Real-time Carpooling Coordination for Dynamic Passenger Matching
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Solution Overview
Problem
Current transportation networking services, such as Uber and Lyft, primarily offer fixed-rate services that do not efficiently accommodate real-time carpooling opportunities, limiting the ability to match non-affiliated passengers with existing transportation vehicle units en route, thereby not optimizing route efficiency or passenger capacity.
Innovation Solution
A system and method for identifying and directing transportation vehicle units already en route to accommodate additional passengers by determining their ability to transport them without significant delay, allowing for real-time carpooling and optimizing routes to minimize delays for all passengers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed-rate services are used, then service simplicity is maintained, but route efficiency and passenger capacity are not optimized
Solution Approach 1:
The system transitions from fixed static rates to dynamic real-time pricing and routing. The coordinating system continuously monitors vehicle locations, passenger requests, and route conditions to dynamically optimize carpooling opportunities, allowing the service to adapt to changing conditions while maintaining operational simplicity through automated coordination.
Solution Approach 2:
The system implements real-time feedback loops where the coordinating system receives data on vehicle locations, passenger requests, and route efficiency metrics, then uses this feedback to continuously adjust carpooling matching decisions. This enables optimization of route efficiency while managing complexity through data-driven automated control.
2Adaptability or versatility
If real-time carpooling matching is implemented, then passenger capacity utilization increases, but system complexity and coordination requirements increase
Solution Approach 1:
The coordinating system performs multiple functions: matching passengers with vehicles, optimizing routes, calculating dynamic pricing, and coordinating rendezvous points. By consolidating these diverse functions into a single multi-functional system, the patent achieves high passenger capacity utilization while managing complexity through functional integration rather than proliferation of separate systems.
Solution Approach 2:
The system enables vehicles and passengers to self-coordinate through automated matching algorithms. The coordinating system provides the framework and algorithms, but the actual matching and coordination happens automatically based on real-time data, reducing the need for complex manual coordination while maximizing capacity utilization.
3Loss of energy
If vehicles accommodate additional passengers en route, then transportation costs decrease, but timing precision and route deviation control become challenging
Solution Approach 1:
The system performs preliminary calculations of optimal rendezvous points, timing adjustments, and route modifications before vehicles and passengers commit to carpooling arrangements. By pre-calculating these parameters based on real-time data, the system enables cost-effective capacity utilization while maintaining timing precision through advance planning and coordination.
Data Source
AI summary
Computationally implemented methods, devices and systems that are designed for transmitting a request for one or more identities of a transportation vehicle unit for transporting a first end user; receiving the one or more identities of the transportation vehicle unit for transporting the first end user, the transportation vehicle unit currently en route to or is currently transporting a second end user and having been identified based, at least in part, on a determination that the transportation vehicle unit is able to accommodate transport of the first end user while transporting the second end user; and directing the identified transportation vehicle unit to rendezvous with the first end user in order to transport the first end user.