Dynamic Share-Ride Fare Control for Booking Maximization
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Solution Overview
Problem
Ride-sharing systems face challenges in maximizing share-ride bookings and maintaining a gross merchandise value (GMV) per unit of distance, leading to passenger dissatisfaction and environmental issues due to high traffic and pollution from excessive cab operations.
Innovation Solution
A method and system that dynamically control share-ride fares by analyzing historical data to determine conversion rates and GMV, generating error signals based on deviations from defined thresholds, and adjusting fares in real-time to optimize bookings and maintain business requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the cab service provider engages more cabs to meet increased demand, then the service coverage and availability improve, but traffic congestion and environmental pollution worsen
Solution Approach 1:
The patent combines multiple individual cab trips into shared ride routes by identifying common source-destination pairs and grouping passengers traveling to similar locations. This merging approach allows multiple passengers to share a single cab journey, thereby reducing the total number of cabs needed to serve the same demand while improving service coverage.
Solution Approach 2:
The system recovers unused cab capacity by identifying passengers who can be accommodated in existing cabs on shared routes. Instead of dispatching separate cabs for each passenger request, the system recovers capacity by filling available seats in cabs that are already deployed for similar routes, thereby reducing overall cab deployment and associated environmental harm.
2Ease of operation
If the cab service provider implements fixed pricing for share-rides, then pricing simplicity improves, but passenger satisfaction and booking rates worsen due to higher costs
Solution Approach 1:
The patent implements dynamic pricing for share-ride services where fares are adjusted in real-time based on demand conditions, time of day, route popularity, and available capacity. This dynamic approach replaces fixed pricing with flexible rate structures that automatically optimize for both passenger affordability and provider revenue, thereby increasing booking rates while maintaining pricing clarity through algorithmic determination.
Solution Approach 2:
The system changes pricing parameters dynamically by adjusting share-ride fares based on multiple variables including time of day, demand intensity, route characteristics, and cab availability. This parameter-based pricing strategy allows the system to optimize fares for different market conditions, making share-rides more attractive to passengers during off-peak periods while maintaining profitability during high-demand periods.
3Productivity
If the cab service provider offers monetary discounts to increase share-ride bookings, then booking rates improve, but gross merchandise value per unit distance deteriorates
Solution Approach 1:
The patent implements dynamic pricing that adjusts share-ride fares in real-time based on market conditions, capacity utilization, and demand patterns. This dynamic approach replaces static discounts with adaptive pricing that automatically optimizes revenue per unit distance while maintaining competitive rates. The system adjusts fares upward when capacity is underutilized and downward when demand is high, eliminating the need for blanket discounts while preserving booking rates.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor booking rates, revenue metrics, and capacity utilization to adjust pricing strategies. By analyzing real-time data on share-ride performance, the system provides feedback to the pricing algorithm, enabling automatic optimization of fares to maintain both high booking rates and healthy gross merchandise value without requiring manual discount interventions.
4Quantity of substance
If the cab service provider uses dynamic pricing models, then revenue optimization improves, but passenger satisfaction worsens due to fare variability and unpredictability
Solution Approach 1:
The patent changes pricing parameters dynamically but within predefined boundaries and guidelines. The system adjusts fares based on multiple transparent factors including time of day, route demand, and capacity utilization, all of which are communicated to passengers in advance. This parameter-based approach provides predictability within flexibility, allowing revenue optimization while maintaining passenger satisfaction through transparent and understandable pricing variations.
Solution Approach 2:
The system performs preliminary pricing calculations and communicates fare estimates to passengers before booking confirmation. By providing advance notice of dynamic fare adjustments and explaining the factors influencing pricing, the system allows passengers to make informed decisions. This preliminary action reduces surprises and maintains trust, thereby preserving passenger satisfaction even as revenue optimization through dynamic pricing increases.
Data Source
AI summary
A method and a system for maximizing share-ride bookings in a geographical area in a ride-sharing system are provided. Historical share-ride demands for the geographical area are estimated. A time period is segmented into time intervals such that each time interval has an equal count of the estimated historical share-ride demands. A conversion rate and a gross merchandise value (GMV) per unit of distance are determined for a first time interval at a check point of a second time interval. Error signals are generated at the check point based on deviations in the conversion rate and the GMV per unit of distance with respect to a defined conversion rate and a defined GMV per unit of distance, respectively. A share-ride fare in the second time interval is controlled based on the error signals to maximize the share-ride bookings during the second time interval.