Road Network Definition via Machine Learning Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for collecting and analyzing traffic data are either manually intensive, prone to error, or require significant resources and infrastructure for equipment installation and maintenance, and are not efficient in defining geographic locations of roadway sections.
Innovation Solution
A method involving partitioning a road network into contiguous subzones using geospatial indexing systems, selecting vehicle data indicative of operating conditions, generating features from this data, and processing it with machine learning techniques to classify and determine geographic locations, thereby defining road network zones and boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual counters are used to collect traffic data, then the method is simple and requires no equipment, but it is manually intensive and prone to human error
Solution Approach 1:
The patent replaces manual counting mechanisms with automated vehicle data processing systems. Instead of manual counters physically observing and recording traffic, the system automatically processes vehicle data from multiple sources (GPS trackers, traffic cameras, mobile devices) to extract traffic metrics, eliminating manual labor and human error while maintaining simplicity in implementation.
Solution Approach 2:
The system enables self-service data collection by automatically aggregating and processing traffic data from various sources without requiring manual intervention. The automated processing pipeline continuously collects, validates, and analyzes vehicle data to generate traffic metrics, allowing the system to serve itself rather than requiring manual operational input.
2Reliability
If sensing equipment is installed to collect traffic data, then data collection is automated and reduces human error, but it requires purchase, installation, and maintenance of equipment
Solution Approach 1:
The patent employs multi-functional existing devices and data sources (GPS trackers, traffic cameras, mobile devices) to serve multiple purposes: collecting location data, capturing images, and providing traffic metrics. Rather than installing dedicated sensing equipment for each function, the system leverages universally available devices that can perform multiple data collection tasks, reducing equipment complexity while maintaining reliability.
Solution Approach 2:
The system introduces a centralized data processing platform as an intermediary between various data sources and the final traffic metrics. This intermediary layer aggregates data from multiple sources, validates consistency, processes the information, and generates accurate traffic metrics without requiring direct installation of complex sensing equipment at each data collection point.
3Reliability
If GPS tracker devices are used to collect traffic data, then data collection is automated, but multiple passes through the roadway section are needed to gather sufficient data
Solution Approach 1:
The patent merges data from multiple sources (GPS trackers, traffic cameras, mobile devices) into a unified processing system that simultaneously collects and analyzes various types of traffic data. This consolidation allows the system to gather sufficient data for accurate traffic metrics in a single pass through the roadway section, as multiple data streams provide complementary information that accelerates data collection without requiring repeated passes.
Solution Approach 2:
The system performs preliminary data aggregation and processing by continuously collecting and pre-processing data from multiple sources before final analysis is needed. This preliminary action involves accumulating data in advance, validating it against established criteria, and preparing it for rapid processing, thereby reducing the time required for subsequent analysis and eliminating the need for multiple sequential passes through the roadway section.
4Quantity of substance
If image processing is used to extract traffic data from video footage, then comprehensive traffic data can be collected, but the process is complex and resource intensive
Solution Approach 1:
The patent extracts and processes only the essential data elements from video footage and other sources rather than analyzing the complete image data. By identifying and extracting specific features such as vehicle locations, speeds, and patterns, the system obtains comprehensive traffic data without performing computationally intensive processing of entire video streams, thereby reducing resource requirements while maintaining data completeness.
Solution Approach 2:
The system applies partial processing by focusing computational resources on the most critical data extraction tasks rather than exhaustive analysis of all available data. By prioritizing processing of high-value information (vehicle trajectories, congestion patterns, intersection flow) and using lighter processing for supplementary data, the system achieves comprehensive traffic insights with reduced computational intensity compared to full image processing approaches.
Data Source
AI summary
Disclosed are systems and methods relating to defining a road network used by vehicles for movement and/or parking. A classifier may be employed for identifying portions of the road network via machine learning techniques and processing of historical telematic data.