Route Prediction Using Wireless Network Association
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Solution Overview
Problem
Transportation agencies face challenges in providing real-time and relevant information to riders on predetermined routes, such as bus schedules, as existing systems rely on manual checks or limited online updates, lacking accuracy and timeliness.
Innovation Solution
A system that uses wireless networks to predict the route of a mobile device by associating Wi-Fi access points with transportation vehicles, confirming the device's location through sensor data, and sending relevant content, such as arrival times at future stops, based on the predicted route.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual checks or limited online updates are used for schedule information, then system complexity is reduced, but information accuracy and timeliness deteriorate
Solution Approach 1:
The system enables self-service by allowing mobile devices to automatically scan for wireless networks, retrieve route information, and receive updates without manual intervention. The mobile device autonomously queries the server using scanned network data and receives push notifications, eliminating the need for manual schedule checks while maintaining high information accuracy.
Solution Approach 2:
The system performs preliminary action by pre-establishing associations between wireless networks and transportation routes in a database before runtime. When a mobile device scans for networks, the server already has the routing information ready to retrieve and send, enabling rapid response without complex real-time processing.
2Measurement precision
If wireless network scanning and sensor data analysis are implemented, then route prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The server acts as an intermediary that handles the complex task of analyzing sensor data and determining route predictions. The mobile device simply collects sensor data and transmits it to the server, which performs the computationally intensive analysis and returns results, thereby improving accuracy without significantly increasing device complexity.
Solution Approach 2:
The system implements feedback by continuously monitoring wireless network signals and sensor data, comparing predicted routes with actual device movement, and adjusting predictions accordingly. This feedback loop improves route prediction accuracy over time while maintaining manageable system complexity through iterative refinement rather than complex algorithms.
3Loss of information
If real-time content delivery is implemented, then user experience is improved, but information loss is reduced
Solution Approach 1:
The server performs preliminary action by pre-fetching and caching content related to predicted routes before the user actually needs it. When the system predicts a user will take a certain route based on wireless network scanning and sensor data, relevant content is prepared in advance and delivered just in time, ensuring information timeliness without requiring complex real-time processing.
Solution Approach 2:
The system dynamically adjusts content delivery based on real-time device location and predicted route. Content is delivered only when relevant to the user's current or predicted location, avoiding unnecessary transmissions and improving delivery efficiency while maintaining information timeliness through adaptive, context-aware delivery.
Data Source
AI summary
The disclosed implementations provide a system and method of predicting routes for mobile devices using wireless networks, including generating and sending content to a mobile device that is travelling on a predetermined route (e.g., a bus route determined by a transportation agency). The mobile device can scan for a wireless network that is installed on a vehicle travelling on a predetermined route. The system can predict which predetermined route the mobile device is travelling on by accessing a database that associates wireless networks with transportation vehicles. The system can confirm whether the mobile device is travelling on a predetermined route based on the device's sensor measurements, timestamps collected over a period of time and the identity of the wireless network that is connected to the device. The system can send content to the mobile device based on the mobile device's location and predicted future locations along the predetermined route.