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

TL;DR

This paper introduces Mt-KaRRi, a highly scalable dispatcher for dynamic ride-pooling that processes millions of travelers per hour, enabling large-scale simulation and analysis of future autonomous transport systems.

Contribution

The paper presents Mt-KaRRi, a novel dispatcher leveraging advanced shortest-path algorithms for scalable, real-time ride-pooling simulation with extensive experiments in multiple urban areas.

Findings

Dispatcher scales with ~1ms response time per request.

Enables large-scale ride-pooling studies with tens of thousands of vehicles.

Provides insights into ride quality and resource utilization at scale.

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.