Multi-Modal Traffic Pattern Segmentation
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Solution Overview
Problem
Navigation systems face inaccuracies in traffic estimation due to the naive handling of probe data from various transportation modes, leading to errors in traffic speed estimation, especially when data from pedestrians, bikes, and vehicles are averaged without proper classification.
Innovation Solution
A method that calculates speed clusters and profiles from received speed data to identify multiple modes of transportation on a link, allowing for accurate traffic pattern generation and route optimization by distinguishing between different transportation modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If probe data from all transportation modes are averaged together, then the quantity of traffic information increases, but the measurement precision of traffic speed estimation deteriorates
Solution Approach 1:
The patent segments probe data by identifying multiple speed clusters corresponding to different transportation modes (e.g., vehicles, bikes, pedestrians). Each cluster represents a distinct mode with its own speed characteristics. This segmentation allows the system to process and analyze each mode separately rather than averaging all data together, thereby maintaining measurement precision while utilizing the full quantity of probe data from all modes.
2Quantity of substance
If additional information is collected with probes, then the quantity of data increases, but the reliability of traffic pattern estimation deteriorates when the information cannot be identified or classified
Solution Approach 1:
The patent employs a feedback mechanism through iterative clustering and validation processes. The system collects probe data, identifies speed clusters, validates whether each cluster represents a distinct transportation mode, and uses this feedback to refine subsequent analysis. This feedback loop ensures that only reliably classified data from identified transportation modes is used for traffic pattern estimation, preventing unclassified or misclassified data from degrading reliability.
Data Source
AI summary
A method and system include identification of road segments with multi-modal traffic patterns. A server receives speed data for a link. The server calculates a quantity of speed clusters from the speed data. The server calculates speed profiles for the quantity of speed clusters. The server identifies one or more modes of transportation for the link based on the quantity of the speed clusters and the speed profiles for the quantity of speed clusters.