# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 5 Conclusions  
Driving style analysis and vehicle trajectory prediction are critical for advancing intelligent vehicle technologies. This study examines variations in driving styles and introduces an innovative trajectory prediction model, HTSA-LSTM. The model is based on Long Short-Term Memory (LSTM) networks, integrating spatiotemporal attention mechanisms and driver behavior characteristics to achieve high-precision, long-term predictions. The key findings of this research are summarized as follows:  
The study focuses on long-term vehicle trajectory prediction in multi-vehicle interaction scenarios. The HTSA-LSTM model incorporates a spatiotemporal attention mechanism to capture the complex spatiotemporal dependencies between vehicles. In addition to considering the continuity of vehicle motion and the influence of neighboring vehicles, the model segments driving data into driving primitives to analyze individual driving preferences, enabling precise future trajectory prediction. A 3 \(\times\) 13 road grid structure was employed to represent the trajectories of the target vehicle and surrounding vehicles, and temporal and spatial attention weights were analyzed to identify key influential factors. Figure 17: Prediction results for speed and acceleration on urban expressways (UEs). Figure 18: Prediction results for speed and acceleration on highways (Hs). Trained and tested on the NGSIM US101 dataset, the model achieved a 20.72% reduction in RMSE and a 24.98% reduction in NLL for 5 s predictions compared to baseline models. The real-world applicability of HTSA-LSTM was validated using driving datasets, achieving over 97.9% accuracy for 5 s predictions on urban expressways (UEs) and highways (Hs), and 92.7% accuracy for 3 s predictions on city roads (CRs). Compared to STA-LSTM, HTSA-LSTM demonstrated superior prediction accuracy, especially in long-term forecasts (e.g., 6 s), with significantly lower RMSE and NLL metrics. This indicates that HTSA-LSTM exhibits greater robustness and accuracy in dynamic driving scenarios, showing clear advantages in handling vehicle interactions and complex environments, making it a promising solution for intelligent driving applications. Although the HTSA-LSTM model has achieved good results in highway environments, its performance on urban roads still has room for improvement due to the more diverse traffic behaviors and complex vehicle-road interactions present in urban settings. Future work could consider incorporating advanced techniques such as multi-scale features, contextual information, and graph neural networks (GNNs) to more effectively model the complex traffic interactions in urban areas. While ensuring strong performance, we focus on the model's generalization ability across diverse traffic scenarios and investigate the impact of training data selection on its robustness and generalization performance. By integrating long-term trend analysis with real-time data--such as traffic density, road conditions, and meteorological information--our model not only predicts regular congestion patterns, capacity changes, and the impacts of special events but also rapidly adapts to current environmental conditions even in unfamiliar scenarios. This combined approach, however, increases data preprocessing requirements and computational complexity, necessitating research into efficient multimodal fusion strategies and online updating methods to balance real-time performance with prediction accuracy. Moreover, the current framework's omission of dynamic conditions like fluctuating traffic density, sudden road closures, and adverse weather, which can alter spatial-temporal correlations and driver behaviors, may limit its adaptability. Future enhancements will focus on dynamic data fusion within the spatial-temporal attention module--leveraging adaptive weight adjustments (e.g., using traffic density for grid aggregation and gating mechanisms for road closures) and integrating weather data into the driving style analysis layer--to better capture complex, real-world scenarios.