Mobile Alert Network for Location-Specific Traffic Alerts
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Solution Overview
Problem
Current traffic information systems are unreliable and inefficient, often providing outdated or irrelevant information to drivers, requiring manual interaction, and are not tailored to individual locations, leading to unnecessary distractions and costs.
Innovation Solution
A passive traffic alerting method that identifies traffic events through data analysis and sends location-specific alerts to mobile communicators without requiring them to launch an application, using a Mobile Alert Network (MAN) service that integrates data from multiple sources and sends alerts via SMS or other formats based on user zones and events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traffic information is broadcast through traditional channels (news channels, police reports), then coverage area is large, but information reliability deteriorates due to outdated or incorrectly interpreted data
Solution Approach 1:
The patent introduces mobile devices as intermediary sensors that collect real-time traffic data directly from vehicles, bypassing traditional unreliable channels. These mobile sensors act as mediators between the traffic system and central processing, providing firsthand data that is both timely and reliable.
Solution Approach 2:
The system implements continuous feedback loops where mobile sensors report traffic conditions in real-time, the central system processes this data, and updates are immediately pushed back to relevant users. This closed-loop feedback ensures information remains current and actionable.
2Adaptability or versatility
If personalized traffic information services are provided, then information relevance improves, but device complexity increases requiring manual application launching
Solution Approach 1:
The system enables self-service by automatically detecting user location, identifying relevant traffic events, and delivering personalized alerts without requiring users to manually launch applications or input preferences. The service adapts to user needs autonomously based on contextual data.
Solution Approach 2:
The system performs preliminary actions by pre-configuring alert preferences, pre-identifying relevant traffic events in the user's path, and pre-positioning information for immediate delivery. This eliminates the need for manual intervention during critical moments.
3Quantity of substance
If comprehensive traffic reports are broadcast to all users, then information completeness improves, but information relevance deteriorates for individual users
Solution Approach 1:
The patent applies local quality by filtering and delivering traffic information specific to each user's location, route, and preferences. Instead of uniform comprehensive reports, the system tailors information density and type to local user needs, ensuring high relevance without sacrificing overall system completeness.
4Productivity
If manual application launching is required for traffic services, then service functionality improves, but ease of operation deteriorates due to driver distraction risks
Solution Approach 1:
The system performs all service functions autonomously without requiring driver interaction. Mobile sensors automatically collect data, the system processes information, and alerts are delivered passively, maintaining full functionality while eliminating the need for manual application launching during driving.
Data Source
AI summary
Systems and methods for sending messages to a mobile device based on a location determined by a receive-only sensor network are described. A method can include receiving a signal from a mobile device, detecting contextual parameters associated with the mobile device, preparing a message for the mobile device, and transmitting the message to the mobile device. The signal from the mobile device can be received on a receive-only sensor array. The contextual parameters can include at least a location associated with the mobile device. The message can be prepared based on the contextual parameters.