## 1 Introduction  
These days, most people use navigation apps such as _Google Maps, Waze_, and _Filtsmeister_ to find the best route for their trip due to the proliferation of smartphones and internet connectivity [1; 2]. These apps utilize the users' GPS data known as crowdsourced data to estimate and provide real-time travel information. Recently, these apps also enabled a feature where users can provide feedback and share their experiences; for instance, users can report traffic accidents and police speed checks. This information not only can help these apps predict future travel times more accurately but also can warn other app users about the conditions and help travelers adjust their routes or driving behaviors accordingly. The 3 Es approach, which comprises engineering, education, and enforcement, has been used for decades to improve road safety. The traffic safety level is expected to increase through educating people about safety knowledge and driving skills, as well as improving road infrastructure and vehicle standards. In addition, traffic enforcement can influence the behavior of high-risk drivers and is still an integral part of safety improvement. For instance, around 3 million traffic fines were issued in The Netherlands in the second half of 2021, of which 512,277 were issued by police flashing along the roads [3]. However, the report of police activity on navigation apps may nullify the impact of enforcement. Intuitively drivers who receive crowdsourced data from the app could potentially adjust their driving behavior, which otherwise may be different. This means that police activity reports in navigation apps could impact the driving speed in certain locations, and as a result, may influence the overall traffic condition. Traffic speed is one of the three imperative traffic parameters, which could reflect the traffic state and has become the key component of traffic prediction research. Forecasting traffic speed is of great significance for road users and urban managers, which could help to reasonably plan travel routes and manage urban traffic. Urban traffic speed prediction has always been one of the most challenging parts of intelligent transportation systems (ITSs) due to the complicated spatial and temporal correlations of urban road networks. Many predictive models have been proposed to capture the spatio-temporal dependence of traffic data, and their effectiveness has been proved using real traffic data [4; 5; 6]. Moreover, to increase the prediction accuracy, the influence of external factors, such as social attributes [7], accidents [8], weather [9], and point of interest (POI) [10] have been investigated. However, the impacts of external factors on traffic speed are unclear. In particular, the impact of crowdsourced data about police activity on traffic speed has not been investigated yet. Capturing such temporary impacts is important for traffic management because the induced speed changes at enforcement locations may impact the overall traffic situation. Therefore, the purpose of this research is to examine to what extent the reported police enforcement on navigation apps affects traffic speed and the accuracy of traffic speed prediction models. To achieve these goals, this research extracts police enforcement information from crowdsourced data from navigation apps in The Netherlands, collects historical traffic speed data on the same road segments where enforcement happened, and predicts the speed based on a deep learning prediction model. The impact of police speed checks reported in navigation apps on drivers' behaviors, as well as the accuracy of deep learning predictive models in the presence of police enforcement, are examined. Our analysis shows that the average actual traffic speed is 1-3 [km/h] lower compared to the predicted speed. In addition, the accuracy of the speed prediction model is lower during reported police enforcement periods than the periods without police enforcement. The rest of this paper is organized as follows. Section 2 presents the related literature.