Transportation Service Reservation System with Demand Prediction
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Solution Overview
Problem
Current transportation service reservation systems fail to optimize profit and user satisfaction by not considering future demands, leading to vehicle shortages and decreased service availability, especially during peak periods or sudden demand increases.
Innovation Solution
A method that includes receiving a ride request, generating feasible products with multiple service types using a computer that refers to a storage unit with vehicle schedule information, computing choice probabilities and expected future demand, and selecting an assortment of products to present to users based on these factors, dynamically allocating taxi, shared-taxi, and mini-bus services to vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the system allocates vehicles to current ride requests to maximize immediate profit, then the profit from current users improves, but future vehicle availability deteriorates leading to shortages
Solution Approach 1:
The system performs preliminary actions by predicting future ride demands and proactively reserving vehicles for future time periods. The demand prediction unit forecasts future demands, and the schedule determination unit creates advance reservations based on these predictions, ensuring vehicles are available when needed without compromising current service quality
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual ride demands against predicted demands, and adjusting future reservations based on prediction accuracy. The schedule determination unit uses feedback from demand predictions to dynamically adjust vehicle reservations, optimizing the balance between current profit and future availability
2Ease of operation
If the system provides taxi service to current user, then immediate service quality improves, but future service opportunities are lost due to vehicle shortage
Solution Approach 1:
The system dynamically adjusts service allocation based on real-time conditions and predicted future demands. The schedule determination unit creates flexible reservations that can adapt to changing conditions, allowing the system to optimize between current service quality and future service opportunities by making intelligent predictions about future demand patterns
3Loss of energy
If the system uses dynamic allocation to maximize current profit, then immediate profit improves, but long-term user satisfaction deteriorates due to vehicle shortages
Solution Approach 1:
The system performs preliminary demand predictions and creates advance vehicle reservations to ensure future availability. By predicting future ride demands and securing vehicles in advance, the system maintains reliability and user satisfaction while still optimizing current profit through intelligent allocation
Solution Approach 2:
The system uses feedback from demand predictions to continuously optimize the balance between profit maximization and user satisfaction. The schedule determination unit adjusts reservations based on predicted accuracy and actual outcomes, ensuring long-term reliability while maintaining short-term profitability
Data Source
AI summary
A transportation service reservation method executes, by a computer, a process including receiving a ride request specifying an origin and a destination, generating feasible products having service types, by referring to a storage unit that stores information indicating a schedule allocated to each vehicle and the service types of the schedule, for each vehicle capable of providing products having the service types, computing a choice probability of each product forming an assortment of the feasible products, for each of assortments satisfying a predetermined condition amongst the subsets of the feasible product set that are generated, computing an expected value of a number of future ride requests, and selecting an assortment to be presented with respect to the ride request, from the assortment satisfying the predetermined condition, based on the choice probability and the expected value.