# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 4 Trajectory Prediction Based on Real-World Driving Data  
### Analysis of HTSA-LSTM Prediction Results Based on Real-World Driving Data  
This section validates the HTSA-LSTM model using real-world driving datasets. The experimental data were collected on roads primarily comprising two-way, four-lane, and six-lane sections, with some urban two-lane roads. Occasional on-ramps and off-ramps were present, but the experiment occurred during non-peak hours, with minimal traffic and no congestion. As a result, the analysis mainly focuses on straight four-lane road sections.  
A field experiment was conducted in Wuhan, China, yielding the \"real-world dataset\". Previous studies have indicated that factors such as driver gender, driving experience, and age significantly influence driving behavior [34]. To minimize the influence of variables like driver gender, experience, and age, 20 drivers (11 male, 9 female) aged 25 to 53 participated. Of these, 6 were taxi drivers and 14 were university staff or students, with driving experience ranging from 3 to 21 years and accumulated mileage between 10,000 and 450,000 km (avg. 113,000 km). The experiment took place on clear days, with drivers navigating urban roads, expressways, and highways. Drivers first completed a 10 km familiarization route, followed by driving a total of 1,800 km across different road types. A CANoe module employs high-speed sampling rates (125 kbps to 1 Mbps) and differential signaling to consolidate real-time data from various subsystems such as the engine, transmission, braking system, and steering system. These integrated data are then  
Figure 14: The trajectory predictions for vehicle 1585 and its neighboring vehicles.  
transmitted via the CANoe bus to a central data recorder or ECU, enabling data sharing among nodes and supporting real-time monitoring and control. To collect driving behavior data across different road types, this experiment selected urban roads (CRs), urban expressways (UEs), and highways (Hs) within the city as test road types, conducted under clear weather conditions and outside peak hours, as shown in Figure 15.  
The raw data, stored in 20 CSV files (1,524,108 rows, 82 columns, 556 MB), contained vehicle kinematics, position deviations, and distance/speed of nearby vehicles. Preprocessing techniques like cubic spline interpolation and Savitzky-Golay filtering were applied to address issues such as missing data, noise, and frame drops. After excluding anomalous data from four participants, the final analysis was based on data from 16 drivers.  
Table 4 presents the results of the HTSA-LSTM model on the real-world driving dataset. As observed, overall, the performance of the evaluation metrics decreases as the prediction horizon increases. For the same prediction horizon, the model's performance on urban city roads (CRs) is relatively poorer, with less stable prediction trends. This is primarily because, compared to urban expressways (UEs) and highways (Hs), CRs feature a greater variety of traffic participants (e.g., motor vehicles, non-motorized vehicles, pedestrians), higher density, and more frequent state changes. These factors increase the impact of surrounding traffic participants on the target vehicle's driving state and result in a higher probability of abrupt changes in driving conditions, leading to reduced trajectory prediction accuracy on CRs.  
On UEs and Hs, the overall driving conditions share many similarities, such as the predominance of motor vehicles and relatively lower vehicle density, which reduces the mutual influence among vehicles. Additionally, the time series of vehicle driving data on UEs and Hs tends to be more continuous and smoother, which is highly beneficial for accurate prediction of time-series characteristics. Consequently, the HTSA-LSTM model demonstrates strong performance when applied to UEs and Hs.