Spatiotemporal Forecasting of Incidents and Congestion with Implications for Sustainable Traffic Control

2025/09/29 by Kinchen, Tony, Bai, Ting, S., Nishanth Venkatesh +1
#FOS: Electrical engineering #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2509.25515

Abstract

Urban traffic anomalies such as collisions and disruptions threaten the safety, efficiency, and sustainability of transportation systems. We present a simulation-based framework for modeling, detecting, and predicting such anomalies in urban networks. Using the 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 edge and network-level analysis. 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.

Citations

• Stochastic Time-Optimal Trajectory Planning for Connected and Automated Vehicles in Mixed-Traffic Merging Scenarios
• Large-Scale Multi-Fleet Platoon Coordination: A Dynamic Programming Approach
• Approximate Dynamic Programming for Platoon Coordination under Hours-of-Service Regulations
• A Safety-Prioritized Receding Horizon Control Framework for Platoon Formation in a Mixed Traffic Environment
• Federated Variational Learning for Anomaly Detection in Multivariate Time Series
• Estimating congestion zones and travel time indexes based on the floating car data
• Urban Anomaly Analytics: Description, Detection, and Prediction

Related

• RadNet: Incident Prediction in Spatio-Temporal Road Graph Networks Using Traffic Forecasting
• Accident-Driven Congestion Prediction and Simulation: An Explainable Framework Using Advanced Clustering and Bayesian Networks
• FUSE-Traffic: Fusion of Unstructured and Structured Data for Event-aware Traffic Forecasting
• RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework
• PSTN: Periodic Spatial-temporal Deep Neural Network for Traffic Condition Prediction