城市交通异常（如碰撞与中断）威胁着交通系统的安全性、效率与可持续性。本文提出一种基于仿真的框架，用于建模、检测与预测城市路网中的此类异常。我们利用城市交通仿真平台 Simulation of Urban MObility (SUMO)，生成可复现的追尾与交叉口碰撞场景，并配以匹配的基线场景，从而支持受控实验与对比评估。我们记录车辆级的行程时间、速度与排放数据，以支持边缘层级与网络层级的分析。基于该数据集，我们构建了一种混合预测架构，将双向长短期记忆网络（BiLSTM）与扩散卷积循环神经网络（DCRNN）相结合，以同时捕捉时间动态与空间依赖关系。我们在纽约市百老汇走廊开展的仿真实验表明，该框架能够稳定复现事故条件、量化其影响，并提供准确的多步长交通预测。结果凸显了将受控异常生成与深度预测模型相结合的价值，可支撑可复现的评估及可持续交通管理。

Urban traffic anomalies, such as collisions and disruptions, threaten the safety, efficiency, and sustainability of transportation systems. In this paper, we present a simulation-based framework for modeling, detecting, and predicting such anomalies in urban networks. Using the Simulation of Urban MObility (SUMO) platform, we generate reproducible rear-end and intersection crash scenarios with matched baselines, enabling controlled experimentation and comparative evaluation. We record vehicle-level travel time, speed, and emissions for both edge- and network-level analysis. Building on this dataset, we develop a hybrid forecasting architecture that combines bidirectional long short-term memory networks with a diffusion convolutional recurrent neural network to capture temporal dynamics and spatial dependencies. Our simulation studies on the Broadway corridor in New York City demonstrate the framework's ability to reproduce consistent incident conditions, quantify their effects, and provide accurate multi-horizon traffic forecasts. Our results highlight the value of combining controlled anomaly generation with deep predictive models to support reproducible evaluation and sustainable traffic management.