Travel Time Calculation Using eCall Position Data
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Solution Overview
Problem
Existing methods for determining travel time of mobile user terminals are complex and costly, requiring special infrastructure and high participation of equipped vehicles, and struggle to distinguish between travel delays caused by accidents and traffic congestion.
Innovation Solution
A method that uses eCall information, including position data, to determine travel time by comparing timestamped specific information from mobile user terminals at two geographical points, allowing for accurate identification of delays without the need for special infrastructure, using existing mobile network data and emergency call system data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stationary radar sensors or video cameras are deployed to measure travel time, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent applies the self-service principle by utilizing the mobile user terminal itself to perform the measurement. The terminal's own movement data, captured by its built-in sensors and communication modules during normal operation, is used to determine travel time. This eliminates the need for external stationary sensors or specialized infrastructure, as the terminal serves both its primary function and the measurement function simultaneously.
Solution Approach 2:
The patent uses the mobile network as an intermediary to facilitate travel time measurement. The network's existing data collection mechanisms, signal timing information, and communication protocols serve as the mediator to transmit and process location and timing data between the terminal and the evaluation system, avoiding direct deployment of complex measurement infrastructure.
2Measurement precision
If floating car technologies are used to acquire traffic data, then measurement precision is improved, but loss of energy and cost increase due to high communication requirements
Solution Approach 1:
The patent applies partial action by selectively processing only the necessary portions of the terminal's operational data to determine travel time. Instead of continuously transmitting all possible data from the terminal, the system processes specific timing and location information that is already captured during normal terminal operation, reducing unnecessary communication energy consumption while maintaining measurement precision.
Solution Approach 2:
The terminal uses its own built-in sensors and processing capabilities to capture and preliminarily process movement data during normal operation. This self-service approach reduces the need for continuous external communication and centralized processing, thereby lowering energy consumption associated with wireless transmission while still achieving accurate traffic data collection.
3Device complexity
If existing mobile network data is used to determine travel time, then device complexity is reduced, but measurement precision deteriorates due to inability to distinguish delay causes
Solution Approach 1:
The patent merges multiple data sources and analysis approaches within the terminal itself. It combines location data from GPS or network-based positioning, timing information from communication protocols, and event detection from sensors to create a comprehensive picture of travel conditions. This integration allows the terminal to distinguish between different causes of delay (accidents, congestion, weather) by analyzing patterns across multiple data streams simultaneously.
Solution Approach 2:
The system implements feedback mechanisms where the terminal continuously monitors its own movement, compares actual travel time against expected values, and identifies anomalies that indicate specific delay causes. By analyzing the timing patterns and contextual information from multiple sensors, the terminal provides feedback about the nature of delays encountered, enabling precise differentiation between various disruption types without requiring additional infrastructure.
Data Source
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AI summary
In a process to generate traffic density information based on a mobile cellular radio network, call information from a first user and incorporating position information in a first network is also captured by a second network. The time and position information is logged and processed by a central unit, which subsequently determines the time taken to travel between two points.