Parking Plan Proposal Device for Shared Lot Congestion
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Solution Overview
Problem
Existing parking lot congestion prediction technologies only notify users of congestion without providing actionable solutions, leading to user dissatisfaction.
Innovation Solution
A plan proposal device that predicts user actions, such as parking, in shared parking lots, generates proposal data based on prediction results and parking lot information, and outputs actionable plans to minimize congestion and improve user satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only congestion notification is provided to users, then system complexity is reduced, but user satisfaction deteriorates
Solution Approach 1:
The system performs preliminary actions by predicting future parking lot states and generating action plans before users need them. The prediction unit forecasts congestion states, and the plan generation unit creates optimized parking plans in advance, allowing users to take preventive actions rather than react to congestion notifications after the fact.
Solution Approach 2:
The system implements feedback by using actual user actions and parking lot state changes to refine future predictions. The prediction unit continuously learns from observed patterns, and the system adjusts its plan generation based on feedback from users' responses to proposed plans and actual parking lot conditions, improving overall user satisfaction iteratively.
2Ease of operation
If personalized action plans are generated for each user, then user satisfaction is improved, but device complexity increases
Solution Approach 1:
The system segments the complex task of plan generation into distinct functional units: a prediction unit that forecasts parking lot states, a plan generation unit that creates action plans based on predictions, and an output unit that delivers plans to users. This modular segmentation manages device complexity by distributing functions across specialized components rather than requiring a monolithic complex system.
Solution Approach 2:
The system enables self-service by automatically generating personalized action plans without requiring manual intervention from operators. The prediction and plan generation units autonomously analyze data, create optimized parking plans tailored to individual user patterns and preferences, and deliver them directly to users, reducing the need for complex manual planning systems.
3Measurement precision
If prediction accuracy is increased through detailed analysis, then plan quality is improved, but processing time increases
Solution Approach 1:
The system performs preliminary analysis by continuously learning user patterns and parking lot behaviors in advance through the prediction unit. By pre-processing data and establishing baseline predictions, the system reduces the computational burden during actual plan generation, maintaining high accuracy while minimizing real-time processing delays.
Data Source
AI summary
A plan proposal device includes a controller configured to predict, as a user action, an action of a first user in a certain period, including parking a vehicle in a parking lot shared by a plurality of users including the first user, generate proposal data for proposing an action plan in the certain period based on an obtained prediction result and information on the parking lot in the certain period, and output the generated proposal data.