Transportation Mode Detection Using Stop Segmentation
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Solution Overview
Problem
Existing wireless devices face challenges in accurately detecting changes in transportation modes, leading to difficulties in tracking and logistics management.
Innovation Solution
An electronic device equipped with memory circuitry, a wireless interface, and processor circuitry that obtains positioning and movement data to determine transition parameters, which are then used to identify changes in transportation modes, thereby improving detection accuracy and reducing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional transportation mode detection methods are used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent segments the transportation journey into multiple stops based on positioning data, and further segments each stop into distinct phases (arrival, stationary, departure) using movement data. This segmentation allows for precise identification of transition points between transportation modes by analyzing movement patterns at each stop phase separately, thereby improving detection accuracy without requiring a single complex detection system.
Solution Approach 2:
The patent introduces a temporal dimension by analyzing movement data over time at each stop, and a spatial dimension by combining positioning data with movement data. This multi-dimensional analysis approach enables accurate detection of transportation mode changes by examining patterns across multiple dimensions rather than relying on a single detection metric, resolving the contradiction between precision and complexity.
2Measurement precision
If transition parameters are determined for each stop, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary segmentation of the transportation journey into stops and phases before detailed transition parameter analysis. By pre-identifying potential transition points through positioning data and movement data patterns, the system prepares the data structure in advance, allowing for efficient subsequent analysis of transition parameters only at relevant stops, thus improving precision without proportionally increasing processing time.
Solution Approach 2:
The patent applies detailed transition parameter determination selectively at specific stops where transportation mode changes are likely to occur, rather than uniformly across all stops. By focusing computational resources on local areas of interest (stops with detected movement patterns indicating potential transitions), the system achieves high measurement precision at critical points while minimizing overall processing time.
3Reliability
If multiple criteria are used to detect transportation mode changes, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent segments the detection criteria into distinct categories: positioning data-based criteria (stop identification), movement data-based criteria (transition parameter determination), and pattern recognition criteria (transportation mode classification). This segmentation of criteria into modular, independent evaluation components allows for reliable multi-criteria detection while managing complexity through structured organization of evaluation rules.
Solution Approach 2:
The patent implements dynamic criterion application where the specific criteria evaluated and their thresholds are adapted based on the detected transportation context and stop characteristics. The system dynamically adjusts which criteria are most relevant for each situation, rather than rigidly applying all criteria uniformly, thereby improving reliability through context-aware detection while reducing unnecessary computational complexity.
Data Source
AI summary
An electronic device includes memory circuitry, a wireless interface, and processor circuitry. The processor circuitry is configured to obtain positioning data and movement data from a wireless device. The processor circuitry is configured to determine, based on the positioning data, one or more stops including a first stop of a transportation journey of the wireless device. The processor circuitry is configured to determine, based on movement data associated with the first stop, a transition parameter associated with the first stop. The processor circuitry is configured to determine whether the transition parameter satisfies a first criterion indicative of a change of transportation mode at the first stop. The processor circuitry is configured to output, based on the transition parameter, a transportation parameter.