Parking Violation Avoidance System Using Crowdsourced Rule Data
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Solution Overview
Problem
Complexity of urban parking regulations leads to confusion among drivers, resulting in frequent parking violations due to unclear signage and overlapping rules, making it difficult for them to comply with parking laws and avoid citations.
Innovation Solution
A system and method utilizing a central computing platform that stores and updates parking violation data, including historical and real-time information, to provide users with notifications on potential violations based on their location and vehicle type, using a database that clusters data by vehicle type and location, and incorporates user engagement panels for crowdsourced data and rewards for contributing accurate information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If parking rules and regulations are made comprehensive to cover all scenarios, then parking compliance improves, but the complexity of understanding and applying these rules increases
Solution Approach 1:
The patent introduces a mobile application as an intermediary between the complex parking regulations and the driver. The app receives comprehensive parking rules from the city, processes them into location-specific guidance, and presents simplified parking information to the driver through GPS-based notifications. This mediator system resolves the contradiction by making comprehensive rules accessible without increasing the driver's cognitive load.
Solution Approach 2:
The patent segments the comprehensive parking regulations into location-specific, time-specific, and vehicle-type-specific rules. Instead of presenting the entire rulebook at once, the system divides the rules into manageable chunks based on the driver's current location and situation, making the complex information digestible and actionable.
2Reliability
If real-time parking guidance is provided to drivers, then parking violations decrease, but the system complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing parking rule data in a database organized by location, time, and vehicle type. When a driver approaches a potential violation zone, the system has already prepared the relevant guidance information, allowing for rapid retrieval and notification without complex real-time calculations.
Solution Approach 2:
The system leverages the driver's own mobile device and GPS capabilities to provide personalized parking guidance. The driver's vehicle location, time, and vehicle type are used to automatically retrieve applicable parking rules, making the system adaptive without requiring complex centralized processing for each individual case.
3Measurement precision
If comprehensive parking data is collected and analyzed, then guidance accuracy improves, but the amount of data processing and storage required increases
Solution Approach 1:
The patent applies local quality by organizing parking data according to specific local characteristics - different rules for different locations, times, and vehicle types. The system stores and processes only the relevant portion of comprehensive data that applies to each specific situation, rather than processing all data uniformly, thereby maintaining accuracy while managing data volume.
Data Source
AI summary
A system and method for identifying a potential parking violation for a location includes storing and rating parking violation related data, and notifying a user, by a computing system communicating with a user's computing device, of the potential parking violation and how to avoid receiving the potential parking violation citation. The data is stored in a database and clustered by data types. The user's computing device is used to identify user type, location, and time. A user engagement panel is used to share and rate parking violation related data and notifications, and rewards are allocated to a user for contributing useful data. Highly rated data may be incorporated into the notifications. The data is analyzed to predict or infer a violation. A notification corresponding to a current location and user type are generated when violations are predicted.