Yasa, Doğa (Chair of Robotics, Artificial Intelligence and Real-time Systems), Tangirala, Nagacharan Teja (Technical University of Munich), Knoll, Alois (Technische Universität München)

Analysis of Heterogeneous Fleets with Autonomous and Human-Driven Vehicles

Scheduled for presentation during the Poster Session "Travel Behavior Under ITS" (WeAT16), Wednesday, September 25, 2024, 10:30−12:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 7, 2024

Abstract

Deployment of Autonomous Mobility on Demand (AMoD) is expected to optimize the urban mobility system. However, the infrastructure required for widespread adoption of AMoD is expensive and takes time to build. Hence, the taxi system is expected to operate as a heterogeneous fleet of Autonomous Vehicles (AVs) and Human-driven Vehicles (HDVs). Studying such heterogeneous fleets is essential because transitioning to an autonomous fleet can take a few years. This paper proposes a HDV model that captures a driver’s behavior, such as working shifts, taking breaks, and recharging the vehicle. The model has been implemented in an open-source AMoD simulation framework called FleetPy. Sample experiments are carried out to demonstrate the possible insights obtained with the HDV model. The open-source data available for the Manhattan region is utilized for the experiments. A fleet planning experiment is designed with a varying composition of AVs and HDVs to determine the appropriate fleet size to satisfy a given traffic demand. Results indicate that 100 AVs under ideal operating conditions can replace 2500 HDVs for the selected traffic demand. The experiments demonstrate that the HDVs model can be beneficial in deriving insights related to heterogeneous fleets.