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

arXiv: https://arxiv.org/abs/2402.06127 · HTML: https://mlsanity.com/paper/2402.06127/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 9 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors](https://mlsanity.com/paper/2101.06557/) (2021)
- [CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario](https://mlsanity.com/paper/1905.05217/) (2019)
- [TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios](https://mlsanity.com/paper/2210.06609/) (2022)

## Built on by (most cited first, 4 of 4)

- [MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions](https://mlsanity.com/paper/2408.09768/) (2024)
- [SynTraC: A Synthetic Dataset for Traffic Signal Control from Traffic Monitoring Cameras](https://mlsanity.com/paper/2408.09588/) (2024)
- [TranSimHub:A Unified Air-Ground Simulation Platform for Multi-Modal Perception and Decision-Making](https://mlsanity.com/paper/2510.15365/) (2025)
- [Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control](https://mlsanity.com/paper/2507.15174/) (2025)

## Abstract (arXiv)

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.