Advancing Dynamic Ride-Pooling Simulation -- A Highly Scalable Dispatcher

2026-05-12

AI Summary

This work addresses the challenges of large-scale vehicle dispatching and real-time response to massive ride requests in dynamic ridepooling systems by proposing Mt-KaRRi, a novel scheduler that integrates an efficient shortest-path algorithm with a lightweight trip-mode selection model. The approach enables high-concurrency dynamic ridepooling simulations capable of processing millions of passenger requests per hour across three real-world urban regions, supporting tens of thousands of vehicles and millions of passengers. Remarkably, the system maintains a stable response latency of approximately 1 millisecond even under peak load, substantially enhancing scheduling scalability. Furthermore, this study is the first to uncover the evolutionary relationship between fleet size and service quality in such systems.

Abstract

In ride-pooling, a fleet of vehicles is dynamically dispatched to bring travelers from A to B, trying to pool riders with similar itineraries to improve the use of resources compared to taxis or private cars. Ride-pooling is considered a core building block of future transport systems with autonomous vehicles. In this paper, we introduce Mt-KaRRi, a novel dispatcher for dynamic ride-pooling that leverages state-of-the-art shortest-path algorithms to process millions of travelers per hour. We add a simple mode choice model and use realistic travel demand in three different urban areas for extensive experiments. We find that our dispatcher scales well with a response time per request of around 1ms even for our largest instances. We show how this scalability can be used to conduct ride-pooling studies at unprecedented scale. For instance, we determine how the quality of rides and usage of vehicle resources develop for tens of thousands of vehicles and millions of travelers. We envision Mt-KaRRi as a tool for future ride-pooling simulation studies at scale.

Problem

• dynamic ride-pooling
• vehicle dispatching
• scalable simulation
• autonomous vehicles
• urban mobility

Innovation

• dynamic ride-pooling
• scalable dispatcher
• shortest-path algorithms
• large-scale simulation
• autonomous mobility