Intelligent Telematics System for Automated Road Network Zone Classification
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Solution Overview
Problem
Current methods for collecting traffic data, such as manual counting and equipment-based sensing, are labor-intensive, prone to errors, and costly, while GPS tracker devices require multiple passes and complex data processing, limiting the efficiency and accuracy of traffic metric collection.
Innovation Solution
An intelligent telematics system comprising monitoring devices, a datastore, and a network interface that transmits raw vehicle data, processes it using machine learning techniques to classify road network zones, and generates features for determining geographic locations, thereby defining road network zones and improving traffic data collection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual counting methods are used to collect traffic data, then the equipment cost is reduced, but the labor intensity and human error increase
Solution Approach 1:
The patent replaces manual counting mechanisms with automated vehicle detectors that use sensor systems (inductive loops, radar, cameras) to automatically detect and count vehicles. This substitution eliminates the need for manual observation and recording while reducing labor intensity and human error in traffic data collection.
Solution Approach 2:
The system enables self-service data collection where the automated detection equipment independently performs traffic monitoring without requiring manual intervention. The detectors automatically sense vehicle presence, count vehicles, and record traffic metrics, allowing the system to serve itself rather than requiring continuous human operation.
2Measurement precision
If sensing equipment is installed to collect traffic data, then data collection accuracy is improved, but the system complexity and cost increase
Solution Approach 1:
The patent segments the traffic data collection system into multiple independent detection units that can be deployed at different locations. Each sensing equipment unit operates independently to collect local traffic data, which is then aggregated by a central processing system. This segmentation allows for modular deployment, reducing overall system complexity while maintaining accuracy through distributed measurement.
Solution Approach 2:
The sensing equipment is designed with multi-functionality to perform various traffic monitoring tasks (vehicle detection, counting, speed measurement, occupancy detection) using the same hardware platform. This universal approach reduces system complexity by consolidating multiple specialized devices into a single versatile system that can collect diverse traffic metrics.
3Ease of manufacture
If GPS tracker devices are used to collect traffic data, then the equipment cost is reduced, but the data collection time and processing complexity increase
Solution Approach 1:
The patent implements preliminary action by pre-configuring GPS trackers with specific monitoring parameters and thresholds before deployment. The devices are pre-programmed to automatically detect and transmit relevant traffic data when conditions are met, eliminating the need for manual data extraction and reducing processing time. This preliminary setup allows for efficient, automated data collection without requiring extensive post-collection processing.
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.