"How do urban incidents affect traffic speed?" A Deep Graph Convolutional Network for Incident-driven Traffic Speed Prediction
This work addresses traffic planning challenges by improving prediction accuracy, though it is incremental as it builds on existing methods by adding incident features.
The paper tackled traffic speed prediction by incorporating urban incident data, achieving superior performance on real-world datasets from San Francisco and New York City compared to benchmarks.
Accurate traffic speed prediction is an important and challenging topic for transportation planning. Previous studies on traffic speed prediction predominately used spatio-temporal and context features for prediction. However, they have not made good use of the impact of urban traffic incidents. In this work, we aim to make use of the information of urban incidents to achieve a better prediction of traffic speed. Our incident-driven prediction framework consists of three processes. First, we propose a critical incident discovery method to discover urban traffic incidents with high impact on traffic speed. Second, we design a binary classifier, which uses deep learning methods to extract the latent incident impact features from the middle layer of the classifier. Combining above methods, we propose a Deep Incident-Aware Graph Convolutional Network (DIGC-Net) to effectively incorporate urban traffic incident, spatio-temporal, periodic and context features for traffic speed prediction. We conduct experiments on two real-world urban traffic datasets of San Francisco and New York City. The results demonstrate the superior performance of our model compare to the competing benchmarks.