# Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques
## 5 Results and Discussions
### Performance Comparison on Road Segments of Varying Complexities  
To explore SLIT's effectiveness in diverse traffic scenarios, its performance was evaluated across road segments categorized by different levels of complexity. This complexity was measured based on the variability of travel times, as depicted in Figure 4. We divided road segments into three groups--High, Moderate, and Low Variability--according to their standard deviations (\(\sigma\)). Segments with \(\sigma\) greater than 100, termed High Variability, experience significant travel time fluctuations, often due to congestion, accidents, or adverse weather. Moderate Variability segments, defined by \(\sigma\) between 50 and 100, experience moderate fluctuations, while those with \(\sigma\) below 50, labeled Low Variability, indicate consistent and predictable travel times. Additionally, we consider the coefficient of variation (\(CV\)), which measures variability relative to the mean. This metric is calculated as the ratio of the standard deviation (\(\sigma\)) to the mean travel time (\(\mu\)), and is represented as follows: \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & **Method** & **1 Day** & **2 Days** & **3 Days** & **4 Days** & **5 Days** & **6 Days** & **7 Days** \\ \hline \multirow{8}{*}{**MAE**} & DE-SLSTM [22] & 30.154 & 32.390 & 32.138 & 31.442 & 32.297 & 33.285 & 31.926 \\  & MTSMFF [23] & 32.093 & 32.389 & 31.667 & 31.070 & 31.586 & 31.145 & 31.520 \\  & DHM [8] & 33.056 & 33.521 & 33.663 & 33.971 & 33.354 & 33.770 & 31.635 \\  & TFT [24] & 31.781 & 32.949 & 31.622 & 31.816 & 32.651 & 32.966 & 32.429 \\  & PASS2S [5] & 28.295 & 29.178 & 29.035 & 28.684 & 28.914 & 29.368 & 29.028 \\  & SLIT & **27.745** & **27.758** & **27.670** & **28.383** & **28.354** & **29.074** & **28.906** \\ \hline \multirow{8}{*}{**RMSE**} & DE-SLSTM [22] & 71.720 & 75.150 & 74.403 & 70.567 & 70.850 & 74.206 & 70.945 \\  & MTSMFF [23] & 71.692 & 71.533 & 69.482 & 69.014 & 70.718 & 69.537 & 67.771 \\ \cline{1-1}  & DHM [8] & 74.553 & 72.640 & 71.874 & 72.127 & 71.246 & 72.920 & 67.911 \\ \cline{1-1}  & TFT [24] & 70.021 & 72.044 & 69.479 & 69.227 & 70.322 & 71.335 & 68.161 \\ \cline{1-1}  & PASS2S [5] & **67.431** & **67.937** & 66.930 & **65.891** & **67.171** & **67.267** & **64.238** \\ \cline{1-1}  & SLIT & 68.808 & 68.299 & **65.951** & 68.345 & 68.044 & 69.401 & 65.711 \\ \hline \multirow{8}{*}{**SMAPE(\%)**} & DE-SLSTM [22] & 5.492 & 5.810 & 5.856 & 5.814 & 5.997 & 6.058 & 5.866 \\ \cline{1-1}  & MTSMFF [23] & 5.955 & 6.020 & 5.920 & 5.866 & 5.854 & 5.842 & 5.910 \\ \cline{1-1}  & DHM [8] & 6.154 & 6.311 & 6.322 & 6.422 & 6.256 & 6.297 & 5.950 \\ \cline{1-1}  & TFT [24] & 5.984 & 6.162 & 5.978 & 6.017 & 6.173 & 6.193 & 6.118 \\ \cline{1-1}  & PASS2S [5] & 5.238 & 5.367 & 5.423 & 5.341 & 5.359 & 5.451 & 5.433 \\ \cline{1-1}  & SLIT & **5.090** & **5.070** & **5.134** & **5.201** & **5.246** & **5.296** & **5.321** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with competitive methods in long-term prediction over 7-day horizons. \[CV=\frac{\sigma}{\mu} \tag{5}\]  
A higher \(CV\) value suggests a higher forecasting challenge due to increased unpredictability within a segment [5; 32]. Typically, High or Moderate Variability segments exhibit \(CV\) values exceeding 0.1, signifying the heightened challenge in forecasting their conditions as opposed to those in the Low Variability category. Figure 4 provides a visual representation of the \(\sigma\) and \(CV\) of travel times for each road segment, illustrating the different levels of variability and complexity across the categorized segments. This depiction clearly represents the variability range from high to low, offering a concise visual summary of the textual data presented. Table 6 illustrates the short-term prediction performance for different types of road segments. The proposed SLIT outperforms all competitive methods across complex, medium, and smooth road conditions, proving its robustness.