Grid-Based Traffic Algorithm Selection for Coverage-Aware Routing
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Solution Overview
Problem
Existing traffic estimation systems rely on a single general-purpose algorithm that is inefficient due to varying performance based on coverage, leading to inaccuracies and computational inefficiencies across different regions.
Innovation Solution
Segmenting routes based on grid spaces or road types and employing multiple traffic algorithms tailored to specific grid spaces or road types to generate accurate traffic data, using a combination of historical, real-time, Bayesian inference, and deep learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single general-purpose algorithm is used for traffic estimation across all regions, then the system is simple to operate and maintain, but the accuracy and computational efficiency deteriorate due to varying performance based on coverage in different regions
Solution Approach 1:
The patent segments the routes into multiple grid spaces and further segments each grid space into road type segments (e.g., highway, arterial, local road). This segmentation allows different algorithms to be applied to different segments based on their specific characteristics, thereby improving traffic data accuracy while managing system complexity through structured division.
Solution Approach 2:
The patent applies the principle of local quality by selecting different traffic algorithms for different grid spaces and road types based on local characteristics such as vehicle coverage density. High-coverage areas use computationally expensive algorithms (e.g., deep learning), while low-coverage areas use computationally inexpensive algorithms (e.g., historical data), optimizing both accuracy and efficiency for each local context.
2Measurement precision
If computationally expensive algorithms are used in all areas, then traffic data accuracy is improved, but computational efficiency and resource utilization deteriorate
Solution Approach 1:
The patent changes the parameter of algorithm selection based on the vehicle coverage parameter of each grid space. By evaluating the coverage density and dynamically selecting algorithms with appropriate computational complexity, the system achieves high accuracy in high-coverage areas while maintaining computational efficiency in low-coverage areas, thus resolving the contradiction between accuracy and productivity.
3Productivity
If multiple traffic algorithms are implemented for different grid spaces and road types, then computational efficiency is improved by using appropriate algorithms for each segment, but the system complexity increases
Solution Approach 1:
The patent manages algorithm complexity through hierarchical segmentation: first dividing the route into grid spaces, then further dividing each grid space into road type segments. This structured segmentation provides a clear framework for algorithm selection and management, making the complex multi-algorithm system more tractable and maintainable.
Solution Approach 2:
The patent performs preliminary actions by pre-segmenting routes into grid spaces and road types before algorithm selection. This pre-processing creates a structured foundation that simplifies subsequent algorithm selection and execution, reducing the operational complexity of managing multiple algorithms during runtime.
4Measurement precision
If route segmentation based on grid spaces and road types is performed, then algorithm selection accuracy is improved, but the processing time and system complexity increase
Solution Approach 1:
The patent segments routes into grid spaces and road types to improve algorithm selection accuracy. While segmentation adds processing steps, it enables more precise matching of algorithms to local conditions, ultimately improving the quality of traffic estimates and justifying the additional processing time through better results.
Data Source
AI summary
Methods and systems for generating traffic data are provided. An example method includes receiving one or more routes from a first location to a second location, receiving vehicle data along each of the one or more routes, and segmenting the one or more routes, based on a plurality of grid spaces on a map. The method further includes determining, based on the vehicle data, a first algorithm corresponding to a first grid space from the plurality of grid spaces and a second traffic algorithm corresponding to a second grid space from the plurality of grid spaces. The method further includes generating traffic data, along the one or more routes, using the first algorithm and the second algorithm.