## 6 Conclusions  
This paper has tuned and enhanced an existing data-driven urban traffic forecasting technique presented by [15] and [16] to predict pedestrian movements at large-scale event terrains. Parameter tuning of the cell sequencing technique and cell sequence filtering algorithm ensured that the forecasting model can now also cope with the first three differences in the characteristics in the input. The extension of the time-dependent cell sequences facilitates the forecasting of pedestrians movements for 15 min or more ahead in real-time while no historic information on those movements is available. Using the cell derivation method first proposed by [16] GPS traces of pedestrian movements have been translated into individual cell sequences. These cell sequences were accordingly used to train and validate seven distinct model structures, namely three shallow neural networks, three Markov chain models of different orders and a RNN-GRU. The RNN-GRU model outperformed all other model structures. Accordingly, the RNN-GRU model was trained using three types of data division methods and three types of cell sequencing techniques. The results illustrated that the RNN-GRU preserves its predictive power when a limited amount of data is used from the first few hours of a \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Seq.** & **Division** & \multicolumn{3}{c}{**Error Rate (\(E^{m}\))**} & \multicolumn{3}{c}{**Average Error Distance (\(D^{m}\))**} \\
**Network** & **Method** & **Method** & **1** & **5** & **20** & **1** & **5** & **20** \\ \hline RNN-GRU & Consec & Sat/Sun & 0.2217 & 0.2411 & 0.2396 & 168.27 & 160.39 & 173.98 \\ RNN-GRU & Consec & M/E & 0.3532 & 0.3272 & 0.3648 & 131.96 & 144.64 & 131.57 \\ RNN-GRU & Consec & Random & 0.2798 & 0.3010 & 0.2698 & 106.57 & 117.56 & 112.96 \\ RNN-GRU & First & Sat/Sun & 0.2229 & 0.1862 & 0.1840 & 103.43 & 79.59 & 83.64 \\ RNN-GRU & First & M/E & 0.2043 & 0.2135 & 0.2186 & 87.64 & 96.64 & 96.68 \\ RNN-GRU & First & Random & 0.2909 & 0.2586 & 0.2450 & 128.44 & 108.41 & 97.30 \\ RNN-GRU & Longest & Sat/Sun & 0.1859 & 0.2245 & 0.2245 & 85.18 & 101.07 & 93.99 \\ RNN-GRU & Longest & M/E & 0.1952 & 0.1790 & 0.2180 & 85.27 & 81.89 & 94.67 \\ RNN-GRU & Longest & Random & 0.2302 & 0.2332 & 0.2372 & 107.21 & 102.53 & 102.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of cross-validation results for distinct sequencing methods and divisions between training and testing data sets. multi-day event. Moreover, the error in the prediction decreases when the length of the stay in a cell is taken into account. Consequently, this research concludes that using cell sequence data and a RNN-GRU crowd movements at large-scale events can be predicted without first map-matching the GPS trajectories. Moreover, the features of the crowd movements at these terrains allow this type of network to be trained while the event is ongoing. Consequently, by means of the cell sequence data the RNN-GRU can be trained and applied for real-time crowd movement prediction purposes. This paper has tested a specific set of model types for the purpose of cell sequence prediction. The RNN-GRU is known for its good memory retention qualities and the fact that neural networks can cope very well with the non-linear dependencies in traffic. However, the field of deep learning progresses rapidly and more types of deep learning networks with interesting features have seen the light. LSTM networks, and other RNNs with a more complex design, might improve the goodness-of-fit even further. Moreover, this work determines how to best predict the sequence of cells traversed by one individual in order to predict the aggregate features of the crowd, such as flow and density. Noise might be introduced in the process of aggregating the individual predictions. Therefore, it is essential to also study methods that can directly estimate the characteristics of the crowd movement at an aggregate level. Lastly, the data of one large-scale event in The Netherlands is used to train the RNN-GRU.