Fare Collection System Using CAPM Risk Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Public transit networks face challenges in effectively managing fare collection systems to optimize fare pricing and revenue generation, lacking insights into the riskiness and volatility of fare classes.
Innovation Solution
A system that includes a fare collection system and a processing device applying a Capital Asset Pricing Model (CAPM) to assess the riskiness of fare classes, generating a report with return versus volatility charts, allowing for informed decisions on fare pricing and route management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional fare collection systems are used without risk assessment, then fare management is simple, but revenue optimization and network growth potential are not realized
Solution Approach 1:
The system performs preliminary risk assessment and volatility analysis on fare classes before making management decisions. By calculating risk metrics, beta values, and expected returns in advance using historical fare data, the system enables proactive fare management rather than reactive adjustments, allowing agencies to optimize revenue before implementing changes.
Solution Approach 2:
The patent introduces an intermediary analytical layer between fare collection and fare management decisions. This intermediary system processes raw fare data through CAPM-based risk assessment models, generating insights about fare class volatility and riskiness that bridge the gap between simple collection and optimized management, enabling data-driven decisions without requiring complex manual analysis.
2Loss of information
If fare classes are managed without risk assessment insights, then management decisions are made quickly, but fare pricing optimization and volatility understanding are limited
Solution Approach 1:
The system establishes a feedback loop where historical fare data is continuously analyzed to generate risk assessments and volatility metrics. These insights feed back into fare management decisions, which then generate new data for subsequent analysis. The automated feedback mechanism ensures that risk information is continuously updated and available for decision-making without requiring manual data gathering each time.
Solution Approach 2:
The fare management system performs self-service by automatically collecting fare data, calculating risk metrics, generating volatility charts, and providing actionable insights without external intervention. The system serves its own information needs by autonomously processing fare class data and generating the analytical outputs required for optimized fare management, eliminating the need for external consulting or manual analysis.
3Measurement precision
If comprehensive fare data analysis is performed, then revenue optimization is improved, but system complexity and computational requirements increase
Solution Approach 1:
The system transforms raw fare data into meaningful risk parameters by applying the Capital Asset Pricing Model framework. It converts fare revenue data into beta values (measuring systematic risk), expected returns, and volatility metrics. By changing the parameters from raw financial data to standardized risk metrics, the system achieves precise risk measurement while maintaining manageable computational complexity through established financial modeling approaches.
Data Source
AI summary
A system in a public transit network may include a fare collection system, a fare management system, and a processing device for assessing the riskiness of fare classes in the transit network and causing the fare management system to take an action based on the assessment. Particularly, the processing device will receive fare information from the fare collection system, such as via a token reader, determine a fare return series over a period of time, apply a Capital Asset Pricing Model to the fare return series and assess the return versus volatility chart for multiple fare classes. Based on the assessment, the processing device will cause the fare management system to take an action such as changing a fare price or a route, deleting a fare class or a route, combining one or more fare classes or routes, or combining one or more fare classes with one or more routes.