{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T05:49:50Z","timestamp":1772344190720,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T00:00:00Z","timestamp":1547769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"publisher","award":["669792"],"award-info":[{"award-number":["669792"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Currently, effective crowd management based on the information provided by crowd monitoring systems is difficult as this information comes in at the moment adverse crowd movements are already occurring. Up to this moment, very little forecasting techniques have been developed that predict crowd flows a longer time period ahead. Moreover, most contemporary state estimation methods apply demanding pre-processing steps, such as map-matching. The objective of this paper is to design, train and benchmark a data-driven procedure to forecast crowd movements, which can in real-time predict crowd movement. This procedure entails two steps. The first step comprises of a cell sequence derivation method that allows the representation of spatially continuous GPS traces in terms of discrete cell sequences. The second step entails the training of a Recursive Neural Network (RNN) with a Gated Recurrent Unit (GRU) and six benchmark models to forecast the next location of pedestrians. The RNN-GRU is found to outperform the other tested models. Some additional tests of the ability of the RNN-GRU to forecast illustrate that the RNN-GRU preserves its predictive power when a limited amount of data is used from the first few hours of a multi-day event and temporal information is incorporated in the cell sequences.<\/jats:p>","DOI":"10.3390\/s19020382","type":"journal-article","created":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T05:41:08Z","timestamp":1547790068000},"page":"382","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Forecasting Pedestrian Movements Using Recurrent Neural Networks: An Application of Crowd Monitoring Data"],"prefix":"10.3390","volume":"19","author":[{"given":"Dorine C.","family":"Duives","sequence":"first","affiliation":[{"name":"Transport &amp; Planning, Delft University of Technology, 2628 CN Delft, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangxing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, The University of Queensland, Brisbane St. Lucia, QLD 4072, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6380-3001","authenticated-orcid":false,"given":"Jiwon","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, The University of Queensland, Brisbane St. Lucia, QLD 4072, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Danalet, A., Bierlaire, M., and Farooq, B. (2014). Estimating Pedestrian Destinations Using Traces from WiFi Infrastructures. Pedestrian and Evacuation Dynamics 2012, Springer.","DOI":"10.1007\/978-3-319-02447-9_111"},{"key":"ref_2","unstructured":"Poucin, G., Farooq, B., and Patterson, Z. (2016, January 10\u201314). Pedestrian Activity Pattern Mining in WiFi-Network Connection Data. Proceedings of the 95th Annual Meeting Transportation Research Board, Washington, DC, USA."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Duives, D.C., Daamen, W., and Hoogendoorn, S.P. (2018, January 7\u201311). How to Measure Static Crowds? Monitoring the Number of Pedestrians at Large Open Areas by Means of Wi-Fi Sensors. Proceedings of the 97th Annual Meeting of the Transportation Research Board, Washington, DC, USA.","DOI":"10.1155\/2018\/7328074"},{"key":"ref_4","unstructured":"Yang, H., Ozbay, K., and Bartin, B. (2010, January 11\u201315). Investigating the Performance of Automatic Counting Sensors for Pedestrian Traffic Data Collection. Proceedings of the 12th World Conference on Transportation Research, Lisbon, Portugal."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kim, J.W., Choi, K.S., Choi, B.D., Lee, J.Y., and Ko, S.J. (2003). Real-Time System for Counting the Number of Passing People Using a Single Camera. Joint Pattern Recognition Symposium, Springer.","DOI":"10.1007\/978-3-540-45243-0_60"},{"key":"ref_6","unstructured":"Daamen, W., Kinkel, E., Duives, D., and Hoogendoorn, S.P. (2017, January 8\u201312). Monitoring Visitor Flow and Behaviour during a Festival: The Mysteryland Case Study. Proceedings of the 96th Annual Meeting of the Transportation Research Board, Washington, DC, USA."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Song, W., Ma, J., and Fu, L. (2016, January 17\u201321). Comparing Three Types of Real-Time Data Collection Techniques: Counting Cameras, Wi-Fi Sensors and GPS Trackers. Proceedings of the Pedestrian and Evacuation Dynamics 2016 (PED2016), Hefei, China.","DOI":"10.17815\/CD.2016.11"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Motsch, S., Moussa\u00efd, M.G., Guillot, E., Moreau, M., Pettr\u00e9, J., Theraulaz, G., Appert-Rolland, C., and Degond, P. (2018). Modeling Crowd Dynamics through Coarse-Grained Data Analysis. Math. Biosci. Eng.","DOI":"10.1101\/175760"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Toto, E., Rundensteiner, E.A., Li, Y., Jordan, R., Ishutkina, M., Claypool, K., Luo, J., and Zhang, F. (2016). PULSE: A Real Time System for Crowd Flow Prediction at Metropolitan Subway Stations. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.","DOI":"10.1007\/978-3-319-46131-1_19"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Herring, R., Hofleitner, A., Abbeel, P., and Bayen, A. (2010, January 19\u201322). Estimating Arterial Traffic Conditions Using Sparse Probe Data. Proceedings of the IEEE Conference on Intelligent Transportation Systems (ITSC), Funchal, Portugal.","DOI":"10.1109\/ITSC.2010.5624994"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Guo, J., and Williams, B. (2010). Real-Time Short-Term Traffic Speed Level Forecasting and Uncertainty Quantification Using Layered Kalman Filters. Transp. Res. Rec. J. Transp. Res. Board.","DOI":"10.3141\/2175-04"},{"key":"ref_12","unstructured":"Yu, G., Hu, J., Zhang, C., Zhuang, L., and Song, J. (2003, January 9\u201311). Short-Term Traffic Flow Forecasting Based on Markov Chain Model. Proceedings of the IEEE Intelligent Vehicles Symposium, Columbus, OH, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Chen, W., Wu, X., Chen, P.C.Y., and Liu, J. (2017). LSTM Network: A Deep Learning Approach for Short-Term Traffic Forecast. IET Intell. Transp. Syst.","DOI":"10.1049\/iet-its.2016.0208"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Thonhofer, E., Palau, T., Kuhn, A., Jakubek, S., and Kozek, M. (2018). Macroscopic Traffic Model for Large Scale Urban Traffic Network Design. Simul. Model. Pract. Theory.","DOI":"10.1016\/j.simpat.2017.09.007"},{"key":"ref_15","unstructured":"Choi, S., Yea, H., and Kim, J. (2018, January 7\u201311). Network-Wide Vehicle Trajectory Prediction in Urban Traffic Networks Using Deep Learning. Proceedings of the 97th Annual meeting of the Transportation Research Board, Washington, DC, USA."},{"key":"ref_16","unstructured":"Kim, J., Zheng, J., Corcoran, J., Ahn, S., and Papamanolis, M. (2017, January 8\u201312). Trajectory Flow Map: Graph-Based Approach to Analysing Temporal Evolution of Aggregated Traffic Flows in Large-Scale Urban Networks. Proceedings of the 96th Annual meeting of the Transportation Research Board, Washington, DC, USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Blue, V.J., and Adler, J.L. (1999). Using Cellular Automata. Microsimulation to Model Pedestrian Movement, Elsevier Science Ltd.","DOI":"10.3141\/1678-17"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4282","DOI":"10.1103\/PhysRevE.51.4282","article-title":"Social Force Model for Pedestrian Dynamics","volume":"51","author":"Helbing","year":"1995","journal-title":"Phys. Rev. Part E"},{"key":"ref_19","first-page":"665","article-title":"Pedestrian Reactive Navigation for Crowd Simulation: A Predictive Approach","volume":"Volume 26","author":"Slavik","year":"2007","journal-title":"Eurographics 2007, Computer Graphics Forum"},{"key":"ref_20","first-page":"2755","article-title":"Experimental Study of the Behavioural Mechanisms Underlying Self-Organization in Human Crowds","volume":"276","author":"Moussaid","year":"2009","journal-title":"Proc. R. Soc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/S0191-2615(01)00015-7","article-title":"A Continuum Theory for the Flow of Pedestrians","volume":"36","author":"Hughes","year":"2002","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1142\/S0218202508003054","article-title":"On the Modelling Crowd Dynamics from Scaling to Hyperbolic Macroscopic Models","volume":"18","author":"Bellomo","year":"2008","journal-title":"Math. Model. Methods Appl. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1016\/j.physa.2014.07.050","article-title":"Continuum Modelling of Pedestrian Flows: From Microscopic Principles to Self-Organised Macroscopic Phenomena","volume":"416","author":"Hoogendoorn","year":"2014","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"H\u00e4nseler, F.S., Bierlaire, M., Farooq, B., and M\u00fchlematter, T. (2014). A Macroscopic Loading Model for Time-Varying Pedestrian Flows in Public Walking Areas. Transp. Res. Part B Methodol.","DOI":"10.1016\/j.trb.2014.08.003"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.jcp.2012.11.033","article-title":"Self-Organized Hydrodynamics with Congestion and Path Formation in Crowds","volume":"237","author":"Degond","year":"2013","journal-title":"J. Comput. Phys."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Asahara, A., Maruyama, K., Sato, A., and Seto, K. (2011, January 1\u20134). Pedestrian-Movement Prediction Based on Mixed Markov-Chain Model. Proceedings of the 19th International Conference on Advances in Geographic Information Systems (GIS \u201911), Chicago, IL, USA.","DOI":"10.1145\/2093973.2093979"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Goldhammer, M., Doll, K., Brunsmann, U., Gensler, A., and Sick, B. (2014, January 24\u201328). Pedestrian\u2019s Trajectory Forecast in Public Traffic with Artificial Neural Networks. Proceedings of the International Conference on Pattern Recognition, Stockholm, Sweden.","DOI":"10.1109\/ICPR.2014.704"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-fei, L., and Savarese, S. (2016, January 27\u201330). Social LSTM: Human Trajectory Prediction in Crowded Spaces. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bera, A., Kim, S., Randhavane, T., Pratapa, S., and Manocha, D. (2016, January 16\u201321). GLMP\u2014Realtime Pedestrian Path Prediction Using Global and Local Movement Patterns. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487768"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.trc.2014.03.015","article-title":"A Bayesian Approach to Detect Pedestrian Destination\u2014Sequences from Wi-Fi Signatures","volume":"44","author":"Danalet","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jocm.2016.04.003","article-title":"Location Choices with Longitudinal Wi-Fi Data","volume":"18","author":"Danalet","year":"2016","journal-title":"J. Choice Model."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Prasad, P., and Agrawal, P. (2009, January 15\u201317). Mobility Prediction for Wireless Network Resource Management. Proceedings of the 41st Southeastern Symposium on System Theory, Tullahoma, TN, USA.","DOI":"10.1109\/SSST.2009.4806853"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Prasad, P., and Agrawal, P. (2010, January 9\u201312). Movement Prediction in Wireless Networks Using Mobility Traces. Proceedings of the IEEE 2010 7th IEEE Consumer Communications and Networking Conference (CCNC 2010), Las Vegas, NV, USA.","DOI":"10.1109\/CCNC.2010.5421613"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.trc.2018.05.013","article-title":"Link-Based Measurement Model to Estimate Route Choice Parameters in Urban Pedestrian Networks","volume":"93","author":"Oyama","year":"2018","journal-title":"Transp. Res. Part C"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kalman, R.E. (1960). A New Approach to Linear Filtering and Prediction Problems. J. Basic Eng.","DOI":"10.1115\/1.3662552"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Okutani, I., and Stephanedes, Y.J. (1984). Dynamic Prediction of Traffic Volume through Kalman Filtering Theory. Transp. Res. Part B.","DOI":"10.1016\/0191-2615(84)90002-X"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/j.proeng.2017.04.417","article-title":"Traffic Flow Prediction Using Kalman Filtering Technique","volume":"187","author":"Kumar","year":"2017","journal-title":"Procedia Eng."},{"key":"ref_38","unstructured":"Tamp\u00e8re, C.M.J., and Immers, L.H. (October, January 30). An Extended Kalman Filter Application for Traffic State Estimation Using CTM with Implicit Mode Switching and Dynamic Parameters. Proceedings of the IEEE Conference on Intelligent Transportation Systems (ITSC), Seattle, WA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, H., and Rakha, H.A. (2014). Real-Time Travel Time Prediction Using Particle Filtering with a Non-Explicit State-Transition Model. Transp. Res. Part C Emerg. Technol.","DOI":"10.1016\/j.trc.2014.02.008"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sau, J., El Faouzi, N., Aissa, A.B., and de Mouzon, O. (2007). Particle Filter-Based Real-Time Estimation and Prediction of Traffic Conditions. Recent Advances in Stochastic Modeling and Data Analysis, World Scientific.","DOI":"10.1142\/9789812709691_0049"},{"key":"ref_41","unstructured":"Lin, W. (2001, January 25\u201329). A Gaussian Maximum Likelihood Formulation for Short-Term Forecasting of Traffic Flow. Proceedings of the 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585), Oakland, CA, USA."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer-Verlag.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ma, J., Theiler, J., and Perkins, S. (2003). Accurate On-Line Support Vector Regression. Neural Comput.","DOI":"10.1162\/089976603322385117"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Castro-Neto, M., Jeong, Y.S., Jeong, M.K., and Han, L.D. (2009). Online-SVR for Short-Term Traffic Flow Prediction under Typical and Atypical Traffic Conditions. Expert Syst. Appl.","DOI":"10.1016\/j.eswa.2008.07.069"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Qi, Y., and Ishak, S. (2014). A Hidden Markov Model for Short Term Prediction of Traffic Conditions on Freeways. Transp. Res. Part C Emerg. Technol.","DOI":"10.1016\/j.trc.2014.02.007"},{"key":"ref_46","first-page":"98","article-title":"Short-Term Traffic Prediction: Neural Network Approach","volume":"1453","author":"Smith","year":"1994","journal-title":"Transp. Res. Rec."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"116","DOI":"10.3141\/2024-14","article-title":"Adaptive Seasonal Time Series Models for Forecasting Short-Term Traffic Flow","volume":"2024","author":"Shekhar","year":"2014","journal-title":"Transp. Res. Rec."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ma, X., Tao, Z., Wang, Y., Yu, H., and Wang, Y. (2015). Long Short-Term Memory Neural Network for Traffic Speed Prediction Using Remote Microwave Sensor Data. Transp. Res. Part C Emerg. Technol.","DOI":"10.1016\/j.trc.2015.03.014"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Fu, R., Zhang, Z., and Li, L. (2017, January 19\u201321). Using LSTM and GRU Neural Network Methods for Traffic Flow Prediction. Proceedings of the 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC 2016), Hefei, China.","DOI":"10.1109\/YAC.2016.7804912"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhou, M., Qu, X., and Li, X. (2017). A Recurrent Neural Network Based Microscopic Car Following Model to Predict Traffic Oscillation. Transp. Res. Part C Emerg. Technol.","DOI":"10.1016\/j.trc.2017.08.027"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Adrienko, N., and Andrienko, G. (2011). Spatial Generalization and Aggregation of Massive Movement Data. IEEE Trans. Vis. Comput. Graph.","DOI":"10.1109\/TVCG.2010.44"},{"key":"ref_52","unstructured":"Kingma, D.P., and Ba, J. (arXiv, 2015). Adam: A Method for Stochastic Optimization, arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/2\/382\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:27:02Z","timestamp":1760185622000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/2\/382"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,18]]},"references-count":52,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["s19020382"],"URL":"https:\/\/doi.org\/10.3390\/s19020382","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,18]]}}}