## 1 Introduction  
Crowd monitoring systems are maturing rapidly due to the fast advancement of sensor technologies. Wi-Fi sensors can be used to determine the activity patterns of pedestrians (e.g., [1; 2]) and measure the number of people in the vicinity of the Wi-Fi sensor (among other works [3]). Automated counting systems, such as real-time automatic video detection software in combination with cameras, radar and Lidar, are used to identify the amount of flow in certain corridors (e.g., [4; 5]). GPS trackers provide insights into the complete moment patterns of subset of the moving crowd (e.g., [6]). Recently, combinations of sensor are used to create a comprehensive overview of the movement patterns at large-scale events [7]. Even though crowd monitoring systems already provide much-needed insights into the unfolding of crowd movement dynamics, effective crowd management based on the information provided by these systems is difficult as this information comes in at the moment the adverse crowd movements are already occurring. That is, crowd monitoring systems do not yet provide enough response time to pre-emptively determine the perspective for action and implement the necessary crowd management solutions. In order to improve the effectivity and efficiency of crowd management, a method to forecast crowd movements in real-time based on real-time crowd monitoring information is wanted. Up to this moment, forecasting procedures to predict longer term crowd movements that do not rely on historic information regarding the crowds' movements on previous days are limited. Most presented methods either use a very small prediction horizon, e.g., [8], or extrapolate the crowds' movements patterns on previous days (e.g., [9]). In case of large-scale events, no historic information exists as the pedestrian infrastructure changes every year. Moreover, a forecasting horizon of at least 15 min is necessary in order to set-up and deploy the necessary intervention strategies. On the other hand, forecasting models with these two specific properties are often used to manage vehicular traffic. Both data-driven and model-driven solutions have been developed to forecast the flow of vehicles through large networks. The 'older' model-driven solutions use macroscopic models such as LWR and the cell transmission model. Data-driven solutions make use of smart filtering techniques (e.g., Kalman and Particle Filters) and machine learning techniques (e.g., AutoRegressive Integrated Moving Average (ARIMA) models, Markov Chain models and several types of neural networks, e.g., [10; 11; 12; 13; 14]). These procedures can possibly be adapted to also forecast pedestrian traffic flows. The objective of this paper is to develop a forecasting method that can predict pedestrian movements at large-scale events in real-time which is fed using GPS traces. A promising data-driven urban vehicular traffic forecasting technique, namely that of [15], is adapted in order to predict pedestrian movements at large-scale events based on GPS traces. This forecasting technique comprises of two steps. The first step comprises of a cell sequence derivation method proposed by [16] that codes the GPS traces. The second step entails a forecasting method. By means of a deep learning technique named Recursive Neural Network (RNN) with a Gated Recurrent Unit (GRU), the next location of a pedestrian is predicted using the available historic cell sequence. GPS traces of visitors of a large music festival in The Netherlands, named Mysteryland, are used to train and validate the two-step procedure. The real-time forecasting capability of the new forecasting procedure are tested using time-dependent data division strategies and more elaborate cell sequence derivation methods that incorporate temporal information captured in the GPS traces.