Statistical Trip Itinerary Determination Using Sensor Fusion
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Solution Overview
Problem
The complexity of tracking and allocating funding for various modes of transportation, as modern users engage with multiple transportation options, poses a high-overhead task in today's modern environment.
Innovation Solution
A module, referred to as the Statistical Movement Monitoring (SMOM) module, uses LOCAMO data from mobile communication devices to accurately track travel routes and modes of transportation, even with limited availability and accuracy, by generating prospective trip plans and determining the most probable time series of transportation segments using emission and transition probability matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional tracking methods are used to monitor multiple transportation modes, then comprehensive travel data can be collected, but the computational and communication overhead becomes excessive
Solution Approach 1:
The mobile device's built-in sensors (accelerometer, gyroscope, GPS, barometer) autonomously collect and process travel data without requiring external tracking infrastructure. The device itself performs the monitoring function, eliminating the need for complex centralized tracking systems while maintaining reliable multi-mode transportation detection
Solution Approach 2:
The patent replaces traditional mechanical tracking infrastructure with sensor-based detection using the mobile device's inertial measurement units and other sensors. This substitution reduces computational overhead by performing local sensor fusion and analysis rather than relying on complex centralized processing systems
2Measurement precision
If LOCAMO data collection frequency is increased to improve tracking accuracy, then more detailed travel route information is obtained, but battery consumption and data processing load increase
Solution Approach 1:
The system employs periodic sampling of sensor data at optimized intervals rather than continuous monitoring. The sensor fusion algorithm processes data at strategically determined time points, maintaining adequate measurement precision for travel route identification while significantly reducing battery consumption and processing load compared to continuous high-frequency sampling
Solution Approach 2:
The system collects and processes only the minimum necessary sensor data required for accurate travel mode classification and route tracking. By selectively processing relevant sensor inputs rather than all available data streams, the system achieves sufficient measurement precision while minimizing energy consumption and computational overhead
Data Source
AI summary
A process for tracking a user's use of available modes of transportation during a trip between an origin and a destination, the process comprising: generating a plurality of prospective trip plan (PTP) segments, the PTP segments being arranged into sequences that provide PTPs from the origin to the destination, each PTP segment being characterized by a transportation mode; collecting a travel data set comprising at least one travel measurement for each of a plurality of intermediary timepoints between the user leaving the origin and arriving at the destination; and determining a most probable time series of PTP segments traveled by the user based on an emissions matrix defining a probabilistic relationship between a PTP segment and a travel measurement, a transitions matrix characterizing a likelihood of the user transitioning from one PTP segment of the plurality of PTP segments to another, and the travel data set.