Graph convolutional network for traffic incidents duration classification

Abstract

Traffic incidents are a primary cause of severe congestion in urban areas, making accurate forecasting of incident duration essential for effective traffic management systems. However, the inherent uncertainty associated with incidents presents significant challenges in predicting their durations. In this paper, we propose a novel deep neural network model for predicting and classifying traffic incident durations. To capture the dynamic nature of incidents, the model learns from time series data on traffic flow, speed, and occupancy. Additionally, it employs a graph neural network architecture to model the spatial relationships between sensors, while also accounting for factors such as time and incident type. By training the model with cross-entropy loss, we enable it to predict whether an incident’s duration will be long or short. Experimental results demonstrate that our model outperforms existing baselines, demonstrating the effectiveness of our proposed approach. Furthermore, we conduct a case study to visualize the impact of incidents and further validate the model’s predictive capability.