Traffic Bottleneck Ranking Using Road Segment Graph Aggregation
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Solution Overview
Problem
Conventional methods for identifying and ranking traffic bottlenecks are limited to small-scale applications, focusing on short-term disruptions in specific roadways and do not account for complex road networks or provide accurate economic and social cost metrics, which are crucial for commercial operations like freight transport.
Innovation Solution
A system and method that uses a processor to identify and rank traffic bottlenecks by generating a compressed undirected graph from road segment metrics, including speed, travel time, and disruption data, and applying a utility function that considers economic and social costs across an entire road network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional methods focus on short-term recurrent and non-recurrent traffic disruptions in relatively small geographical locations, then analysis complexity is reduced, but the system cannot provide useful information for commercial users managing vehicle fleets across wide geographical areas
Solution Approach 1:
The patent segments the traffic analysis system into multiple hierarchical levels: local traffic bottleneck detection units that analyze small geographical areas, regional aggregation modules that combine data from multiple local units, and a central commercial fleet management platform. This segmentation allows each component to operate independently at its optimal complexity level while collectively providing wide geographical coverage for commercial users.
2Ease of manufacture
If conventional methods use metrics such as speed and hourly volume to analyse traffic bottlenecks, then data collection is simplified, but the system cannot accurately determine factors such as economic loss caused by delays
Solution Approach 1:
The patent merges multiple data sources and metric types into a comprehensive economic impact assessment model. It combines traditional traffic metrics (speed, volume) with commercial operation data (vehicle type, cargo value, delivery deadlines, fuel consumption) and applies multi-criteria decision analysis to calculate precise economic losses, thereby achieving both data collection simplicity and measurement precision.
3Ease of manufacture
If conventional methods limit analysis to traffic bottlenecks on specific types of roadways such as highways, then the system is easier to implement, but it ignores more-complex road networks that are often used for various commercial activities
Solution Approach 1:
The patent develops a universal traffic bottleneck analysis system that functions across multiple road network types (highways, urban streets, rural roads, industrial zones). The system employs adaptable detection algorithms that automatically adjust to different roadway characteristics, traffic patterns, and commercial activity types, enabling a single platform to serve diverse applications from personal commuting to commercial freight transport.
4Measurement precision
If the system aggregates multiple road segment metrics to identify traffic bottlenecks, then identification accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary filtering and preprocessing to road segment metrics before aggregation. Detection units first screen individual road segments using threshold-based criteria (e.g., speed reduction >20%, travel time increase >30%), then aggregate only those segments that meet the criteria. This preliminary action reduces the volume of data requiring complex computational aggregation while maintaining high identification accuracy for significant bottlenecks.
Data Source
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AI summary
Disclosed herein are systems and methods for identifying and ranking traffic bottlenecks. An example of such methods may include operating at least one processor to: receive traffic data associated with a road network comprising a plurality of road segments, the traffic data comprising vehicle speed data collected from a plurality of vehicles operating within the road network; determine a benchmark speed for each of the plurality of road segments; determine, for each of the plurality of road segments, whether a traffic disruption is present by comparing the benchmark speed thereof to vehicle speed data collected from at least one vehicle operating therealong; determine, for each of the plurality of road segments having the traffic disruption therealong, one or more road segment metrics associated therewith; identify a traffic bottleneck by aggregating a plurality of adjacent road segments having traffic disruptions therealong and the one or more road segment metrics associated therewith; and rank each traffic bottleneck based on one or more aggregated road segment metrics associated therewith.