Deep-Learning Parking Permit Pricing For Variable Demand
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing parking lot pricing systems use flat-rate fees, failing to consider varying demand and supply, leading to inefficiencies and revenue loss for operators and inconvenience for users.
Innovation Solution
A dynamic pricing system using a deep learning algorithm that adjusts parking permit prices based on real-time data, including occupancy rates and time periods, through a Markov Decision Process (MDP) algorithm to optimize resource utilization and revenue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If flat-rate pricing is used, then the pricing system is simple and easy to operate, but it fails to consider varying demand and supply conditions, leading to revenue loss and inefficiency
Solution Approach 1:
The patent implements dynamic pricing that automatically adjusts parking permit prices based on real-time factors including occupancy rate, time period, and weather conditions. The system transitions from static flat-rate pricing to a dynamic model where prices fluctuate according to demand and supply conditions, enabling the system to adapt to changing environmental factors and maximize revenue while maintaining operational simplicity through automated decision-making
Solution Approach 2:
The patent changes the pricing parameters from a fixed flat rate to variable prices influenced by multiple factors such as occupancy rate thresholds, time-based pricing tiers, and weather condition adjustments. The system modifies price parameters dynamically based on measured conditions, allowing for optimized revenue generation while responding to real-time changes in parking demand and supply
2Device complexity
If flat-rate pricing is used, then the system complexity is low, but it cannot maximize operator revenue due to lack of responsiveness to demand and supply variations
Solution Approach 1:
The patent incorporates feedback mechanisms where the system continuously monitors occupancy rates, time periods, and weather conditions, then uses this information to adjust pricing decisions. The feedback loop enables the system to learn from actual parking demand patterns and optimize revenue by responding to real-time conditions, transforming the pricing system from a static model to an adaptive one that maximizes operator revenue
Solution Approach 2:
The system performs self-service by automatically determining optimal pricing decisions based on pre-established algorithms and real-time data analysis. The pricing system independently processes occupancy information, time-based rules, and weather conditions to generate pricing recommendations without requiring manual intervention, thereby managing increased complexity through automation while achieving revenue maximization
3Productivity
If dynamic pricing based on multiple factors is implemented, then revenue and resource efficiency are maximized, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the pricing determination process into distinct modules: occupancy rate assessment, time period classification, weather condition evaluation, and integrated pricing calculation. Each factor is processed separately through dedicated logic blocks, then combined to generate the final pricing decision. This segmentation reduces overall system complexity by breaking down the complex pricing algorithm into manageable, independent components that can be implemented and maintained more easily
Data Source
AI summary
The present invention provides a dynamic pricing system that determines a price of a parking permit on the basis of deep learning. The present invention is a technology developed through the Development of Artificial Intelligence Dynamic Pricing Solution for Smart Mobility Services, a project CY230022 funded by the Seoul Business Agency (2023 Artificial Intelligence Technology Commercialization Support Project). A data collection unit may collect data to determine the price of the parking permit, a pricing unit may determine the price of the parking permit from the data using a Markov Decision Process (MDP) algorithm, a memory may store instructions to operate the data collection unit and the pricing unit, and a processor may execute the instructions stored in the memory to operate the data collection unit and the pricing unit.