Dynamic Rendezvous Queueing for On-Demand Transport Throughput
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Solution Overview
Problem
Network-based on-demand transportation systems face inefficiencies in rendezvous throughput, leading to traffic congestion and increased wait times at common pick-up and drop-off locations, particularly during high-demand events like holidays or mass egress events.
Innovation Solution
A network computing system coordinates on-demand transportation by determining a common rendezvous location and managing a dynamic queue of transport providers based on real-time location data, ETAs, and traffic conditions, using application-based coordination to optimize pick-up and drop-off processes through route adjustments and trip swaps, leveraging both human-driven and autonomous vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple transport providers converge at a common rendezvous location simultaneously, then pick-up efficiency is improved, but traffic congestion increases and wait times extend
Solution Approach 1:
The system performs preliminary actions by calculating optimal arrival times for each transport provider before they reach the rendezvous location. The coordination system determines a schedule that staggers arrivals, ensuring providers arrive at different times rather than simultaneously, which prevents congestion while maintaining efficient throughput
Solution Approach 2:
The system implements dynamic routing and timing adjustments based on real-time conditions. Transport providers receive updated instructions to modify their arrival times and routes dynamically, allowing the system to adapt to changing traffic conditions and maintain optimal rendezvous throughput without congestion
2Productivity
If transport providers are coordinated through a dynamic queue system, then traffic flow is improved, but system complexity increases
Solution Approach 1:
The patent introduces a coordination system as an intermediary between transport providers and the rendezvous location. This intermediary manages the dynamic queue, calculates optimal arrival times, and communicates routing instructions to providers, thereby simplifying the overall system by centralizing coordination logic rather than requiring complex peer-to-peer communication between providers
Solution Approach 2:
The system implements feedback mechanisms where transport providers report their status and location back to the coordination system. This feedback loop allows the system to adjust the dynamic queue in real-time, optimizing traffic flow while keeping the coordination logic manageable through iterative adjustments rather than complex predetermined schedules
Data Source
AI summary
A network computing system can maximize throughput for a common rendezvous location by determining estimated times of arrival (ETAs) to the common rendezvous location for matched users and/or the transport providers. Based on the ETAs of each of the transport providers, the computing system can generate a dynamic queue comprising the transport providers for the common rendezvous location, and manage the dynamic queue by sequentially routing the transport providers through the common rendezvous location. The computing system can further dynamically adjust the queue based on changes to the ETAs.