Parking Simulation Using ML User Profiles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current parking management systems lack the ability to accurately simulate user behavior and optimize off-street parking revenue, as they rely on historical data and average duration times, failing to account for user profiles and dynamic pricing effectively.
Innovation Solution
A simulation framework that uses machine learning to model user arrival and choice, dividing users into profiles learned automatically from historical data, and employs a micro-simulation to replicate real-world behavior, incorporating features like occupancy and pricing options to predict user decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If average duration time is used for all vehicles, then device complexity is reduced, but measurement precision of user behavior deteriorates
Solution Approach 1:
The patent segments users into multiple profiles based on their behavior characteristics, with each profile having its own duration time distribution parameters. This segmentation allows the system to capture heterogeneous user behaviors without requiring a single complex model for all users, thus maintaining reasonable complexity while improving measurement precision.
Solution Approach 2:
The patent changes the parameters from a single average duration time for all vehicles to multiple sets of duration time parameters (mean and standard deviation) for each user profile. This parameter change enables more precise measurement of user behavior by accounting for the variability among different user types.
2Ease of operation
If manually-crafted algorithms are used to determine user choices, then ease of operation is improved, but adaptability to different user behaviors deteriorates
Solution Approach 1:
The patent employs unsupervised learning algorithms that automatically learn user profiles and choice behaviors from historical data without manual intervention. The system self-adjusts to different user behaviors by identifying patterns in the data, eliminating the need for manual crafting of choice algorithms while maintaining ease of operation through automated processes.
Solution Approach 2:
The patent replaces manual mechanical algorithms with data-driven machine learning models. Instead of manually crafting decision rules, the system uses supervised learning algorithms to automatically learn choice probabilities from historical data, substituting mechanical rule-based systems with adaptive statistical models.
3Device complexity
If fixed user groups are defined a priori, then device complexity is reduced, but measurement precision of user behavior deteriorates
Solution Approach 1:
The system automatically learns and defines user profiles from historical data using unsupervised learning algorithms, eliminating the need for manual a priori classification. This self-service approach allows the system to discover actual user behavior patterns in the data, improving measurement precision while keeping the process automated and relatively simple.
Solution Approach 2:
The patent performs preliminary clustering of users into profiles based on historical behavior data before simulating choices. This preliminary action of automatically grouping similar users enables the subsequent choice modeling to be more accurate, as each profile can be assigned appropriate duration time parameters learned from similar users.
4Ease of manufacture
If regression models with manually specified formulas are used, then ease of manufacture is improved, but adaptability to real-world behavior deteriorates
Solution Approach 1:
The patent replaces manually specified regression formulas with machine learning models that automatically learn from data. Instead of requiring manual specification of parametric formulas, the system uses supervised learning algorithms to discover the relationships between user characteristics and choices, greatly improving adaptability to real-world behavior while maintaining ease of deployment through automated model training.
Solution Approach 2:
The patent changes from fixed manually-specified parametric formulas to data-driven parameters learned from historical choices. The model learns the actual relationships and parameters from real user behavior data, making it adaptable to various parking scenarios without requiring manual re-specification of formulas for different contexts.
Data Source
AI summary
Methods and systems for modeling user arrival and choice in the context of off-street parking solutions. A first component models the arrival and duration of stay of users as a function of time, taking into account different user profiles (or “clusters”), captured by a latent variable. A second component provides a ranking function (for each user cluster), wherein the input features describing the “choice” constitute status variables associated different car park(s), and the output constitutes a preferred car park and a pricing scheme. The system simulates different user behaviors by assuming some standard groups of users will behave similarly. Groups of users or user profiles are learned automatically. The profiles are then employed as a key element for automatically learning a decision function of parking users, and automatically learning one decision function per profile.