Parking Occupancy Prediction via Economic Activity Codes
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Solution Overview
Problem
Current navigation systems fail to accurately account for parking availability, leading to inefficient routing and increased time spent searching for parking spots, as they rely on incomplete and unreliable point of interest data that does not consider the economic activity and varied needs of different businesses and locations.
Innovation Solution
A method and apparatus that use economic activity codes and machine learning to predict parking occupancy by analyzing historical data and relationships between economic entities and parking usage within a predefined distance of a parking area, providing more accurate and reliable predictions for navigation systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If navigation systems use traditional point of interest data to determine destination, then routing calculation is simple and fast, but parking availability prediction is inaccurate and unreliable
Solution Approach 1:
The system segments the problem of parking availability prediction by dividing it into multiple components: identifying economic entities around parking areas, collecting their activity codes, analyzing historical occupancy data, and generating predictions. This segmentation allows each component to be processed independently, improving prediction accuracy while managing system complexity through modular data processing stages.
Solution Approach 2:
The system transitions from traditional two-dimensional map data to a multi-dimensional analysis by incorporating economic activity codes, historical occupancy patterns, and temporal factors. This dimensional expansion enables more accurate parking availability predictions by considering multiple variables simultaneously, transforming the prediction from a simple binary state to a probabilistic assessment based on multiple dimensions of data.
2Productivity
If navigation systems calculate efficient routing without parking consideration, then route calculation is fast and simple, but drivers spend additional time cruising for parking spots
Solution Approach 1:
The system performs preliminary action by predicting parking availability before the driver arrives at the destination. By analyzing historical data and economic activity codes in advance, the system identifies parking areas likely to have available spots during the driver's arrival time, allowing the navigation system to pre-calculate routes to suitable parking locations rather than reacting after arrival.
Solution Approach 2:
The system implements feedback by continuously monitoring actual parking occupancy and comparing it with predictions. This feedback loop refines the prediction model over time, improving accuracy. The system uses historical occupancy data and economic activity information to continuously adjust and improve parking availability predictions, making the routing increasingly efficient with each iteration.
3Loss of time
If navigation systems consider parking availability in routing decisions, then drivers reach destination faster, but requires complex prediction models and historical data analysis
Solution Approach 1:
The system uses copying by creating simplified representations of complex parking patterns through economic activity codes. Instead of modeling every individual parking spot and its occupancy, the system copies the essential characteristics of parking demand based on economic entity types and their activity patterns. This abstraction allows complex parking behavior to be represented through manageable code categories that can be processed efficiently.
Solution Approach 2:
The system applies parameter changes by transforming raw historical occupancy data into predicted probability values that can be directly integrated into routing decisions. By changing the parameters from detailed historical records to aggregated prediction metrics based on economic activity codes, the system reduces data complexity while maintaining the essential information needed for accurate parking availability assessment.
Data Source
AI summary
System and methods are provided for predicting occupancy of a parking area. A request is received for data relating to occupancy of the parking area. One or more entities within a first predefined distance of the first parking area are identified. Activity classification codes for each of the one or more entities are identified. A predicted occupancy for the parking area is calculated as a function of the activity classification codes. The predicted occupancy for the parking area is transmitted.