Parking Space Availability Management via Dynamic Time Bins
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Solution Overview
Problem
Parking facilities face challenges in determining which spaces to make available for rent due to high variability in occupancy levels, especially during off-peak hours when regular tenants are not using the spaces, leading to underutilization and missed revenue opportunities.
Innovation Solution
A method and system that determine time bins based on occupancy patterns, calculate the optimal number of reserved spaces to rent by analyzing unoccupied spaces, and apply restrictions to ensure regular tenants' access, using data from sensors, traffic, and infrastructure to offer spaces to non-tenants strategically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If parking facilities make more spaces available for rent during low occupancy periods, then revenue is maximized, but regular tenants' access may be disrupted
Solution Approach 1:
The system dynamically adjusts the number of spaces available for rent based on real-time occupancy data and historical patterns. Time bins are created to represent different occupancy periods, and the system determines optimal rentable spaces for each bin, allowing flexible adaptation to varying demand while preserving tenant access during peak periods.
Solution Approach 2:
The system continuously monitors occupancy data from sensors and updates its decisions in real-time. This feedback mechanism allows the system to learn from actual usage patterns and adjust space allocation accordingly, ensuring that revenue opportunities are captured without compromising regular tenants' needs.
2Productivity
If parking facilities convert private spaces to shared use, then operational efficiency is enhanced, but occupancy variability becomes more challenging to manage
Solution Approach 1:
The parking facility is segmented into different time bins based on occupancy patterns. Each time bin represents a specific period with characteristic occupancy levels, allowing the system to manage variability by making decisions tailored to each segment rather than applying a uniform approach throughout the day.
Solution Approach 2:
The system changes the parameter of space availability based on the current time bin and occupancy conditions. By adjusting the number of rentable spaces according to predetermined thresholds and actual occupancy data, the system adapts to variability while maintaining operational efficiency.
3Productivity
If the system offers more spaces for rent, then revenue increases, but the risk of insufficient spaces for regular tenants increases
Solution Approach 1:
The system applies partial action by offering only a determined number of spaces for rent during each time bin, rather than making all available spaces rentable. This partial utilization of available capacity ensures that sufficient spaces are reserved for regular tenants while still capturing revenue opportunities from underutilized capacity.
Data Source
AI summary
Systems and methods are provided for determining parking spaces to rent to the public. The systems and methods can determine a plurality of time bins and receive data on the occupancy of parking spaces for each time bin. A determined number of reserved spaces can be calculated for each time bin. Restrictions associated with the parking space can also be determined. A final number of parking spaces to rent can be determined based on the determined number of reserved spaces and the restrictions.