Anonymized Parking Duration Calculation via Event Balancing
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Solution Overview
Problem
Service providers face challenges in determining parking durations from anonymized parking data due to the lack of association between park in and park out events, which affects the accuracy of parking-related services.
Innovation Solution
A method and system that filter anonymized parking data to remove events occurring outside a specified time interval and balance the number of park in and park out events, allowing for the calculation of parking duration data without explicit vehicle IDs or duration information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If parking data is anonymized by removing vehicle identifiers and not associating park in events with park out events, then vehicle privacy is preserved, but the ability to calculate accurate parking durations is lost
Solution Approach 1:
The patent segments the parking data processing into distinct filtering stages: first filtering out events outside the time interval, then balancing the number of park in and park out events. This segmentation allows the system to work with anonymized data while still deriving meaningful parking duration statistics through systematic data processing.
Solution Approach 2:
The patent introduces an intermediary processing layer that uses time interval analysis and event balancing as intermediate steps. Instead of directly calculating parking duration from raw anonymized data, the system uses these intermediate filtering and balancing operations to reconstruct meaningful parking patterns without requiring vehicle identifiers.
2Loss of information
If park in and park out events are not associated with each other, then data anonymity is maintained, but the distortion in parking duration data increases
Solution Approach 1:
The patent applies preliminary filtering actions before calculating parking duration. By first filtering out events outside the time interval and then balancing the number of park in and park out events, the system prepares the anonymized data in advance to minimize distortion and enable more reliable parking duration calculations.
Data Source
AI summary
An approach is provided for measuring parking duration from anonymized data. The approach involves receiving parking data indicating anonymized park in and park out events from connected vehicles. The approach also involves performing a first filtering of the parking data to remove the park out events that occur within a time interval and before a first park in event occurring within the time interval, and to remove the park in events that occur within the time interval and after a last park out event occurring within the time interval. The approach further involves performing a second filtering of the parking data remaining after the first filtering to remove park in events or park out events so that the numbers of park in and park out events are balanced. The approach then involves calculating parking duration data from the park in and park out events remaining after the second filtering.