CityFlowER: An Efficient and Realistic Traffic Simulator with Embedded Machine Learning Models

TL;DR

CityFlowER is a novel traffic simulator that integrates machine learning models directly into its core, significantly improving realism and efficiency for large-scale city traffic simulations.

Contribution

It introduces pre-embedded ML models within the simulator, enhancing scalability, realism, and computational speed over existing rule-based and API-dependent simulators.

Findings

CityFlowER outperforms existing simulators in realism and efficiency.

Pre-embedding ML models reduces simulation time significantly.

The simulator demonstrates high scalability for large city traffic scenarios.

Abstract

Traffic simulation is an essential tool for transportation infrastructure planning, intelligent traffic control policy learning, and traffic flow analysis. Its effectiveness relies heavily on the realism of the simulators used. Traditional traffic simulators, such as SUMO and CityFlow, are often limited by their reliance on rule-based models with hyperparameters that oversimplify driving behaviors, resulting in unrealistic simulations. To enhance realism, some simulators have provided Application Programming Interfaces (APIs) to interact with Machine Learning (ML) models, which learn from observed data and offer more sophisticated driving behavior models. However, this approach faces challenges in scalability and time efficiency as vehicle numbers increase. Addressing these limitations, we introduce CityFlowER, an advancement over the existing CityFlow simulator, designed for efficient and realistic city-wide traffic simulation. CityFlowER innovatively pre-embeds ML models within the simulator, eliminating the need for external API interactions and enabling faster data computation. This approach allows for a blend of rule-based and ML behavior models for individual vehicles, offering unparalleled flexibility and efficiency, particularly in large-scale simulations. We provide detailed comparisons with existing simulators, implementation insights, and comprehensive experiments to demonstrate CityFlowER's superiority in terms of realism, efficiency, and adaptability.