Personalized Parking Spot Locator Using Reinforcement Learning
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Solution Overview
Problem
Users face difficulties in finding suitable parking spots that meet their preferences, such as proximity to destinations and willingness to pay for convenience, due to the lack of personalized recommendations in existing systems.
Innovation Solution
A reinforcement learning model is implemented to personalize parking spot recommendations based on user preferences and historical data, using a distributed data processing environment that includes IoT platforms and sensors to determine availability and satisfaction ratings, thereby creating a ranking prioritization mechanism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If personalized recommendations are implemented using reinforcement learning, then user satisfaction and parking spot matching quality improve, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the parking recommendation problem into distinct components: state representation (user preferences, destination, time), action space (parking spot selection), and reward function (satisfaction rating). This segmentation allows the reinforcement learning model to process each component separately, managing complexity while maintaining recommendation accuracy.
Solution Approach 2:
The patent introduces an intermediary reinforcement learning agent that mediates between user preferences and parking spot options. This agent learns optimal parking recommendations through continuous interaction and feedback, acting as a bridge that translates complex user requirements into suitable parking spot selections without requiring direct complex processing of all possible combinations.
2Loss of information
If real-time parking spot availability monitoring is implemented using IoT sensors, then information accuracy improves, but infrastructure cost and system complexity increase
Solution Approach 1:
The IoT sensors deployed in the system serve multiple functions: detecting parking spot occupancy status, monitoring vehicle presence, and providing real-time availability updates. This multi-functionality reduces the need for separate specialized sensors for each function, thereby lowering overall infrastructure complexity and cost while maintaining high information accuracy.
Solution Approach 2:
The parking spots are equipped with sensors that automatically detect and report their own occupancy status without requiring manual intervention. This self-service capability enables real-time monitoring and accurate information delivery about parking availability, eliminating the need for human operators to manually track and update parking status.
3Measurement precision
If continuous feedback collection through satisfaction ratings is implemented, then model personalization and predictive accuracy improve, but data processing requirements and computational load increase
Solution Approach 1:
The system performs preliminary processing of satisfaction rating data by aggregating and summarizing user feedback in real-time, rather than processing every individual rating separately. This preliminary aggregation reduces the computational load for training the reinforcement learning model while maintaining the precision of user preference measurements through cumulative learning.
Solution Approach 2:
The reinforcement learning model uses partial updates based on incoming satisfaction ratings rather than reprocessing the entire dataset each time. This partial update approach allows the model to continuously improve personalization accuracy with new feedback while significantly reducing computational energy consumption compared to full retraining.
Data Source
AI summary
In an approach to creating and using a reinforcement learning model for personalizing a recommendation of a parking spot, one or more computer processors receive a first destination associated with the vehicle. One or more computer processors determine a parking spot availability in proximity to the destination. One or more computer processors determine a recommended parking spot location. One or more computer processors display the recommended parking spot location to the user. One or more computer processors determine a first parking spot selection. One or more computer processors receive a first satisfaction rating associated with the recommended parking spot location.