Context-Aware Parking Assignment for Smarter Lot Allocation
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Solution Overview
Problem
Current building management systems (BMS) lack the ability to dynamically assign parking spaces based on individual context information, such as schedules and physical characteristics, leading to inefficient parking lot management and user experience.
Innovation Solution
Implement a smart parking system that uses sensors and machine learning to detect vehicles and identify individuals, retrieve context information, and dynamically assign parking spaces based on factors like schedule, physical location, and organizational role, while also providing directions and notifications to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional building management systems are used for parking lot management, then the system structure is simple, but the parking space allocation efficiency is low and user experience is poor
Solution Approach 1:
The system segments the parking lot into multiple zones with different characteristics (e.g., disabled zones, visitor zones, employee zones) and assigns specific parking spaces to individuals based on their context information. This segmentation enables efficient allocation while maintaining manageable system complexity through modular zone management.
Solution Approach 2:
The parking space assignment system dynamically adjusts parking space allocation based on real-time context information such as user schedules, organizational roles, and physical characteristics. The system continuously updates assignments rather than using static pre-allocated spaces, improving efficiency while the dynamic nature is managed through automated processing that prevents complexity from overwhelming the system.
2Ease of operation
If dynamic parking space assignment based on context information is implemented, then user experience is improved, but data processing requirements increase
Solution Approach 1:
The system retrieves and processes context information (schedules, organizational roles, physical characteristics) in advance before parking space assignment is needed. This preliminary data preparation reduces real-time processing requirements and enables fast, personalized assignments that improve user experience without overwhelming the system during peak parking demand.
Solution Approach 2:
The system automatically processes context information and makes parking space assignments without requiring manual intervention or extensive data processing requests from users. The automated retrieval and analysis of context information reduces the data processing burden on the system while delivering personalized assignments that enhance user experience.
3Productivity
If parking spaces are assigned based on multiple context factors, then parking space optimization is improved, but system complexity increases
Solution Approach 1:
The system applies different assignment criteria and weights to different user contexts and parking zones. For example, disabled users receive priority in accessible zones, while visitors are assigned to visitor zones based on their schedules. This localized quality approach optimizes parking space utilization across different areas without requiring a single complex algorithm to handle all scenarios uniformly.
Solution Approach 2:
The system adjusts assignment parameters such as priority levels, zone restrictions, and time-based availability based on user context information like organizational role and schedule. By dynamically changing these parameters rather than using a fixed complex algorithm, the system achieves optimized parking space allocation while keeping the underlying system architecture manageable.
Data Source
AI summary
One or more non-transitory computer-readable storage media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to detect a vehicle that enters into a parking lot, identify an individual associated with the vehicle, retrieve context information corresponding to the individual, dynamically determine a first parking space based on the context information and available parking spaces, and provide the individual with directions to the first parking space.