determining an occurrence of the merge lane traffic jam based on the vehicle speed data.