IoT Parking Recommendation System for Smart Cities
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Solution Overview
Problem
In smart cities, users face difficulties in finding parking spaces due to increased vehicle demand, leading to low parking efficiency and varying preferences for different parking lots, necessitating a method to recommend suitable parking lots based on specific conditions.
Innovation Solution
An Internet of Things (IoT) system with a management platform determines the arrival duration, prediction time period, and occupation rate of candidate parking lots, recommending the best lot based on these factors and parking lot information to improve user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users search for parking lots manually during peak periods, then they can find parking spaces, but the time required increases and parking efficiency decreases
Solution Approach 1:
The system performs preliminary actions by predicting future occupation rates of parking lots before users arrive. The management platform calculates prediction time periods and occupation rates in advance, allowing users to receive recommendations before reaching the parking area, thus reducing search time and improving parking efficiency during peak periods
Solution Approach 2:
The system implements feedback by continuously monitoring actual parking occupation rates and comparing them with predicted rates. The management platform uses real-time data from parking sensors to update occupation rates and refine predictions, creating a closed-loop system that improves recommendation accuracy over time and helps users find parking faster
2Measurement precision
If the system considers multiple factors for parking recommendation, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex recommendation problem into distinct computational components: calculating arrival duration separately, determining prediction time periods separately, computing occupation rates separately, and then integrating these results for final recommendations. The management platform divides the parking area into multiple zones with independent sensors, allowing parallel processing and reducing overall system complexity while maintaining high recommendation accuracy
Data Source
AI summary
The embodiments of the present disclosure provide a method for recommending a parking lot in a smart city and an Internet of Things system, implemented based on a management platform of an Internet of Things system for recommending a parking lot in a smart city. The method includes: determining an arrival duration of a user reaching at least one candidate parking lot and a prediction time period corresponding to each candidate parking lot in the at least one candidate parking lot based on a user request; determining an occupation rate of the candidate parking lot during the prediction time period; and determining a recommended parking lot at least based on the occupation rate of the candidate parking lot during the prediction time period and parking lot information of the candidate parking lot.