## 5 Conclusions and Future Research  
In recent years, using crowdsourced data for traffic management has become increasingly possible due to smart devices and internet connectivity. The report of police activity in navigation apps by users is an example of such crowdsourced data. The traffic speed prediction models have considered the effect of external factors, such as accidents, weather, and POI, to increase prediction accuracy. In this research, the potential impact of the crowdsourced data of police enforcement on traffic speed and the prediction accuracy of a speed prediction model using real data from The Netherlands were investigated. The analyses showed that in most cases, there are significant statistical differences between the predicted speed and actual speed when there exist police enforcement reports, and the actual average speed is lower than the predicted average speed by 1-3 [km/h] during the presence of a police enforcement report. This suggests that the drivers adjust their speed as a result of such reports on navigation apps, which can nullify the enforcement effort by police. In addition, we find that the report of police activity lessens the performance of the speed prediction model GCN-GRU. Existing models considered the influence of external factors, such as weather and accidents on traffic speed. However, the presence of police enforcement on a road segment also affects the performance of deep learning prediction models. Therefore, during the model training phase, it is necessary to incorporate the impact of external factors related to the reported police activity. Furthermore, the existence of crowdsourced police activity data allows drivers to be aware of the locations and times of police presence in advance. While this might somewhat diminish the effectiveness of law enforcement, such as reducing the number of traffic citations, it can encourage drivers to slow down in advance, which is also a safety-enhancing measure. A limitation of this study is selecting road segments that have both police activity reports as well as speed loop detectors. Most of the reported police enforcement takes place on rural or secondary roads, where few or no speed sensors, and consequently speed data, exist to conduct such investigations. Moreover, this study investigates the impact of police activity reported in one navigation app in The Netherlands. Hence, it is worth performing similar analyses in other locations and using data from more navigation apps to examine if the same results can be made. In addition, there is a limitation in performing traffic \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**With Police** & & & & & & & \\ \hline
**Groups** & **Count** & **Average** & **F** & \(p\)**-Value** & **MAE** & **RMSE** & **ACC** \\ \hline Prediction value & 45 & 97.97 & 5.2935 & 0.0238 & 2.3813 & 2.9230 & 0.9695 \\ Actual value & 45 & 95.88 & & & & & \\ \hline
**Without Police** & & & & & & & \\ \hline
**Groups** & **Count** & **Average** & **F** & \(p\)**-Value** & **MAE** & **RMSE** & **ACC** \\ \hline Prediction value & 84 & 97.54 & 0.1173 & 0.7324 & 1.7312 & 2.0463 & 0.9791 \\ Actual value & 84 & 97.82 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The test results of two lanes of the A2 highway for the north-to-south driving direction. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**With Police** & & & & & & & \\ \hline
**Groups** & **Count** & **Average** & **F** & \(p\)**-Value** & **MAE** & **RMSE** & **ACC** \\ \hline Prediction value & 46 & 92.46 & 91.36 & 5.5116 & 0.0211 & 2.1936 & 2.5764 & 0.9718 \\ Actual value & 46 & 91.36 & & & & & \\ \hline
**Without Police** & & & & & & & \\ \hline
**Groups** & **Count** & **Average** & **F** & \(p\)**-Value** & **MAE** & **RMSE** & **ACC** \\ \hline Prediction value & 59 & 92.86 & 0.6986 & 0.4050 & 1.6798 & 2.1959 & 0.9718 \\ Actual value & 59 & 93.15 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The test results of three lanes of the A2 highway for the north-to-south driving direction.