Ride-Hailing Fare Recommendation System with Net Value Calculation
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Solution Overview
Problem
Ride-hailing operators face challenges in making informed decisions due to limited information about available fares, which hinders optimization of fare revenue and value.
Innovation Solution
A system and method that provide ride-hailing recommendations by calculating and presenting net values for potential fares based on route characteristics, subsequent ride-hailing request probabilities, and adjusting for factors like wait times and operator preferences, to optimize fare selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If operators are provided with limited information about fares (pick-up location, drop-off location, and fare value), then the operator can make quick decisions, but the operator cannot optimize fare revenue and value effectively
Solution Approach 1:
The system performs preliminary calculations of net value, route characteristics, and subsequent request probabilities before presenting fare options to the operator. This allows the operator to make informed decisions without manually analyzing multiple factors, thus maintaining quick decision-making while optimizing fare revenue through data-driven recommendations.
Solution Approach 2:
The system acts as an intermediary between the fare information system and the operator, processing raw fare data and transforming it into actionable net value recommendations. This intermediary layer enriches the limited fare information with additional contextual data (route characteristics, demand patterns) without requiring the operator to directly access or analyze the underlying complex data structures.
2Productivity
If the system calculates and presents multiple fare factors (route characteristics, subsequent request probabilities, net values), then fare optimization is improved, but the system complexity increases
Solution Approach 1:
The system extracts and separates different fare evaluation factors (route characteristics, subsequent request probabilities, net values) into distinct computational modules. This modular approach allows each factor to be calculated independently using specific algorithms, making the overall complex system manageable through division of labor among specialized components.
Solution Approach 2:
The system transforms multiple complex fare evaluation parameters into a single net value metric that consolidates route characteristics, fare value, and subsequent request probabilities. This parameter transformation simplifies the operator's decision-making process by presenting a unified recommendation metric while the underlying system handles the complexity of multiple input parameters.
3Measurement precision
If the system considers route characteristics and subsequent request probabilities, then fare value determination is improved, but the calculation time and processing requirements increase
Solution Approach 1:
The system pre-calculates and stores route characteristics (distance, traffic patterns, road types) and subsequent request probabilities for various locations before they are needed for fare evaluation. This preliminary preparation allows the system to quickly retrieve and combine these pre-processed data elements when evaluating fares, reducing real-time calculation time while maintaining accurate fare value determination.
Data Source
AI summary
A method for providing ride-hailing recommendations to an operator includes receiving at least a first initial ride-hailing request and a second initial ride-hailing request from potential ride-hailing users; identifying at least a first route characteristic corresponding to a first route associated with the first initial ride-hailing request; identifying at least a second route characteristic corresponding to a second route associated with the second initial ride-hailing request; calculating a first subsequent ride-hailing request probability; calculating a second subsequent ride-hailing request; determining a first ride-hailing request value based on at least the first route characteristic and the first subsequent ride-hailing request probability; determining a second ride-hailing request value based on at least the second route characteristic and the second subsequent ride-hailing request probability; and displaying the first and second initial ride-hailing requests and the first and second ride-hailing request values.