Scaling Laws of Dynamic High-Capacity Ride-Sharing

TL;DR

This paper uncovers universal scaling laws that describe how performance metrics in high-capacity dynamic ride-sharing systems depend on demand-supply ratios, aiding urban planning and operational strategies.

Contribution

It introduces universal scaling laws for high-capacity ride-sharing, validated across multiple city networks, linking system load to key performance metrics.

Findings

Scaling laws effectively predict passenger service rate and vehicle occupancy.

Universal applicability across different city topologies and demand scenarios.

Provides a quantitative framework for optimizing ride-sharing systems.

Abstract

Dynamic ride-sharing services, including ride-pooling offered by ride-hailing platforms and demand-responsive buses, have become an essential part of urban mobility systems. These services cater to personalized and on-demand mobility requirements while simultaneously improving efficiency and sustainability by accommodating several trip requests within a single ride. However, quantifying the advantages and disadvantages of dynamic ride-sharing, particularly high-capacity ride-sharing, remains a challenge due to the complex dynamics that depend on several factors, including matching algorithms, vehicle capacity, transportation network topology, and spatiotemporal demand and supply distribution. In this study, we conduct extensive experiments on an agent-based simulation platform calibrated by real-world mobility data from Chengdu, Hong Kong, and Manhattan. Our findings reveal a few scaling laws that can effectively measure how key performance metrics such as passenger service rate and vehicle occupancy rate change with a dimensionless system loading factor that reflects the relative magnitude of demand versus supply. Moreover, our results indicate that these scaling laws are universal for different network topologies and supply-demand situations. As a result, these scaling laws offer a means for urban planners, city managers, and ride-hailing platforms to quantify the potential benefits and drawbacks of dynamic ride-sharing under different circumstances and to design better operational and regulatory strategies.