Spatiotemporal Forecasting of Incidents and Congestion with Implications for Sustainable Traffic Control
For traffic management researchers, this provides a reproducible evaluation pipeline for anomaly detection and forecasting, but the method is incremental (combining existing models) and results are simulation-based.
The paper presents a simulation-based framework using SUMO to generate reproducible traffic anomalies and a hybrid deep learning model (BiLSTM + DCRNN) for spatiotemporal forecasting. The framework accurately predicts traffic conditions and quantifies incident effects on the Broadway corridor in NYC.
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.