  As connected autonomous vehicles (CAVs) become increasingly prevalent, there
is a growing need for simulation platforms that can accurately evaluate CAV
behavior in large-scale environments. In this paper, we propose Flowsim, a
novel simulator specifically designed to meet these requirements. Flowsim
offers a modular and extensible architecture that enables the analysis of CAV
behaviors in large-scale scenarios. It provides researchers with a customizable
platform for studying CAV interactions, evaluating communication and networking
protocols, assessing cybersecurity vulnerabilities, optimizing traffic
management strategies, and developing and evaluating policies for CAV
deployment. Flowsim is implemented in pure Python in approximately 1,500 lines
of code, making it highly readable, understandable, and easily modifiable. We
verified the functionality and performance of Flowsim via a series of
experiments based on realistic traffic scenarios. The results show the
effectiveness of Flowsim in providing a flexible and powerful simulation
environment for evaluating CAV behavior and data flow. Flowsim is a valuable
tool for researchers, policymakers, and industry professionals who are involved
in the development, evaluation, and deployment of CAVs. The code of Flowsim is
publicly available on GitHub under the MIT license.
