On the Interplay between Self-Driving Cars and Public Transportation

TL;DR

This paper introduces a unified framework combining network and game theory to analyze how autonomous ride-hailing impacts public transportation, revealing potential cannibalization effects in urban mobility systems.

Contribution

It presents a novel integrated modeling approach to study interactions among mobility providers, authorities, and customers in the era of autonomous transportation.

Findings

Autonomous ride-hailing can serve 7% to 80% of customers, depending on conditions.

The framework enables sensitivity analysis of policy and market parameters.

Results highlight potential risks of public transit cannibalization by autonomous services.

Abstract

Cities worldwide struggle with overloaded transportation systems and their externalities. The emerging autonomous transportation technology has the potential to alleviate these issues, but the decisions of profit-maximizing operators running large autonomous fleets could negatively impact other stakeholders and the transportation system. An analysis of these tradeoffs requires modeling the modes of transportation in a unified framework. In this paper, we propose such a framework, which allows us to study the interplay among mobility service providers (MSPs), public transport authorities, and customers. Our framework combines a graph-theoretic network model for the transportation system with a game-theoretic market model in which MSPs are profit maximizers while customers select individually-optimal transportation options. We apply our framework to data for the city of Berlin and present sensitivity analyses to study parameters that MSPs or municipalities can strategically influence. We show that autonomous ride-hailing systems may cannibalize a public transportation system, serving between 7% and 80% of all customers, depending on market conditions and policy restrictions.