# Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques
## 5 Results and Discussions
### Comprehensive Comparison across All Road Segments
#### 5.1.2 Long-Term Travel Time Prediction  
Table 4 also illustrates the average overall performance for long-term predictions spanning 1 to 7 days. The proposed SLIT consistently achieves the best results in long-term forecasting for both MAE and SMAPE metrics, demonstrating its robustness. Although PASS2S leads in RMSE, the proposed SLIT secures the second-best performance, further presenting its comprehensive predictive strength. Drawing on the strengths of PASS2S, which excels in utilizing attention mechanisms across periodic patterns, our design of SLIT extends this strategy, by enhancing the attention mechanism to cover a broader focus on both short-term and long-term data. This design is intended to allow SLIT to dynamically adjust its focus depending on the immediate and future relevance of the data, thereby providing a more accurate and comprehensive forecasting ability across different time horizons. Overall, SLIT demonstrates a significant advancement in modeling complex temporal relationships over extended horizons. The modest decrease in performance compared with the best-performing model demonstrates the challenges inherent in long-term forecasting. Despite the inherent complexities of long-term forecasting, SLIT maintains a remarkable performance, proving its versatility across varying forecasting horizons, a crucial attribute for ITS implementations where prediction needs can range widely.  
Delving deeper into the long-term prediction performance, Table 5 presents a day-by-day analysis over a 7-day period, revealing SLIT's robust performance in long-term traffic prediction. Specifically, SLIT consistently leads in MAE and SMAPE metrics across each day, proving its superiority compared with competitive methods. Its consistent performance in MAE across all 7 days demonstrates SLIT's ability to enhance the reliability of traffic forecasts. For RMSE, although SLIT does not always secure the top rank, its performance remains competitive, nearly matching or sometimes exceeding PASS2S. This tight competition in RMSE metrics shows SLIT's competency in maintaining prediction performance even as forecasting horizons extend. Furthermore, the SMAPE results across the week highlight SLIT's exceptional performance in ensuring consistently precise predictions. Achieving the lowest SMAPE values demonstrates SLIT's capability to provide consistent and reliable forecasts, which is crucial for planning and operational decision making in ITS applications.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Short-Term**} & \multicolumn{3}{c}{**Long-Term**} \\ \cline{2-7}  & **MAE** & **RMSE** & **SMAPE (\%)** & **MAE** & **RMSE** & **SMAPE (\%)** \\ \hline HA [28] & 35.036 & 75.516 & 6.408 & 34.516 & 74.000 & 6.304 \\ LSTM [29] & 23.626 & 62.972 & 4.210 & 31.520 & 74.062 & 5.743 \\ DNN [30] & 23.695 & 57.216 & 4.389 & 31.279 & 70.295 & 5.886 \\ DE-SLSTM [22] & 20.994 & 51.870 & 3.934 & 31.948 & 72.549 & 5.842 \\ MTSMFF [23] & 26.048 & 59.649 & 5.092 & 31.639 & 69.964 & 5.91 \\ DHM [8] & 19.591 & 52.872 & 3.712 & 33.281 & 71.896 & 6.245 \\ TFT [24] & 31.964 & 70.441 & 6.118 & 32.316 & 70.084 & 6.089 \\ PASS2S [5] & 20.381 & 50.250 & 3.860 & 28.929 & **66.695** & 5.373 \\ \hline SLIT & **17.697** & **45.628** & **3.391** & **28.270** & 67.794 & **5.194** \\ Improvement ratio (\%) & 9.67 & 9.20 & 8.66 & 2.28 & \(-\)1.65 & 3.33 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comprehensive comparison with competitive methods across short- and long-term prediction.