Dynamic Fleet Routing for On-Demand Transportation Requests
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Solution Overview
Problem
Existing mass transportation services, such as bus routes and schedules, are inefficient and inconvenient for passengers and costly for operators, lacking flexibility and real-time adaptability.
Innovation Solution
A dynamic routing plan is generated in real-time for a fleet of multi-passenger vehicles based on trip parameters and operator-specific parameters, using a heuristic algorithm to minimize an objective function that balances operating costs and service quality metrics, with a phased commitment of trip details to passengers and drivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed bus schedules and routes are used, then operational simplicity is maintained, but service convenience and adaptability deteriorate
Solution Approach 1:
The patent implements dynamic routing where bus schedules and routes are continuously adjusted in real-time based on changing passenger demand, weather conditions, traffic patterns, and other parameters. The system transitions from static fixed routes to dynamic adaptive routing that automatically recalculates optimal paths and timing, directly resolving the contradiction between routing flexibility and system complexity.
Solution Approach 2:
The system incorporates continuous feedback loops where passenger requests, vehicle locations, and service quality metrics are monitored in real-time and fed back into the routing optimization algorithm. This feedback mechanism enables the system to adapt and improve routing decisions dynamically, achieving flexibility while managing complexity through data-driven automation.
2Reliability
If real-time routing optimization is implemented, then service quality improves, but computational complexity and processing time increase
Solution Approach 1:
The patent divides the complex routing optimization problem into manageable segments by processing requests in batches, dividing the service area into zones, and breaking down the optimization into discrete decision stages. This segmentation allows the system to handle real-time optimization computationally by processing smaller sub-problems sequentially rather than attempting to solve the entire complex routing problem simultaneously.
Solution Approach 2:
The system performs preliminary routing calculations and prepares multiple pre-computed route options in advance based on historical data and predicted demand patterns. When real-time requests arrive, the system quickly selects and adjusts from these pre-prepared options rather than calculating completely new routes from scratch, reducing computational complexity while maintaining service quality.
3Adaptability or versatility
If dynamic routing adjustments are made frequently, then service adaptability improves, but system stability and passenger reliability deteriorate
Solution Approach 1:
The patent implements periodic routing updates at predetermined intervals or after accumulating a certain number of new requests, rather than adjusting routes with every single request. This periodic action allows the system to maintain stability by committing to routes for defined periods while still achieving adaptability through regular scheduled updates, preventing excessive fluctuations that would confuse passengers.
Solution Approach 2:
The system incorporates buffer times and minimum commitment periods in the routing algorithm, ensuring that routes are not changed too frequently by built-in constraints. These cushioning mechanisms prevent instability by requiring a minimum threshold of new information or time elapsed before routing changes are implemented, balancing adaptability with stability.
Data Source
AI summary
A method and a system for generating, in real time, a dynamic routing plan for a fleet of vehicles is provided. The method is performed by one or more processors and comprises receiving trip or transport requests from a plurality of processing devices, generating routes for a fleet of vehicles, and continuously updating and/or deleting uncommitted segments of the routing plan and/or adding new routes to the routing plan, to minimize an objective function value of the routing plan, which is based on a combination of operating costs of the vehicles and service quality metrics.