Demand for shared mobility to complement public transportation: Human driven and autonomous vehicles

TL;DR

This study examines how ride-sharing, including human-driven and autonomous vehicles, can enhance public transportation by increasing ridership and reducing wait times, especially in low-density areas.

Contribution

It provides a comparative analysis of human-driven and autonomous ride-sharing services and their impact on travel demand and welfare using real-world case data.

Findings

Ride-sharing can increase ridership by up to 76%.

Wait times can be reduced by 47%.

Fare discounts significantly influence demand and mode shift.

Abstract

Recent advances in communication technologies and automated vehicles have opened doors for alternative mobility systems (taxis, carpool, demand-responsive services, peer-to-peer ridesharing, and car sharing, shared autonomous vehicles/shuttles). These new mobility services have gathered interest from researchers, public and private sectors as potential solutions to address last-mile problem--especially in low density areas where implementation of high frequency buses is not feasible. In this study we investigate the effects of ride-sharing service on travel demand and welfare, as it complements public transportation under different scenarios. Two types of management and vehicle types are considered: crowdsourced human driven vehicles (HDV) (e.g. Uber, Lyft) and centrally operated shared autonomous vehicles (SAV). The influence of fare discount on demand and mode shift is also investigated. A case study of Oakville road network in Ontario, Canada is conducted using real data. The results reveal that ride-sharing having the potential of increasing ridership by 76 per cent and decreasing wait time by 47 per cent under centrally operated shared autonomous vehicles with 50 per cent fare discount for sharing.