Portable Notification Apparatus Using Motion Data for Bus Stop Prediction
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Solution Overview
Problem
Users of buses often face difficulties in receiving clear notifications for getting off stops due to the diverse routes and ground-based operations, which differ from subway systems, leading to repeated settings for notification.
Innovation Solution
A portable apparatus that connects to a wireless network to determine vehicle boarding based on motion state data and vehicle information, utilizing a learning model to predict the getting off stop and provide notifications without repeated user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a simple preset notification method is used, then the device complexity is reduced, but the ease of operation deteriorates due to repeated settings required
Solution Approach 1:
The system automatically detects vehicle boarding through motion sensors and wireless network connection, self-learns getting off stops from historical data, and provides notifications without user intervention. This eliminates repeated manual settings while maintaining simple device architecture.
Solution Approach 2:
The system performs preliminary detection of vehicle boarding status and preliminary learning of getting off stops before notification is needed. By anticipating user needs and preparing notification data in advance based on learned patterns, the system avoids requiring users to repeatedly configure settings.
2Reliability
If clear notification is provided like subway systems, then the reliability of notification is improved, but the adaptability deteriorates due to diverse bus routes and types
Solution Approach 1:
The system dynamically adapts notification parameters based on detected vehicle type, route, and learned user preferences. Rather than using fixed notification methods, the system adjusts notification timing, target stops, and delivery methods to match the specific bus route and user behavior patterns, ensuring reliable notification across diverse routes.
Solution Approach 2:
The system incorporates feedback loops where user responses to notifications and actual getting off behavior are continuously monitored and used to refine future notifications. This learning mechanism allows the system to adapt to diverse bus routes while maintaining high notification reliability through iterative improvement based on actual usage patterns.
3Measurement precision
If manual presetting of stops is required, then the measurement precision of getting off stop is improved, but the loss of time increases due to repeated configuration
Solution Approach 1:
The system performs preliminary detection of vehicle boarding and preliminary identification of getting off stops based on motion patterns and wireless network data before the user needs notification. This advance preparation eliminates the need for manual configuration while maintaining accurate stop identification through sensor-based detection and historical learning.
Solution Approach 2:
The system replaces manual mechanical configuration (user pressing buttons to preset stops) with automated sensor-based detection using motion sensors, accelerometers, and wireless network connection detection. This substitution maintains precise getting off stop identification while eliminating the time required for manual configuration.
Data Source
AI summary
The present disclosure discloses a portable apparatus which communicates with a vehicle through a wireless network, obtains vehicle information as the wireless network is connected, determines whether a user boards the vehicle based on motion state data of the user and the vehicle information, determines a stop to provide notification based on whether the user boards the vehicle and a getting-off history, and outputs getting-off notification.