Learning spatial patterns and temporal dependencies for traffic accident severity prediction: A deep learning approach

Abstract

Traffic accidents have a substantial impact on human life and property, resulting in millions of injuries every year. To ensure road safety and enhance the research in this direction, it is necessary to develop methods that can efficiently predict and classify the accident severity. However, traffic accident datasets may contain a large number of features, making it challenging to extract relevant information and patterns from high-dimensional data. Moreover, traffic accidents may be influenced by multiple factors and temporal dependencies, leading to a dynamic impact of each factor on accident severity over time. To address these challenges, we propose a novel deep-learning approach for predicting traffic accident severity. Specifically, we first conduct a thorough data preprocessing step to clean the data and ensure its quality. Then, a Convolutional Neural Network (CNN) is introduced to extract spatial features and patterns from the high-dimensional data, followed by a Bidirectional Long Short-Term Memory network (BiLSTM) to capture the temporal dependencies between various factors that affect traffic accidents. We also implement attention mechanisms to weigh the importance of each feature in the prediction, thereby reducing the impact of noisy or irrelevant data. To evaluate the effectiveness of our approach, we conduct experiments on a real-world traffic accident dataset from two cities. The results demonstrate the practicality and effectiveness of our framework for traffic accident severity prediction, with potential to enhance road safety.