Spatial and temporal prediction of secondary crashes combining stacked sparse auto-encoder and long short-term memory

A novel attention-based cross-modal transfer learning framework for predicting cardiovascular disease

Cardiovascular disease (CVD) remains a leading cause of death globally, presenting significant challenges in early detection and treatment. The complexity of CVD arises from its multifaceted nature, influenced by a combination of genetic, environmental, and lifestyle factors. Traditional diagnostic approaches often struggle to effectively integrate and interpret the heterogeneous data associated with CVD. Addressing this challenge, we introduce a novel Attention-Based Cross-Modal (ABCM) transfer learning framework. This framework innovatively merges diverse data types, including clinical records, medical imagery, and genetic information, through an attention-driven mechanism. This mechanism adeptly identifies and focuses on the most pertinent attributes from each data source, thereby enhancing the model's ability to discern intricate interrelationships among various data types. Our extensive testing and validation demonstrate that the ABCM framework significantly surpasses traditional single-source models and other advanced multi-source methods in predicting CVD. Specifically, our approach achieves an accuracy of 93.5%, precision of 92.0%, recall of 94.5%, and an impressive area under the curve (AUC) of 97.2%. These results not only underscore the superior predictive capability of our model but also highlight its potential in offering more accurate and early detection of CVD. The integration of cross-modal data through attention-based mechanisms provides a deeper understanding of the disease, paving the way for more informed clinical decision-making and personalized patient care.

Dynamic short-term crash analysis and prediction at toll plazas for proactive safety management

Toll plazas are commonly recognized as bottlenecks on toll roads, where vehicles are prone to crashes. However, there has been a lack of research analyzing and predicting dynamic short-term crash risk specifically at toll plazas. This study utilizes traffic, geometric, and weather data to analyze and predict dynamic short-term collision occurrence probability at mainline toll plazas. A random-effects logit regression model is employed to identify crash precursors and assess their impacts on the probability of crash occurrence at toll plazas. Meanwhile, a Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) network is applied for crash prediction. The results of random-effects logit regression model indicate that the flow standard deviation of downstream, upstream occupancy, speed difference and occupancy difference between upstream and downstream positively influence the probability of crash occurrence. Conversely, an increase in the proportion of ETC lanes negatively impacts the probability of crash occurrence. Additionally, there appears a higher likelihood of crashes occurring during summer at toll plaza area. Furthermore, to address the issue of data imbalance, Synthetic Minority Oversampling Techniques (SMOTE) and class weight methods were employed. Stacked Sparse AutoEncoder-Long Short-Term Memory (SSAE-LSTM) and CatBoost were developed and their performance was compared with the proposed model. The results demonstrated that the LSTM-CNN model outperformed the other models in terms of the Area Under the Curve (AUC) values and the true positive rate. The findings of this study can assist engineers in selecting suitable traffic control strategies to improve traffic safety in toll plaza areas. Moreover, the developed collision prediction model can be incorporated into a real-time safety management system to proactively prevent traffic crash.

A spatio-temporal deep learning approach to simulating conflict risk propagation on freeways with trajectory data

On freeways, sudden deceleration or lane-changing by vehicles can trigger conflict risk that propagates backward in a specific pattern. Simulating this pattern of conflict risk propagation can not only help prevent crashes but is also vital for the deployment of advanced vehicle technologies. However, conflict risk propagation simulation (CRPS) on freeways is challenging due to the nuanced nature of the pattern, intricate spatio-temporal interdependencies among sequences and the high-resolution requirements. In this work, we introduce a conflict risk index to delineate potential conflict risk by aggregating various surrogate safety measures (SSMs) over time and space, and then propose a Spatio-Temporal Transformer Network (STTN) to simulate its propagation patterns. Multi-head attention mechanism and stacking layers enable the transformer to learn dynamic and hierarchical features in conflict risk sequences globally and locally. Two components, spatial and temporal learning transformers, are innovatively incorporated to extract and fuse these features, culminating in a fine-grained conflict risk inference. Comprehensive tests in real-world datasets verified the effectiveness of the STTN. Specifically, we employ three widely-recognized SSMs: Modified Time-To-Collision (MTTC), Proportion of Stopping Distance (PSD), and Deceleration Rate to Avoid a Collision (DRAC). These SSMs, gleaned from vehicle trajectories, are employed to delineate the conflict risk. Then, we conduct three comparative simulation tasks: MTTC-based model, PSD-based model, and DRAC-based model. Experimental results show that the PSD-based model exhibits a robust performance on all tasks, and is minimally affected by the durations of the simulation time, while the DRAC-based model more distinctly delineates the spatio-temporal conflict risk heterogeneity. Furthermore, we benchmark the STTN against three common state-of-the-art machine learning models across all simulation tasks. Results reveal that the STTN consistently surpassed these benchmark models (LSTM, CNN and ConvLSTM), suggesting the potential of the attention mechanism on the CRPS tasks. Our investigation offers crucial insights beneficial for traffic safety warning, advanced freeway management systems, and driver assistance systems, among others.