# A CNN-LSTM-Attention Model for Near-Crash Event Identification on Mountainous Roads
## 1 Introduction  
Mountainous roads, characterized by their complex terrain and unpredictable weather conditions, consistently emerge as hotspots for traffic accidents [1]. From 2012 to 2016, over 15% of all traffic accidents in China occurred in such areas, with approximately 68% of these accidents resulted in severe injuries [2], highlighting the urgent need for enhanced road safety in mountainous areas. Similarly, in the United States, frequent traffic accidents have been observed in mountainous highway sections such as Colorado's I-70 [3]. To develop targeted improvements in traffic safety, researchers often rely on mountainous road traffic accident data for causation studies, examining the impact of driver behavior, surrounding traffic, and environmental conditions on accident occurrence [4, 5]. However, relying solely on accident data limits our understanding of the transition from normal driving conditions to accident-prone situations. While virtual simulation methods, such as micro-traffic simulations and driving simulators, offer insights into traffic dynamics, theireffectiveness is curtailed by the authenticity of the simulation environments. There are notable discrepancies between simulated and real driving conditions, leading to potential inaccuracies in safety assessments for mountainous roads. In contrast, naturalistic driving data [6] stands out for its ability to provide complete records of accidents, presenting a pivotal research direction for analyzing and preemptively addressing accidents on mountainous roads. Unlike other studies, naturalistic driving studies offer unobtrusive insights into drivers' interactions with complex road conditions, making them a critical tool for advancing road safety in challenging terrains [7]. Given the rarity of actual traffic accidents and the ethical concerns of detailed accident data analysis, existing studies often leverage near-crash events from naturalistic driving data as a viable alternative to real accidents [8; 9; 10]. The relationships between the influencing factors and traffic accidents can be inferred from their connections with the near-crash events. [PERSON] et al. [10] defined near-crash events as the ones that necessitate emergency maneuvers by drivers. They found that the events could occur 10-15 times more frequently than actual crashes. This higher frequency renders near-crash events more suitable for causation studies of traffic accidents, which demand substantial sample sizes for robust conclusions. The identification of near-crash events in naturalistic driving data are a critical step in this line of research. Current studies mainly focus on the correlations among variables in naturalistic driving data but often overlook their temporal dynamics. This gap is significant as simple machine learning or neural network models, which are commonly employed for identifying near-crash events, may not effectively capture crucial temporal information or fully understand variable interrelations. Consequently, this oversight can compromise the model's ability to accurately detect near-crash events. To fill this gap, this paper introduces an integrated framework for identifying near-crash events on mountainous roads using naturalistic driving data. The main contributions of this paper are summarized as follows. 1. A near-crash event identification process is designed using vehicle motion features such as velocity, longitudinal acceleration, and lateral acceleration. The identification process consists of multiple steps, including data noise filtering, near-crash event extraction and labeling, feature variable selection, and the construction and evaluation of the event identification model. 2. A multilevel deep learning-based near-crash event identification model is proposed. This model integrates the convolutional neural network (CNN), the long short-term memory (LSTM) network, and the attention mechanism.