Offline Fare Card Authorization via Local Risk Analysis
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Solution Overview
Problem
Fare collection systems face challenges in allowing or denying access to transit services when offline, as they lack real-time connectivity with central authorization systems, leading to inefficiencies and potential misuse of services.
Innovation Solution
A local card analysis system that uses locally stored data, historical information, and contextual factors to dynamically determine whether to grant access to transit services, employing rules and analytics to assess the risk of allowing or denying transactions when disconnected from the central system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system operates offline without central system connectivity, then service availability is improved, but authorization reliability deteriorates
Solution Approach 1:
The system performs preliminary actions by storing historical data, rules, and contextual information locally before offline operation begins. This includes caching card data, pre-loading authorization rules, and maintaining local databases that enable autonomous decision-making when disconnected from the central system.
Solution Approach 2:
The edge device acts as an intermediary between the fare card and the central system during offline operations. It locally processes authorization requests using stored data and rules, making autonomous decisions without direct central system involvement, then synchronizes results when connectivity is restored.
2Measurement precision
If the system uses centralized authorization, then authorization accuracy is improved, but system complexity increases
Solution Approach 1:
The authorization system is segmented into centralized and decentralized components. The central system handles complex authorization logic and data management, while edge devices handle local processing and decision-making. This segmentation allows each component to specialize, maintaining accuracy while reducing overall system complexity.
Solution Approach 2:
The edge device performs self-service authorization processing by using its own locally stored data, rules, and analytics capabilities to make authorization decisions independently when offline, reducing reliance on the central system and simplifying the operational architecture.
3Loss of information
If the system continuously synchronizes with the central system, then data accuracy is improved, but network dependency increases
Solution Approach 1:
The system dynamically adjusts its operation mode between online and offline states. When connected, it synchronizes with the central system to maintain data accuracy. When disconnected, it transitions to autonomous offline mode using local data, adapting to network conditions in real-time to balance data accuracy with network independence.
Solution Approach 2:
The system prepares for potential network disruptions by maintaining local copies of critical data, rules, and historical information. This cushioning approach ensures that the system can continue operating with acceptable data accuracy even when network connectivity is lost, reducing vulnerability to network failures.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating rules to apply to fare transactions when a fare card reader cannot communicate with an authorization server, for generating historical data and a risk score for a fare card that are used by a fare card reader, with the rules, to determine whether to authorize access to a transit service when the fare card reader cannot communicate with the authorization server, and for selectively permitting or denying access to the transit service using the rules and the historical data or the score for a fare card.