Dynamic Ride-Sharing Assignment With Future Demand Prediction
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Solution Overview
Problem
Current ride-sharing systems face challenges in efficiently assigning travel requests to autonomous vehicles and predicting future demands, particularly in managing large numbers of passengers and trips while maintaining real-time booking experiences.
Innovation Solution
A system and technique that utilize a reactive anytime optimal method to dynamically generate optimal routes and assign vehicles to passengers, incorporating a pairwise request-vehicle graph and request-trip-vehicle graph to solve the unified problem of passenger and vehicle assignment, allowing for up to 10 simultaneous passengers per vehicle and rebalancing vehicles to high-demand areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ride-sharing systems limit passengers to 2 per vehicle, then vehicle assignment and routing becomes simpler, but system productivity and capacity are reduced
Solution Approach 1:
The patent segments the vehicle assignment problem into multiple hierarchical levels: (1) identifying candidate vehicles for each request, (2) forming vehicle pools based on compatibility, (3) optimizing assignments within pools, and (4) coordinating across multiple pools. This segmentation enables the system to handle high-capacity vehicles (up to 10 passengers) while maintaining computational tractability through structured decomposition of the assignment space.
Solution Approach 2:
The system performs preliminary actions by pre-identifying candidate vehicles and pre-forming vehicle pools before final assignment optimization. This preliminary structuring of the solution space allows the system to efficiently explore assignments for high-capacity vehicles without facing the full combinatorial complexity at once, thereby enabling higher productivity with managed complexity.
2Ease of operation
If the system optimizes for real-time booking experience, then user satisfaction improves, but computational time and processing complexity increase
Solution Approach 1:
The patent implements dynamic adjustment of optimization parameters based on system state. The system dynamically balances exploration vs. exploitation, adjusts pool formation strategies based on demand patterns, and adapts assignment horizons based on computational load and time constraints. This dynamic behavior enables real-time responsiveness while managing computational time through context-aware parameter adjustment.
Solution Approach 2:
The system applies partial optimization by focusing computational efforts on the most critical decision layers (candidate identification and pool formation) while using heuristics for final assignments. This partial action approach delivers sufficient real-time performance and user experience without requiring complete optimization of all assignment dimensions, thereby limiting computational time expenditure while maintaining ease of operation.
3Reliability
If the system uses a larger fleet size, then service coverage and reliability improve, but operational costs and system complexity increase
Solution Approach 1:
The patent enables vehicles to serve multiple functions and multiple requests through high-capacity pooling. A single vehicle can serve up to 10 different passengers sequentially or simultaneously, making the fleet more versatile and reducing the total number of vehicles needed for equivalent service coverage. This multi-functionality improves reliability and service coverage while reducing fleet management complexity compared to managing many single-occupancy vehicles.
Solution Approach 2:
The system changes the key parameter of vehicle capacity from traditional limits (1-2 passengers) to high capacity (up to 10 passengers). This parameter change fundamentally alters the fleet requirements, allowing fewer vehicles to provide equivalent or superior service coverage and reliability, thereby reducing operational complexity while maintaining or improving service levels.
4Productivity
If the system increases vehicle capacity to 10 passengers, then productivity and efficiency improve, but difficulty of detecting and measuring assignment optimality increases
Solution Approach 1:
The patent implements feedback mechanisms that track and evaluate assignment quality metrics including vehicle utilization rates, passenger waiting times, and route efficiency. The system uses this feedback to continuously refine its assignment strategies and verify optimality through measurable performance indicators rather than relying on complex theoretical proofs, thereby enabling high-capacity assignments with tractable verification.
Data Source
AI summary
Described is a method and system for vehicle routing and request assignment which incorporates a prediction of future demand. The method seamlessly integrates sampled future requests into request assignments and vehicle routing.