# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 1 Introduction
Generative Adversarial Networks (GANs) have been applied to analyze multimodal trajectory distributions, as seen in GAN-VEEP [22] and ME-GAN [23]. Generally, deep learning approaches outperform traditional methods in terms of both accuracy and scalability. GCN-based Prediction [24] uses spatial and temporal graphs in connected environments, highlighting its advantage in cooperative driving scenarios. Attention-based GCN [25] introduces a novel use of vehicle-to-traffic rule interactions, offering enhanced prediction in complex environments such as intersections. The Feature-Boosting Network [26] emphasizes feature enhancement techniques, achieving remarkable gains in trajectory prediction accuracy through its novel high-dimensional space analysis. [PERSON] et al. introduced the STA-LSTM model incorporating an attention mechanism in 2022 [27]. While deep learning models generally achieve high prediction accuracy, they often face challenges in identifying key historical moments and influential neighboring vehicles. The STA-LSTM model's spatial-temporal attention mechanism highlights crucial trajectory points and evaluates the influence of surrounding vehicles, enhancing interpretability. However, the 1 s prediction horizon of the model constrains its practical applicability and raises potential safety concerns. Moreover, while the model enhances interpretability, this improvement does not result in a significant increase in accuracy when compared to more advanced models such as CS-LSTM. The grid-based spatial modeling approach, while simplifying computational processes, is insufficient for capturing the intricate interactions present in dynamic environments. To address these limitations, future work should consider incorporating advanced data structures, such as graph neural networks (GNNs). Moreover, the STA-LSTM model's predominant emphasis on highway scenarios restricts its applicability to diverse urban environments, necessitating further validation to ensure the model's accuracy in trajectory prediction on urban road networks. Accurate trajectory prediction must take into account both the temporal continuity of vehicle motion and the spatial influence of surrounding vehicles. Current research often neglects driver behavior variability, hindering personalized intelligent systems. This study establishes a road network using a fixed reference frame centered on the target vehicle, discretizing the surrounding road space into a 3 \(\times\) 13 grid (Figure 1). By integrating driver behavior variability, the model achieves long-term trajectory prediction while accounting for different driving styles in multi-vehicle scenarios. In this study, we developed an enhanced trajectory prediction model based on Long Short-Term Memory (LSTM) networks, named HTSA-LSTM. This model integrates spatiotemporal attention mechanisms and accounts for variations in driving behavior, specifically designed for long-term trajectory prediction in multi-vehicle interaction scenarios, predicting the potential positions a target vehicle may reach within the forecast horizon. The main contributions of this work are as follows:  
* Dynamic Spatiotemporal Attention Mechanism: This mechanism captures essential spatiotemporal dependencies between moving vehicles by integrating dynamic temporal and spatial attention. The model selectively identifies critical moments within historical traffic data and dynamically adjusts weight distributions according to real-time traffic conditions, enhancing its adaptability. This capability significantly improves prediction accuracy, especially in rapidly changing traffic environments, demonstrating the model's effectiveness in handling complex, evolving scenarios. * Driver Behavior Analysis Module: This module segments time-series driving data into behavioral units, enhancing prediction accuracy by incorporating historical trajectories.