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.

翻译：交通模拟是交通基础设施规划、智能交通控制策略学习及交通流分析的关键工具，其有效性高度依赖模拟器的真实度。传统交通模拟器（如SUMO和CityFlow）常受限于基于超参数简化驾驶行为的规则模型，导致模拟结果缺乏真实性。为提升真实度，部分模拟器通过应用程序接口（API）与机器学习模型交互——后者可从观测数据中学习并提供更复杂的驾驶行为模型。然而，随着车辆数量增加，这种方法面临可扩展性和时间效率的挑战。为解决这些问题，我们提出CityFlowER——基于现有CityFlow模拟器的改进版本，专为高效真实的城市级交通模拟而设计。CityFlowER创新性地将机器学习模型预嵌入模拟器内部，免除了外部API交互需求，从而加速数据计算。该方法支持对个体车辆混合使用规则模型与机器学习行为模型，在大规模模拟中展现出无与伦比的灵活性与效率。通过与现有模拟器的详细对比、实现方法阐述及综合实验，我们证明了CityFlowER在真实度、效率和适应性方面的显著优势。