The Impact of Shared Autonomous Vehicles in Microtransit Systems: A Case Study in Atlanta

TL;DR

This paper explores how shared autonomous vehicles can improve microtransit systems by enhancing service quality, reliability, and user experience through optimized dispatching and dynamic rebalancing, based on a case study in Atlanta.

Contribution

It introduces a novel dispatching model incorporating SAV functionalities and a model predictive control approach for dynamic shuttle rebalancing in microtransit.

Findings

SAVs improve service quality over traditional shuttles.

Dynamic rebalancing enhances system reliability.

Case study shows increased user satisfaction with SAV integration.

Abstract

Microtransit systems represent an enhancement to solve the first- and last-mile problem, integrating traditional rail and bus networks with on-demand shuttles into a flexible, integrated system. This type of demand responsive transport provides greater accessibility and higher quality levels of service compared to conventional fixed-route transit services. Advances in technology offer further opportunities to enhance microtransit performance. In particular, shared autonomous vehicles (SAVs) have the potential to transform the mobility landscape by enabling more sustainable operations, enhanced user convenience, and greater system reliability. This paper investigates the integration of SAVs in microtransit systems, advancing the technological capabilities of on-demand shuttles. A shuttle dispatching optimization model is enhanced to accommodate for driver behavior and SAV functionalities. A model predictive control approach is proposed that dynamically rebalances on-demand shuttles towards areas of higher demand without relying on vast historical data. Scenario-driven experiments are conducted using data from the MARTA Reach microtransit pilot. The results demonstrate that SAVs can elevate both service quality and user experience compared to traditional on-demand shuttles in microtransit systems.