## 3 Results and Discussion  
In this section, the proposed DPR-based EMS is evaluated in different stages. First, a standard cycle comprising mixed individual known driving cycles is classified with the DPR and the formation of the RDPs is verified. Next, test data collected from two different routes at two different timings are used to evaluate the accuracy of the model. The effect of increasing time frame on classification accuracy is also evaluated. As a next step, a sensitivity analysis is conducted by varying the thresholds of speed and acceleration. Moreover, the relative importance of features on the classification result is analyzed. Finally, the proposed DPR with the EMS is implemented under varying driving conditions. The simulation was performed on an Intel (R) Core (TM) system with i5-1155G7 at 2.50 GHz. The system had a 64-bit operating system, with a x64-based processor. For real-time implementation, two options of on-board (Edge AI) or off-board processing (Cloud AI) could be considered. Embedded GPUs (Graphics Processing Units) and higher RAM capacities are required for advanced deep learning models with 10-15 trillion operations per second. Cloud AI is useful in offloading heavy computations from the vehicle and is particularly useful in the optimization of high-performing neural networks. A mixed driving cycle is developed by combining individual standard driving cycles to verify the accuracy of RDPs and DPR processes. A training accuracy of 97.7% is achieved with the classification of unknown speed profiles into the known RDPs. In Figure 6, it can be observed that the mixed cycle comprising sections from standard cycles can be successfully classified into the respective target classes. Next, driving data collected over two test routes during early morning and evening, with specifications given in Table 4, are analyzed. The speed and acceleration over time show the variations in pattern due to road and traffic conditions, as shown in Figure 7. The positive and negative values indicate positive and negative acceleration, respectively. The accuracy of classification for both routes at the two timings are shown in Figure 8. The validation accuracy is slightly less than the training accuracy. The accuracy is higher for test route 1 than test route 2, mainly due to the fact that the speed variations are higher in test route 2. Moreover, with heavier traffic in the evening, the accuracy values are less than those in the morning. This can be solved by considering different time horizons or sampling intervals. Figure 8: Accuracy in % for morning and evening data for (**a**) test route 1 and (**b**) test route 2. Figure 6: Classification of mixed cycle into RDPs. Figure 7: Driving speed and acceleration during (**a**) test route 1 and (**b**) test route 2. The timing of data collection is essential for understanding the influence of traffic. The labels of test route \(1\) during evening and early morning hours are shown in Figure 9. As noted from the figure, the data during the evening have a higher percentage of high traffic regions and hence more times of occurrence of UHT. The data during the morning have more frequent occurrence of ULT. The sampling interval for acceleration data changed from 100 s to 10 s and then to 5 s. The corresponding values of training and validation accuracy are recorded in Table 5. An effect of varying the time horizon of the acceleration data can be observed. For the data from early morning, decreasing the time horizon from 100 s to 10 s led to significantly higher accuracy, but decreasing it from 10 s to 5 s did not bring much improvement. This is due to the fact that although analyzing shorter segments is useful in predicting traffic conditions, too much shortening does not lead to meaningful results. For the data from the evening, the improvement in accuracy due to a decrease in time horizon is more significant than in the case of the morning dataset. Decreasing the time horizon to 5 s, however, brings a slight improvement.