CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario (1905.05217v1)

Abstract: Traffic signal control is an emerging application scenario for reinforcement learning. Besides being as an important problem that affects people's daily life in commuting, traffic signal control poses its unique challenges for reinforcement learning in terms of adapting to dynamic traffic environment and coordinating thousands of agents including vehicles and pedestrians. A key factor in the success of modern reinforcement learning relies on a good simulator to generate a large number of data samples for learning. The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios. This motivates us to create a new traffic simulator CityFlow with fundamentally optimized data structures and efficient algorithms. CityFlow can support flexible definitions for road network and traffic flow based on synthetic and real-world data. It also provides user-friendly interface for reinforcement learning. Most importantly, CityFlow is more than twenty times faster than SUMO and is capable of supporting city-wide traffic simulation with an interactive render for monitoring. Besides traffic signal control, CityFlow could serve as the base for other transportation studies and can create new possibilities to test machine learning methods in the intelligent transportation domain.

• The paper presents CityFlow, a novel multi-agent RL platform that overcomes SUMO's scalability limits for large-scale urban traffic simulation.
• It leverages multithreading and optimized data structures to model realistic vehicle behavior at each timestep under dynamic conditions.
• Empirical results show CityFlow executes 72 simulation steps per second while maintaining fidelity in vehicle travel times, enhancing RL traffic control research.

CityFlow: Advancements in Traffic Simulation with Reinforcement Learning

The paper "CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario" presents CityFlow, a novel traffic simulation platform designed to overcome scalability limitations of previous solutions, particularly SUMO. Transport and traffic infrastructure is a pivotal area of paper where reinforcement learning (RL) techniques are gaining traction due to their potential to optimize traffic signals dynamically.

CityFlow addresses several critical issues encountered in urban traffic signal control. The complexity of coordinating thousands of signals across a city is compounded by dynamic traffic environments, necessitating robust simulation tools for RL applications. Current popular simulators like SUMO are limited by their inability to efficiently handle large-scale traffic networks, inhibiting large-scale RL studies. In contrast, CityFlow demonstrates significant improvements in computational efficiency, supporting city-wide simulations that execute over 20 times faster than SUMO.

From a technical perspective, CityFlow achieves these performance enhancements through multithreading and optimized data structures. These architectural changes allow it to handle high volumes of traffic with more detailed vehicle behavior modeling at each timestep, thus accommodating large, realistic road networks. The efficient implementation of the car-following model and intersection algorithms enables the simulator to accurately replicate real-world driving behavior while maintaining computational speed. Additionally, CityFlow leverages a python interface using pybind11, facilitating seamless integration with RL frameworks for data acquisition and model training.

The empirical evaluation highlights CityFlow's superiority over SUMO in generating simulation steps, with a demonstrated capability of executing 72 steps per second on a large grid with eight threads. This improvement is crucial for RL applications that require extensive datasets for training. Notably, despite the increase in simulation speed, CityFlow maintains fidelity with respect to SUMO when comparing simulated vehicle travel times, ensuring that its rapid data generation does not compromise accuracy.

Beyond traffic signal control optimization, the paper suggests potential for CityFlow's application across diverse urban mobility challenges. The authors anticipate its utility in vehicle routing, congestion management, and other transportation studies that require sophisticated simulation environments. The planned integration with real-world data for model calibration hints at its future potential to provide even more realistic traffic simulations.

In conclusion, CityFlow represents a significant step forward in the domain of traffic simulation for RL applications. Its ability to perform rapid, detailed simulations aligns with the growing demands of complex urban traffic systems, supporting both academic research and practical implementations in intelligent transportation systems. The development of CityFlow not only enhances current RL traffic signal control methods but also opens avenues for exploring broader applications within urban mobility frameworks. Future work could capitalize on these capabilities, progressively bridging the gap between simulation and real-world traffic management solutions.