Mobile Parking Beacon Data Processing for Lane Location Accuracy
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Solution Overview
Problem
The inefficiency of existing systems in providing real-time, accurate information on parking availability and pricing due to the offline nature of parking rate systems and lack of real-time connection to parking company operational systems, leading to outdated and inaccurate data, especially in the Internet-of-Things era.
Innovation Solution
A system utilizing machine-readable instructions and hardware processors to process user mobile parking beacon data with machine learning, outputting the most probable lane location vector, and electronically transmitting commands to open movable barriers, integrating with a cloud-based automated system for dynamic vehicle spot assignments and real-time information management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time machine learning processing of mobile parking beacon data is implemented, then measurement precision of lane location is improved, but use of energy and computational resources increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing parking beacon data in a database before actual location determination is needed. The machine learning model is trained in advance on historical beacon data, so that during runtime, only inference needs to be performed rather than full training, significantly reducing real-time computational energy consumption while maintaining high location accuracy.
2Measurement precision
If deep learning processors are used to process parking beacon data, then measurement precision of vehicle location is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary layer between the mobile device and the barrier processing system. A server acts as the intermediary that performs the complex deep learning computations, while mobile devices only need to communicate beacon data and receive location results. This intermediary approach maintains high measurement precision through sophisticated processing while keeping individual device complexity low.
3Productivity
If real-time data processing and transmission is implemented, then productivity of parking transaction processing is improved, but loss of energy and data transmission overhead increases
Solution Approach 1:
The system extracts only the essential data elements needed for processing - specifically parking beacon identifiers and basic vehicle attributes - and transmits only this extracted information to the server. The heavy lifting of data processing occurs on the server side, allowing mobile devices to maintain high productivity in transaction processing while minimizing energy consumption for data transmission by sending only critical extracted data rather than complete datasets.
Data Source
AI summary
Methods, systems, and computing platforms for mobile data communication are disclosed. The processor(s) may be configured to receive a plurality of user mobile parking beacon data and storing the user mobile parking beacon data in a computer readable database for a mobile device. The processor(s) may be configured to process the user mobile parking beacon data with a machine learning processor as to output at least one most probable lane location vector associated with the mobile device. The processor(s) may be configured to electronically output the at least one most probable lane location vector to a parking processing module.