Semi-Automatic User Profiling for Off-Street Parking
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Solution Overview
Problem
Current parking management systems lack the ability to automatically identify and interpret latent user profiles, relying on manual or static definitions, which limits their flexibility and effectiveness in offering targeted services to users.
Innovation Solution
Implement a semi-automatic user profiling system that uses clustering algorithms to discover and tag user profiles based on spatio-temporal behavioral patterns, allowing for the creation, exploration, and visualization of user profiles, and providing a framework for data processing and visualization in off-street parking applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual or static definition of user profiles is used, then the system is simple to operate, but the system lacks flexibility and cannot automatically discover hidden user profiles
Solution Approach 1:
The system enables automatic user profile discovery through self-service mechanisms where the clustering algorithm autonomously identifies and creates user profiles from transaction data without requiring manual intervention. The system automatically tags discovered profiles with meaningful labels, eliminating the need for operators to manually define and categorize every user profile.
Solution Approach 2:
The patent replaces the manual mechanical process of defining user profiles with an automated computational system. Clustering algorithms process transaction data to automatically discover user profiles, substituting human operators' manual work with algorithmic processing that can handle large datasets and identify complex patterns.
2Extent of automation
If clustering algorithms are used to discover user profiles, then automatic pattern discovery is achieved, but the discovered profiles are difficult to interpret
Solution Approach 1:
The system introduces an intermediary tagging mechanism that bridges the gap between automated clustering algorithms and human interpreters. Meaningful tags are assigned to discovered user profiles, serving as intermediaries that translate complex algorithmic outputs into human-understandable categories that preserve interpretability while maintaining automation benefits.
Solution Approach 2:
The system enhances profile interpretability by assigning meaningful labels and tags to clustered user profiles, making the discovered patterns visible and understandable to operators. This labeling process transforms abstract clustering results into interpretable information that can be easily communicated and acted upon.
3Measurement precision
If data surveys are used to develop user profiles, then comprehensive user behavior data is collected, but the process is costly and difficult to perform in adequate and timely fashion
Solution Approach 1:
Instead of conducting costly and time-consuming data surveys, the system uses existing transaction data from parking installations as a copy or proxy for comprehensive user behavior information. The clustering algorithms process this readily available data to infer user profiles, eliminating the need for separate data collection efforts while maintaining analytical depth.
Data Source
AI summary
Methods and systems for interpretable user behavior profiling in off-street parking applications. To render user profiles easy to interpret by decision makers, the semi-automatic discovery and tagging of user profiles can be implemented. Transaction data from one or more (and geographically close) off-street parking installations can be implemented. An analysis of spatio-temporal behavioral patterns can be implemented based on representation of any parking episode by a set of heterogeneous features, the use of clustering methods for automatic pattern discovery, an assessment of obtained clusters, semi-automatic identification/tagging of space-temporal patterns, and a user-friendly interpretation of obtained patterns.