Traffic Jam Information Providing Device for Accurate Congestion Detection
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Solution Overview
Problem
Existing traffic congestion prediction technologies face challenges in accuracy due to reliance on single vehicle-mounted devices and require significant management costs and labor, especially when detecting congestion near road features like signals and railway crossings.
Innovation Solution
A system that uses a traffic jam information providing device connected to multiple vehicles via communication networks, determining target object positions and calculating traffic jam information based on both first and second sensing information to provide accurate road status without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traffic congestion is detected using only vehicle-mounted devices based on speed and position data, then the system complexity is reduced, but the measurement precision of traffic congestion detection deteriorates
Solution Approach 1:
The system segments the detection function by separating the data collection role (vehicle-mounted devices capturing speed and position) from the analysis role (server performing image recognition and congestion determination). This segmentation allows each component to be optimized independently while achieving high detection accuracy through coordinated operation.
Solution Approach 2:
The server acts as an intermediary that receives data from multiple vehicle-mounted devices, performs comprehensive image recognition analysis, and generates authoritative traffic congestion information. This intermediary approach enables centralized processing that improves measurement precision without requiring complex individual vehicle systems.
2Ease of operation
If a vehicle-mounted device autonomously predicts traffic congestion using only local sensing information, then the ease of operation is improved, but the measurement precision deteriorates due to inability to reflect road status
Solution Approach 1:
The system merges local vehicle data (speed, position) with remote server capabilities (image recognition, road status analysis) to create a comprehensive traffic congestion detection system. This combination preserves the ease of autonomous operation at the vehicle level while achieving high precision through server-side analysis of multiple data sources including road images and environmental factors.
Solution Approach 2:
The server provides universal traffic congestion detection services to multiple vehicles simultaneously, performing image recognition and road status analysis that benefits all connected vehicles. This multi-functional approach allows each vehicle to maintain simple operation while accessing enhanced detection precision through the shared server infrastructure.
3Measurement precision
If a vehicle traveling management device is installed in an office of a public transportation carrier to manage multiple vehicles, then the measurement precision of traffic congestion detection is improved, but the loss of substance increases due to management costs and labor requirements
Solution Approach 1:
The server automatically performs image recognition, vehicle position tracking, and traffic congestion determination without requiring human intervention. The system self-manages the complex analysis tasks that would otherwise require labor-intensive office-based management, thereby reducing management costs and labor requirements while maintaining high detection precision through automated processing.
Data Source
AI summary
A traffic-jam information providing device is configured to determine the position of a target object based on first sensing information relating to the position of a target object causing a reduction of speed of a moving object. By reference to second sensing information relating to a moving status of the moving object when the moving object is moving along a path having a plurality of sections, the traffic jam information providing device is configured to calculate traffic jam information in the path which the moving object is moving along based on the second sensing information in a section other than a predetermined section determined with reference to the position of a target object detected based on the first sensing information.