Scaling Law of Urban Ride Sharing

TL;DR

This paper uncovers a universal scaling law for ride shareability across multiple cities, enabling accurate predictions of ride sharing potential using basic urban parameters, which can inform sustainable urban transportation planning.

Contribution

It introduces a universal scaling law for ride shareability in cities and provides a simple model to predict ride sharing potential based on urban parameters.

Findings

Shareability curves collapse onto a single universal curve after rescaling.

A simple theoretical model accurately predicts ride sharing potential in different cities.

The scaling law enables extrapolation of ride sharing potential without adjustable parameters.

Abstract

Sharing rides could drastically improve the efficiency of car and taxi transportation. Unleashing such potential, however, requires understanding how urban parameters affect the fraction of individual trips that can be shared, a quantity that we call shareability. Using data on millions of taxi trips in New York City, San Francisco, Singapore, and Vienna, we compute the shareability curves for each city, and find that a natural rescaling collapses them onto a single, universal curve. We explain this scaling law theoretically with a simple model that predicts the potential for ride sharing in any city, using a few basic urban quantities and no adjustable parameters. Accurate extrapolations of this type will help planners, transportation companies, and society at large to shape a sustainable path for urban growth.