Parking Availability Prediction Using Secondary Data
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Solution Overview
Problem
Current parking location applications do not effectively predict available parking spaces using secondary sources of data, leading to inefficiencies in finding suitable parking locations, which contributes to city traffic.
Innovation Solution
A system and method utilizing statistical and machine learning techniques to predict parking availability based on historical and real-time data inputs, including floating car data, parking transactions, and environmental factors, presented through web-based and in-vehicle applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current parking location applications merely depict the location of parking lots and garages, then the device complexity is reduced, but the measurement precision of parking availability prediction deteriorates
Solution Approach 1:
The patent introduces a server as an intermediary that performs complex statistical and machine learning computations. The server receives secondary data sources (floating car data, parking transactions, event data) and historical parking availability data, processes this information using regression models and machine learning algorithms, then provides prediction results to mobile devices. This mediator approach allows complex predictions to be made without burdening the mobile device with complex computational infrastructure.
Solution Approach 2:
The patent replaces physical parking sensors and infrastructure-based detection systems with a computational approach using secondary data sources. Instead of relying on mechanical sensors in parking spaces, the system uses statistical models that process floating car data, parking transaction records, and event data to predict availability. This substitution of mechanical sensing with information processing achieves prediction accuracy without requiring complex physical infrastructure.
2Reliability
If statistical and machine learning techniques are used to predict parking availability from secondary data sources, then the reliability of parking information is improved, but the loss of time in data processing increases
Solution Approach 1:
The system performs preliminary processing of secondary data sources and historical parking data to build trained regression models and machine learning algorithms in advance. These models are pre-trained on historical observations of secondary data and parking availability patterns. When a user requests parking availability predictions, the pre-trained models can quickly generate results without requiring complex real-time computations, thus reducing the time loss while maintaining high reliability.
Solution Approach 2:
The system incorporates real-time feedback by continuously updating predictions with current secondary data sources (floating car data, parking transactions, event data) and comparing predicted availability with actual user observations. This feedback loop refines the statistical models over time, improving reliability while optimizing processing efficiency through learned patterns from historical data.
3Adaptability or versatility
If real-time data inputs are incorporated into the model to adjust historical trends, then the adaptability of the prediction system is improved, but the device complexity increases
Solution Approach 1:
The patent implements a dynamic prediction model that automatically adjusts historical trends based on real-time data inputs. The system incorporates current floating car data, parking transaction data, and event data to modify predictions according to changing conditions. The model dynamically weights historical patterns against current observations, allowing it to adapt to varying parking demand patterns, weather changes, and local events without requiring manual reconfiguration or complex device architecture.
Data Source
AI summary
This application is directed to a system and method for locating parking spaces. A user can enter the desired location for parking and the application will transmit the information to a parking information database server. The server will return the location of parking spaces and probability of space availability. In addition, the database includes detailed parking information to include price, hours, and any special restrictions. These results can be depicted as a list or graphically displayed on a street map, satellite map or hybrid map views. Filters can be applied to find a specific type of parking available. The application provides real-time availability information in areas where the infrastructure to generate such data is available via sources such as on-street sensors or parking lot barrier systems. The system uses various secondary information to adjust historical observations of space availability in order to make provide accurate space predictions.