Traffic Analytics System for Road Network Definition
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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 locations of roadway sections for data collection.
Innovation Solution
A traffic analytics system that partitions a road network into contiguous subzones using geospatial indexing, selects vehicle data indicative of operating conditions, generates features based on this data, and employs machine learning techniques to classify areas as part of a vehicle way or not, thereby determining geographic locations and boundaries of road networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual counters are used to collect traffic data, then data collection is simple and equipment costs are low, but the process is manually intensive and prone to human error
Solution Approach 1:
The patent replaces manual counting mechanisms with automated vehicle detectors that use sensor technology (inductive loops, radar, cameras) to automatically detect and count vehicles, eliminating human operators and reducing errors while maintaining simplicity in deployment
Solution Approach 2:
The system enables self-service data collection where the detection equipment automatically monitors traffic flow without requiring manual intervention, allowing continuous operation and reducing labor requirements while maintaining accuracy
2Reliability
If sensing equipment is installed to collect traffic data, then data collection is automated and accurate, but it requires purchase, installation, and maintenance of equipment
Solution Approach 1:
The patent segments the road network into multiple zones and uses distributed sensor nodes throughout the network, allowing each sensor to independently collect local data while the overall system maintains high reliability through redundancy and modular architecture
Solution Approach 2:
The sensing equipment is designed with multi-functionality to perform various traffic measurement tasks (vehicle detection, speed measurement, volume counting) using the same hardware platform, reducing the need for specialized equipment and simplifying maintenance
3Quantity of substance
If GPS tracker devices are used to collect traffic data, then data can be obtained from vehicles, but multiple passes through the roadway section are needed to gather sufficient data
Solution Approach 1:
The patent implements continuous data collection through permanently installed sensor equipment that operates 24/7, eliminating the need for repeated passes by mobile GPS devices and providing uninterrupted traffic flow data for comprehensive analysis
Solution Approach 2:
The system performs preliminary data collection during normal traffic operations without requiring special test passes, continuously gathering data in advance so that sufficient statistics are available immediately when analysis is needed
4Loss of information
If video cameras are used to sense vehicle movement, then detailed traffic data can be collected, but image processing is complex and resource intensive
Solution Approach 1:
The patent extracts only the necessary traffic parameters (vehicle presence, speed, direction) from the video data using targeted processing algorithms, separating essential information from redundant visual data to reduce computational burden while maintaining data completeness
Solution Approach 2:
The system uses partial image processing rather than complete analysis of all video frames, processing only the portions of images that contain relevant traffic information and using threshold-based detection to reduce computational resources while maintaining sufficient data quality
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.