Rental Vehicle Demand Forecasting via Customer Segmentation
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Solution Overview
Problem
Rental vehicle agencies face challenges in accurately forecasting demand for specific types of vehicles at airport locations due to a lack of detailed customer data, leading to insufficient availability of desired vehicles.
Innovation Solution
A system and method that utilizes customer data from flight and vehicle rental sources to classify travelers into segments based on their preferences, predicting demand for different vehicle types and optimizing fleet management by integrating lodging and transaction data to manage seasonal demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rental agencies forecast demand using only historical data at a coarse level, then the forecasting process is simple, but the accuracy of demand prediction for specific vehicle types is poor
Solution Approach 1:
The patent segments customers into different categories (e.g., leisure travelers, business travelers, local residents) based on their behavior patterns and preferences. This segmentation allows the system to predict demand for specific vehicle types (luxury, economy, SUV) more accurately by understanding the vehicle preferences of each customer segment, rather than using a single coarse-level forecast for all customers.
Solution Approach 2:
The patent introduces multiple dimensions to the forecasting system by integrating data from various sources including flight information, hotel bookings, event data, and social media. This multi-dimensional approach transforms the simple historical demand forecast into a comprehensive prediction model that considers passenger flow, seasonal variations, local events, and customer preferences simultaneously.
2Reliability
If rental agencies maintain a larger fleet to ensure vehicle availability, then customer satisfaction improves, but operational costs and resource utilization efficiency worsen
Solution Approach 1:
The system performs preliminary demand prediction by analyzing flight schedules, hotel bookings, and event data before peak demand periods occur. This allows rental agencies to proactively allocate the right number and types of vehicles to specific locations and time periods, ensuring availability when needed without maintaining excessive fleets throughout. The predictive insights enable advance fleet optimization decisions.
Solution Approach 2:
The patent implements dynamic fleet management that adjusts vehicle allocation based on real-time and predictive demand signals. Rather than maintaining static fleet sizes, the system continuously optimizes vehicle distribution across locations and types based on updated predictions from flight data, hotel bookings, and seasonal patterns, thereby improving both availability and resource efficiency.
3Measurement precision
If rental agencies collect and process detailed customer data from multiple sources, then demand forecasting accuracy improves, but data privacy risks and system complexity increase
Solution Approach 1:
The patent extracts only the necessary data elements needed for demand forecasting and customer segmentation from multiple sources, rather than collecting entire customer profiles. The system processes aggregated and anonymized data to identify patterns in vehicle preferences and travel behavior without storing or exposing sensitive personal information, thereby reducing privacy risks while maintaining forecasting accuracy.
Data Source
AI summary
A system and a method for optimizing supply of rental vehicles, comprising receiving customer data associated with a plurality of customers from a plurality of sources; classifying the customers into a plurality of segments, based on said data, each segment being indicative of vehicle rental preferences of customers in the segment; determining whether any customers have already opted for a vehicle rental; determining the destination and source location of the customer and predicting likely vehicle demand for different vehicles based on said classification and said determination.