We propose a novel system leveraging deep learning-based methods to predict urban traffic accidents and estimate their severity. The major challenge is the data imbalance problem in traffic accident prediction. The problem is caused by numerous zero values in the dataset due to the rarity of traffic accidents. To address the issue, we propose a grid-clustered feature map with the ideas of grids and cells. To predict the occurrence of accidents in the grid, we introduce an accident detector that combines the power of a Convolutional Neural Network (CNN) with a Deep Neural Network (DNN). Then, hierarchical DNNs are supposed to be an accident risk classifier to estimate the risk of each cell in the accident-occurrence grid. The proposed system can effectively reduce instances with no traffic accidents. Furthermore, we introduce the concept of the Accident Risk Index (ARI) to better represent the severity of risk at each cell. Also, we consider all the explanatory variables, such as dangerous driving behaviors, traffic mobility, and safety facility information, that can be related to traffic accidents. To improve the prediction accuracy, we further take into consideration all the explanatory variables, such as dangerous driving behaviors, traffic mobility, and safety facility information, that can be related to traffic accidents. In the experiment, we highlight the benefits of our method for urban traffic accident management by significantly improving model performance compared to the baselines. The feasibility and applicability of the proposed system are validated in the data of Daejeon City, Republic of Korea. The proposed prediction system can dynamically advise and recommend commuters, traffic management systems, and city planners on alternatives, optimizations, and interventions.

In large-scale traffic optimization, models based on the Macroscopic Fundamental Diagram (MFD) are recognized for their efficiency in broad network analyses. However, they fail to reflect variations in the individual traffic status of each road link, leading to a gap in detailed traffic optimization and analysis. To address the limitation, this study introduces a Local Correction Factor (LCF) that represents local speed deviations between the actual link speed and network mean speed from the MFD based on the link configuration. The LCF is calculated using a deep learning approach that takes as inputs the network mean speed from the MFD and the road network configuration. Our framework integrates Graph Attention Networks (GATs) with Gated Recurrent Units (GRUs) to capture both the spatial configurations and temporal correlations within the network. Coupled with a strategic network partitioning method, our model enhances the precision of link-level traffic speed estimations while preserving the computational advantages of aggregate models. In our experiments, we evaluate the proposed LCF across various urban traffic scenarios, including different levels of origin-destination trip demand and distribution, as well as diverse road configurations. The results demonstrate the robust adaptability and effectiveness of the proposed model. Furthermore, we validate the practicality of our model by calculating the travel time of each randomly generated path, achieving an average error reduction of approximately 84% relative to MFD-based results.

Traffic speed prediction is essential for efficient traffic operation and management by distributing demand concentration in time and space. To make an accurate prediction, it is required to consider spatio-temporal characteristics of the traffic evolution. Recently, deep learning-based approaches, especially Graph Neural Network (GNN) has been widely adopted to reflect the stated characteristics. However, existing GNN models mainly used for short-term prediction, whereas long-term traffic prediction is more useful by enabling earlier and efficient decisions of traffic management as well as individual travels. In this study, we propose Asymmetric Long-Term Graph Multi-Attention Network (ALT-GMAN) algorithm, an extension of the GMAN. ALT-GMAN can predict short and long-term traffic speed by considering asymmetric characteristics of forward and backward waves observed in real roadways. ALT-GMAN is tested with six months highway data of PeMS-Bay area, and MAPE for 3-hours and 6-hours prediction is evaluated as 5.53% and 6.05%, respectively. ALT-GMAN outperforms the existing models in short-term speed prediction, and provides a robust performance in long-term prediction problems, too.