Transportation Decision Interface Engine for Service Upgrade Optimization
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Solution Overview
Problem
Existing travel industry systems lack sophistication in handling limited vehicle inventory, repositioning requirements, and varying service experiences, particularly in scenarios with limited access to vehicles and significant disparities in service quality, leading to inefficient use of transportation resources and inadequate upgrade offers.
Innovation Solution
A system that selectively determines and offers upgrades based on client profiles, reposition estimates, and profitability, using machine-learning architectures to predict user acceptance and reduce repositioning costs, thereby optimizing resource utilization and balancing travel demands across regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If upgrade offers are made based on travel demands for a particular vehicle on the departure date, then last minute upgrade opportunities are provided to clients, but transportation resources remain inefficiently utilized due to insufficient filling of empty seats
Solution Approach 1:
The system performs preliminary actions by calculating reposition estimates and identifying upgrade opportunities in advance of the departure date, rather than waiting until the last minute. The engine proactively determines which clients should be offered upgrades before the travel demand is finalized, allowing for better resource allocation and seat filling.
Solution Approach 2:
The system dynamically adjusts upgrade offer strategies based on multiple factors including client profiles, reposition estimates, and profitability calculations. The engine can adapt its behavior based on the specific circumstances of each client and each flight, making real-time decisions about which upgrades to offer and to whom.
2Adaptability or versatility
If upgrade offers are made assuming access to the particular vehicle or similar vehicles, then upgrade opportunities are identified for clients, but the system lacks sophistication in handling limited vehicle inventory and repositioning requirements
Solution Approach 1:
The system changes the parameters of vehicle assignment by introducing reposition estimates that account for the cost and feasibility of moving vehicles between locations. Instead of assuming any vehicle can be assigned, the engine calculates specific reposition parameters including distance, time, and cost to determine realistic upgrade opportunities.
Solution Approach 2:
The reposition estimate calculation acts as an intermediary between the client's upgrade request and the actual vehicle assignment. This intermediary layer evaluates whether the desired vehicle can be realistically obtained by considering inventory constraints and repositioning requirements, providing a more reliable matching process.
3Productivity
If upgrade offers are presented without considering repositioning costs, then more upgrade opportunities are available to clients, but the travel service provider incurs additional burdens and reduced profitability
Solution Approach 1:
The system introduces profitability as a key parameter in the upgrade offer decision process. By calculating whether each potential upgrade is profitable for the service provider, the engine filters out upgrades that would result in net losses, thereby maintaining a sustainable level of upgrade offers that do not excessively burden the provider with repositioning costs.
4Quantity of substance
If the system presents upgrade offers to all clients, then maximum market coverage is achieved, but network congestion and computational resource dissipation occur
Solution Approach 1:
The system applies local quality by customizing upgrade offers to individual client profiles rather than presenting generic offers to all clients. The engine evaluates each client's specific characteristics, preferences, and travel history to determine which clients should receive upgrade offers, thereby reducing unnecessary network traffic and computational waste.
Data Source
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AI summary
Embodiments described herein are related to systems and methods for offering an upgrade for a transportation service. In one aspect, a computer can receive a reservation request for a transportation service including an itinerary indicating a first location, a second location, and a first service category of the transportation service. The computer can determine a plurality of reposition estimates for a plurality of service categories including the first service category and one or more potential service categories, each reposition estimate being determined based upon the first location, the second location, and a particular service category. The computer can identify, as a second service category, the potential service category having the reposition estimate lower than the reposition estimate of the first service category in the reservation request. The computer can transmit to the client device an upgrade notification indicating an eligibility to update the itinerary to include the second service category.