# Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques
## 6 Conclusions  
This study addresses predicting travel times for both short-term and long-term intervals, a key requirement in Intelligent Transportation Systems (ITSs). We propose the Short-Term and Long-Term Integrated Transformer (SLIT) model in this field, harmonizing the capabilities of Enhanced Data Preprocessing (EDP) with the Short-Term and Long-Term Integrated Encoder-Decoder (SLIED). This combination enables SLIT to effectively handle the intricacies of traffic data across varied time horizons, effectively tackling the challenges of traffic prediction across different time horizons. Extensive evaluations on a large-scale real-world traffic dataset demonstrate the robust capabilities of SLIT compared with existing competitive methods in both short-term and long-term travel time \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**High**} & \multicolumn{3}{c}{**Moderate**} & \multicolumn{3}{c}{**Low**} \\ \cline{2-10}  & **MAE** & **RMSE** & **SMAPE (\%)** & **MAE** & **RMSE** & **SMAPE (\%)** & **MAE** & **RMSE** & **SMAPE (\%)** \\ \hline HA [28] & 68.907 & 141.653 & 10.191 & 25.554 & 68.178 & 6.935 & 13.130 & 25.293 & 2.903 \\ LSTM [29] & 46.336 & 117.84 & 6.619 & 17.772 & 58.566 & 4.637 & 8.604 & 20.183 & 1.918 \\ DNN [30] & 45.047 & 100.781 & 6.739 & 20.179 & 60.147 & 5.305 & 8.245 & 18.959 & 1.819 \\ DE-SLSTM [22] & 38.638 & 87.794 & 5.849 & 18.308 & 57.005 & 4.776 & 8.081 & 18.510 & 1.777 \\ MTSMFF [23] & 44.152 & 97.656 & 6.939 & 22.695 & 62.921 & 6.051 & 13.198 & 25.795 & 2.912 \\ DHM [8] & 34.706 & 88.262 & 5.254 & 17.805 & 57.428 & 4.608 & 8.185 & 20.343 & 1.830 \\ TFT [24] & 57.547 & 124.223 & 8.868 & 25.550 & 68.055 & 6.912 & 14.919 & 27.214 & 3.297 \\ PASS2S [5] & 37.125 & 82.990 & 5.697 & 17.847 & 55.799 & 4.648 & 8.118 & 19.268 & 1.806 \\ \hline  & **MAE** & **RMSE** & **SMAPE (\%)** & **MAE** & **RMSE** & **SMAPE (\%)** & **MAE** & **RMSE** & **SMAPE (\%)** \\ \hline SLIT & **31.454** & **74.005** & **4.852** & **15.928** & **52.754** & **4.149** & **7.412** & **17.229** & **1.667** \\ Improvement ratio (\%) & 9.37 & 10.83 & 7.65 & 10.38 & 5.46 & 9.96 & 8.28 & 6.92 & 6.19 \\ \hline \hline \end{tabular}
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Table 6: Comparison of short-term prediction results on different types of road segments against competitive methods. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**High**} & \multicolumn{3}{c}{**Moderate**} & \multicolumn{3}{c}{**Low**} \\ \cline{2-10}  & **MAE** & **RMSE** & **SMAPE (\%)** & **MAE** & **RMSE** & **SMAPE (\%)** & **MAE** & **RMSE** & **SMAPE (\%)** \\ \hline HA [28] & 67.888 & 140.256 & 10.006 & 24.913 & 63.929 & 6.788 & 13.107 & 25.501 & 2.896 \\ LSTM [29] & 60.085 & 140.170 & 8.734 & 23.520 & 63.769 & 6.311 & 13.049 & 25.833 & 2.873 \\ DNN [30] & 59.056 & 129.579 & 8.976 & 25.511 & 63.939 & 6.899 & 11.977 & 25.129 & 2.637 \\ DE-SLSTM [22] & 61.200 & 135.985 & 8.970 & 23.805 & 63.383 & 6.395 & 12.999 & 25.796 & 2.867 \\ MTSMFF [23] & 58.257 & 126.716 & 8.675 & 24.828 & 64.113 & 6.706 & 13.997 & 26.571 & 3.074 \\ DHM [8] & 61.833 & 130.079 & 9.316 & 25.607 & 64.545 & 6.985 & 14.604 & 28.311 & 3.191 \\ TFT [24] & 59.128 & 126.357 & 8.880 & 25.252 & 63.877 & 6.858 & 14.683 & 27.328 & 3.250 \\ PASS2S [5] & 54.747 & **121.140** & 8.217 & 22.404 & 61.597 & 5.996 & 11.764 & 24.723 & 2.587 \\ \hline SLIT & **53.264** & 124.927 & **7.809** & **22.132** & **61.202** & **5.895** & **11.534** & **24.578** & **2.547** \\ Improvement ratio (\%) & 2.71 & \(-\)3.13 & 4.97 & 1.22 & 0.64 & 1.70 & 1.95 & 0.59 & 1.55 \\ \hline \hline \end{tabular}
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Table 7: Comparison of long-term prediction results on different types of road segments against competitive methods. predictions. Remarkable improvements are observed in SLIT, with enhancements of up to 9.67%, 9.2%, and 8.66% in MAE, RMSE, and SMAPE metrics, respectively, for short-term forecasting.