Bi-modality Detection for Diverging Road Segments
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Solution Overview
Problem
Routing algorithms fail to accurately account for speed differentials between lanes of traffic upstream of diverging road segments, leading to incorrect estimated times of arrival and suboptimal routing due to the transient bi-modality condition caused by split lane traffic.
Innovation Solution
A method to identify and determine the extent of bi-modality conditions by clustering speed distributions from probe points along road segments, allowing for the notification of higher and lower speed lanes and adjusting routing algorithms to avoid delayed lanes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If routing algorithms assume uniform traffic speed across all lanes, then the routing computation is simple and fast, but the estimated time of arrival becomes inaccurate when bi-modality conditions exist
Solution Approach 1:
The patent segments the traffic flow into multiple speed clusters (typically two clusters representing slow and fast lanes) rather than treating all traffic as uniform. This segmentation allows the routing algorithm to account for lane-specific speed variations by identifying which cluster each probe point belongs to, thereby improving ETA accuracy without requiring complete reconstruction of the traffic system
Solution Approach 2:
The patent replaces the traditional mechanical assumption of uniform traffic speed with a data-driven statistical approach using probe point speed measurements. By substituting the uniform speed model with cluster-based speed distributions derived from actual probe data, the system achieves more accurate ETA calculations while maintaining computational efficiency through statistical summarization
2Measurement precision
If routing algorithms account for lane-specific speed differentials, then the estimated time of arrival becomes more accurate, but the algorithm complexity and computational requirements increase
Solution Approach 1:
The patent changes the parameter representation from a single uniform speed value to multiple speed clusters with associated probabilities. This parameter transformation allows the algorithm to capture lane-specific speed variations while maintaining computational tractability by using statistical parameters (mean speeds, standard deviations, cluster probabilities) rather than simulating individual vehicle behaviors
Solution Approach 2:
The patent applies partial action by focusing computational resources on identifying and characterizing the dominant speed clusters rather than analyzing every individual probe point in detail. By extracting only the essential cluster characteristics (speed ranges, probabilities) needed for routing decisions, the system achieves improved accuracy without proportionally increasing computational burden
3Measurement precision
If the system collects and analyzes speed data from multiple probe points to identify bi-modality conditions, then the accuracy of speed distribution measurement improves, but the data processing complexity increases
Solution Approach 1:
The patent creates simplified representations (copies) of the complex probe point data by clustering speeds into representative groups with aggregate statistics. Instead of processing every individual probe point speed measurement, the system creates cluster summaries that capture the essential bi-modality characteristics, thereby reducing data processing complexity while preserving measurement precision
Solution Approach 2:
The patent transforms the raw speed data parameters into cluster-based parameters (mean speeds, standard deviations, cluster probabilities). This parameter transformation reduces the dimensionality of the data while maintaining the essential information needed to identify and characterize bi-modality conditions, balancing measurement precision with processing simplicity
Data Source
AI summary
A method, apparatus and computer program product are provided in order to identify a bi-modality condition upstream of diverging downstream road segments. In the context of a method, a distribution of speeds associated with a plurality of probe points representative of travel along the road segment upstream of diverging downstream road segments is determined. The method also includes evaluating the distribution so as to cluster the speeds associated with the plurality of probe points into a higher speed cluster associated with a higher speed and a lower speed cluster associated with a lower speed. The method also includes identifying a bi-modality condition upstream of the diverging downstream road segments based upon a relationship between the higher speed and the lower speed. The method further includes determining an extent to the bi-modality condition upstream of the diverging downstream road segments.