Towards a Reference Database for Pedestrian Destination Choice Model Development


  • Christopher King University of Bristol, Department of Engineering Mathematics, Bristol, UK
  • Nikolai Bode University of Bristol, Department of Engineering Mathematics, Bristol, UK



The move towards publishing research data openly has led to the formation of reference databases in many fields. The benefits of such resources are numerous, particularly in the development of models. While these exist in research on other aspects of pedestrian behaviour, no reference database is available for modelling pedestrian destination choice, the process by which pedestrians choose where they wish to visit next. This work seeks to construct such a database from the literature. The resulting data obtained are described and potential ways in which they could be used to calibrate a simple pedestrian destination choice model are presented. It contains four datasets that include destination choices for hundreds of pedestrians in settings ranging from university campuses and music festivals to highly structured stated preference surveys. A case study using one of these datasets to calibrate a simple pedestrian destination choice model is provided. These efforts highlight some general issues from creating and using reference data openly. Discussing these issues will hopefully guide the development of reference data and accelerate the development of accurate pedestrian destination choice models that can be applied generally.


Miller J., H.: Activity-Based Analysis. In: Fischer, M.M., Nijkamp, P. (eds.) Hand book of Regional Science, vol. 2, pp. 705–724. Springer Berlin Heidelberg, Berlin (2014). doi:10.1007/978-3-662-60723-7_106

Institute of Advanced Simulation: Pedestrian Dynamics Data Archive (2017). URL, accessed: 15/07/2022

Zhou, B., Tang, X., Wang, X.: Measuring Crowd Collectiveness. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR ’13, pp. 3049–3056. IEEE Computer Society, USA (2013). doi:10.1109/CVPR.2013.392

Zhou, B.: Collective Motion Database (2013). URL, accessed: 15/07/2022

Wang, L., Li, G., Lei, J., Wang, T., Zhang, Y.: Density-Based Manifold Collective Clustering for Coherent Motion Detection. In: Proceedings of the International Conference on Machine Vision and Applications, ICMVA 2018, pp.22–27. Association for Computing Machinery, New York, NY, USA (2018). doi:10.1145/3220511.3220521

Gao, M.L., Wang, Y.T., Jiang, J., Shen, J., Zou, G.F., Liu, L.N.: Crowd motion segmentation via streak flow and collectiveness. In: 2017 Chinese Automation Congress (CAC), pp. 4067–4070 (2017). doi:10.1109/CAC.2017.8243492

Liu, W., Chan, A.B., Lau, R.W.H., Manocha, D.: Leveraging Long-Term Predictions and Online Learning in Agent-Based Multiple Person Tracking. IEEE Transactions on Circuits and Systems for Video Technology 25(3), 399–410 (2015). doi:10.1109/TCSVT.2014.2344511. Conference Name: IEEE Transactions on Circuits and Systems for Video Technology

Lin, W., Mi, Y., Wang, W., Wu, J., Wang, J., Mei, T.: A Diffusion and Clustering-Based Approach for Finding Coherent Motions and Understanding Crowd Scenes. IEEE Transactions on Image Processing 25(4), 1674–1687 (2016). doi:10.1109/TIP.2016.2531281. Conference Name: IEEE Transactions on Image Processing

Wu, S., Yang, H., Zheng, S., Su, H., Fan, Y., Yang, M.H.: Crowd Behavior Analysis via Curl and Divergence of Motion Trajectories. International Journal of Computer Vision 123(3), 499–519 (2017). doi:10.1007/s11263-017-1005-y

Mueller, J.P., Massaron, L.: Machine Learning for Dummies. John Wiley & Sons, Incorporated, Hoboken, United States (2016). URL

Kotz, D., McDonald, C., Henderson, T., Gaughan, S., Proctor, P., Shubina, A., Yeo, J., Akestoridis, D.G., Cannon, H., Dimov, R., Jacobson, J., Johnson, S., Kelk, K., Malajian, D., Muckle-Jones, R., Neophytou, C., Pietikainen, P., Riina, A., Seletsky, O.: CRAWDAD. URL, accessed: 31/01/2022

Sarafraz, A.: Datasets (2020). URL, accessed: 31/01/2022

University College San Diego: Metabolomics Workbench. URL, accessed: 14/07/2022

Seyfried, A., Passon, O., Steffen, B., Boltes, M., Rupprecht, T., Klingsch, W.: New Insights into Pedestrian Flow Through Bottlenecks. Transportation Science 43(3), 395–406 (2009). doi:10.1287/trsc.1090.0263. Publisher: INFORMS

Godel, M., Bode, N.W.F., K ¨ oster, G., Bungatz, H.J.: Bayesian inference methods to calibrate crowd dynamics models for safety applications. Safety Science 147, 105586 (2022). doi:10.1016/j.ssci.2021.105586. Publisher: Elsevier

Cao, R.F., Lee, E.W.M., Yuen, A.C.Y., Chen, T.B.Y., De Cachinho Cordeiro, I.M., Shi, M., Wei, X., Yeoh, G.H.: Simulation of competitive and cooperative egress movements on the crowd emergency evacuation. Simulation Modelling Practice and Theory 109, 102309 (2021). doi:10.1016/j.simpat.2021.102309

Haghpanah, F., Ghobadi, K., Schafer, B.W.: Multi-hazard hospital evacuation planning during disease outbreaks using agent-based modeling. International Journal of Disaster Risk Reduction 66, 102632 (2021). doi:10.1016/j.ijdrr.2021.102632

Li, N., Guo, R.Y.: Simulation of bi-directional pedestrian flow through a bottleneck: Cell transmission model. Physica A: Statistical Mechanics and its Applications 555, 124542 (2020). doi:10.1016/j.physa.2020.124542

Zhuang, Y., Liu, Z., Schadschneider, A., Yang, L., Huang, J.: Exploring the behavior of self-organized queuing for pedestrian flow through a non-service bottleneck. Physica A: Statistical Mechanics and its Applications 562, 125186 (2021). doi:10.1016/j.physa.2020.125186

Li, H., Zhang, J., Yang, L., Song, W., Yuen, K.K.R.: A comparative study on the bottleneck flow between preschool children and adults under different movement motivations. Safety Science 121, 30–41 (2020). doi:10.1016/j.ssci.2019.09.002

Cao, S., Seyfried, A., Zhang, J., Holl, S., Song, W.: Fundamental diagrams for multidirectional pedestrian flows. Journal of Statistical Mechanics: Theory and Experiment 2017(3), 033404 (2017). doi:10.1088/1742-5468/aa620d. Publisher: IOP Publishing

Xu, Q., Chraibi, M., Seyfried, A.: Anticipation in a velocity-based model for pedestrian dynamics. Transportation Research Part C: Emerging Technologies 133, 103464 (2021). doi:10.1016/j.trc.2021.103464

Zhang, J., Klingsch, W., Schadschneider, A., Seyfried, A.: Transitions in pedestrian fundamental diagrams of straight corridors and T-junctions. Journal of Statistical Mechanics: Theory and Experiment 2011(06), P06004 (2011). doi:10.1088/1742-5468/2011/06/P06004. Publisher: IOP Publishing

Rathinakumar, K., Quaini, A.: A microscopic approach to study the onset of a highly infectious disease spreading. Mathematical Biosciences 329, 108475 (2020). doi:10.1016/j.mbs.2020.108475

Zhao, X., Zhang, J., Song, W.: A Radar-Nearest-Neighbor based data-driven approach for crowd simulation. Transportation Research Part C: Emerging Technologies 129, 103260 (2021). doi:10.1016/j.trc.2021.103260

Munafo, M.R., Nosek, B.A., Bishop, D.V.M., Button, K.S., Chambers, C.D., Percie du Sert, N., Simonsohn, U., Wagenmakers, E.J., Ware, J.J., Ioannidis, J.P.A.: A manifesto for reproducible science. Nature Human Behaviour 1(1), 1–9 (2017). doi:10.1038/s41562-016-0021. Number: 1 Publisher: Nature Publishing Group

Wilkinson, M.D., Dumontier, M., Aalbersberg, I.J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.W., da Silva Santos, L.B., Bourne, P.E., Bouwman, J., Brookes, A.J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C.T., Finkers, R., Gonzalez-Beltran, A., Gray, A.J.G., Groth, P., Goble, C., Grethe, J.S., Heringa, J., ’t Hoen, P.A.C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S.J., Martone, M.E., Mons, A., Packer, A.L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M.A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., Mons, B.: The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3(1), 160018 (2016). doi:10.1038/sdata.2016.18. Number: 1 Publisher: Nature Publishing Group

Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., Group, T.P.: Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLOS Medicine 6(7), e1000097 (2009). doi:10.1371/journal.pmed.1000097. Publisher: Public Library of Science

Wee, B.V., Banister, D.: How to Write a Literature Review Paper? Transport Reviews 36(2), 278–288 (2016). doi:10.1080/01441647.2015.1065456. Publisher: Routledge

Kitchenam, B., Charters, S.: Guidelines for performing Systematic Literature Reviews in software engineering. Technical, University of Durham, Durham, UK (2007). URL Systematic_Literature_Reviews_in_software_engineering_ EBSE_Technical_Report_EBSE-2007-01

Greenhalgh, T., Peacock, R.: Effectiveness and efficiency of search methods in systematic reviews of complex evidence: audit of primary sources. BMJ (Clinical research ed.) 331(7524), 1064–1065 (2005). doi:10.1136/bmj.38636.593461.68

Feng, Y., Duives, D.C., Daamen, W., Hoogendoorn, S.P.: Data collection methods for studying pedestrian behaviour: A systematic review. Building and Environment 187, 107329 (2021). doi:10.1016/j.buildenv.2020.107329

Seaborn, K., Miyake, N.P., Pennefather, P., Otake-Matsuura, M.: Voice in Human–Agent Interaction. ACM Computing Surveys 54(4), 1–43 (2022). doi:10.1145/3386867

Xu, M., Nie, X., Li, H., Cheng, J.C., Mei, Z.: Smart construction sites: A promising approach to improving on-site HSE management performance. Journal of Building Engineering 49, 104007 (2022). doi:10.1016/j.jobe.2022.104007

Jalali, S., Wohlin, C.: Systematic literature studies: Database searches vs. backward snowballing. In: Proceedings of the 2012 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, pp. 29–38. IEEE, Lund, Sweden (2012). doi:10.1145/2372251.2372257

Ying, F., Wallis, A.O.G., Beguerisse-D´ıaz, M., Porter, M.A., Howison, S.D.: Customer mobility and congestion in supermarkets. Physical Review E 100 (2019). doi:10.1103/PhysRevE.100.062304. ArXiv: 1905.13098

Arentze, T.A., Timmermans, H.J.P.: Deriving performance indicators from models of multipurpose shopping behavior. Journal of Retailing and Consumer Services 8(6), 325–334 (2001). doi:10.1016/S0969-6989(00)00038-2

Beaulieu, A., Farooq, B.: A dynamic mixed logit model with agent effect for pedestrian next location choice using ubiquitous Wi-Fi network data. International Journal of Transportation Science and Technology 8(3), 280–289 (2019). doi:10.1016/j.ijtst.2019.02.003

Ettema, D., Bastin, F., Polak, J., Ashiru, O.: Modelling the joint choice of activity timing and duration. Transportation Research Part A: Policy and Practice 41(9), 827–841 (2007). doi:10.1016/j.tra.2007.03.001

Hui, S.K., Bradlow, E.T., Fader, P.S.: Testing Behavioral Hypotheses Using an Integrated Model of Grocery Store Shopping Path and Purchase Behavior. Journal of Consumer Research 36(3), 478–493 (2009). doi:10.1086/599046

Danalet, A.: Activity choice modeling for pedestrian facilities. Ph.D. thesis, Swiss Federal Institute of Technology, Lausanne (2015). URL

Tinguely, L.: Exploiting pedestrian WiFi traces for destination choice modeling. Master’s thesis, EPFL, Lausanne, Switzerland (2015). URL

Vukadinovic, V., Dreier, F., Mangold, S.: A simple framework to simulate the mobility and activity of theme park visitors. In: Proceedings of the 2011 Winter Simulation Conference (WSC), pp. 3248–3260 (2011). doi:10.1109/WSC.2011.6148022

Yao, R., Bekhor, S.: Data-driven choice set generation and estimation of route choice models. Transportation Research Part C: Emerging Technologies 121, 102832 (2020). doi:10.1016/j.trc.2020.102832

Zhu, W., Timmermans, H.J.P.: Modeling pedestrian shopping behavior using principles of bounded rationality: model comparison and validation. Journal of Geographical Systems 13(2), 101–126 (2011). doi:10.1007/s10109-010-0122-8

Duives, D.C., Daamen, W., Hoogendoorn, S.P.: State-of-the-art crowd motion simulation models. Transportation Research Part C: Emerging Technologies 37, 193–209 (2013). doi:10.1016/j.trc.2013.02.005

Feliciani, C., Nishinari, K.: Estimation of pedestrian crowds’ properties using commercial tablets and smartphones. Transportmetrica B: Transport Dynamics 7(1), 865–896 (2019). doi:10.1080/21680566.2018.1517061. Publisher: Taylor & Francis

Voskamp, H.a.W.: Measuring the influence of congested bottleneck on route choice behavior of pedestrians at Utrecht Centraal. Master’s thesis, Delft University of Technology, Delft (2012). URL

Bigano, A., Hamilton, J.M., Tol, R.S.J.: The Impact of Climate on Holiday Destination Choice. Climatic Change 76(3), 389–406 (2006). doi:10.1007/s10584-005-9015-0

Karl, M., Reintinger, C., Schmude, J.: Reject or select: Mapping destination choice. Annals of Tourism Research 54, 48–64 (2015). doi:10.1016/j.annals.2015.06.003

Fotheringham, A.S.: Modelling Hierarchical Destination Choice. Environment and Planning A: Economy and Space 18(3), 401–418 (1986). doi:10.1068/a180401

Andion, J., Navarro, J.M., L ´ opez, G., ´ Alvarez Campana, M., Due ´ 34 nas, J.C.: Smart Behavioral Analytics over a Low-Cost IoT Wi-Fi Tracking Real Deployment. Wireless Communications and Mobile Computing 2018, e3136471 (2018). doi:10.1155/2018/3136471. Publisher: Hindawi

Raun, J., Ahas, R., Tiru, M.: Measuring tourism destinations using mobile tracking data. Tourism Management 57, 202–212 (2016). doi:10.1016/j.tourman.2016.06.006

Andresen, E., Chraibi, M., Seyfried, A.: A representation of partial spatial knowledge: a cognitive map approach for evacuation simulations. Transportmetrica A: Transport Science 14(5-6), 433–467 (2018). doi:10.1080/23249935.2018.1432717

Tong, Y., Bode, N.W.F.: Higher investment levels into pre-planned routes increase the adherence of pedestrians to them. Transportation Research Part F: Traffic Psychology and Behaviour 82, 297–315 (2021). doi:10.1016/j.trf.2021.07.019

Shao, W., Terzopoulos, D.: Autonomous Pedestrians. In: Proceedings of the 2005 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA ’05, pp. 19–28. ACM, New York, NY, USA (2005). doi:10.1145/1073368.1073371

Axhausen, K.W., Zimmermann, A., Schonfelder, S., Rindsf ¨ user, G., Haupt, T.: Observing the rhythms of daily life: A six-week travel diary. Transportation 29(2), 95–124 (2002). doi:10.1023/A:1014247822322

Shiftan, Y.: Practical Approach to Model Trip Chaining. Transportation Research Record 1645(1), 17–23 (1998). doi:10.3141/1645-03

Danalet, A., Farooq, B., Bierlaire, M.: A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures. Transportation Research Part C: Emerging Technologies 44, 146–170 (2014). doi:10.1016/j.trc.2014.03.015

Danalet, A., Bierlaire, M.: A path choice approach to activity modeling with a pedestrian case study. p. 45. Unpublished, Monte Verita, Ascona, Switzerland (2014). doi:10.13140/2.1.4099.8728

Danalet, A., Tinguely, L., Lapparent, M.d., Bierlaire, M.: Location choice with longitudinal WiFi data. Journal of Choice Modelling 18, 1–17 (2016). doi:10.1016/j.jocm.2016.04.003

Bonne, B., Barzan, A., Quax, P., Lamotte, W.: WiFiPi: Involuntary tracking of visitors at mass events. In: 2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks” (WoWMoM), pp. 1–6. IEEE, Madrid, Spain (2013). doi:10.1109/WoWMoM.2013.6583443

King, C., Bode, N.W.F.: A virtual experiment on pedestrian destination choice: the role of schedules, the environment and behavioural categories. Royal Society Open Science 9(7), 211982 (2022). doi:10.1098/rsos.211982

Train, K.: Discrete choice methods with simulation, 2nd ed edn. Cambridge University Press, Cambridge; New York (2009). URL OCLC: ocn349248337

Hoogendoorn, S.P., Bovy, P.H.L.: Pedestrian route-choice and activity scheduling theory and models. Transportation Research Part B: Methodological 38(2), 169–190 (2004). doi:10.1016/S0191-2615(03)00007-9

King, C., Koltsova, O., Bode, N.W.F.: Simulating the effect of measurement errors on pedestrian destination choice model calibration. Transportmetrica A: Transport Science pp. 1–41 (2022). doi:10.1080/23249935.2021.2017510. Publisher: Taylor & Francis

Kielar, P.M., Borrmann, A.: Modeling pedestrians’ interest in locations: A concept to improve simulations of pedestrian destination choice. Simulation Modelling Practice and Theory 61, 47–62 (2016). doi:10.1016/j.simpat.2015.11.003

Saarloos, D., Fujiwara, A., Zhang, J.: The Interaction between Pedestrians and Facilities in Central Business Districts: An Explorative Case Study. Journal of the Eastern Asia Society for Transportation Studies 7(0), 1870–1885 (2007). doi:10.11175/easts.7.1870. Publisher: Eastern Asia Society for Transportation Studies

Kwak, J., Jo, H.H., Luttinen, T., Kosonen, I.: Modeling Pedestrian Switching Behavior for Attractions. Transportation Research Procedia 2, 612–617 (2014). doi:10.1016/j.trpro.2014.09.102

Arentze, T.A., Ettema, D., Timmermans, H.J.P.: Location choice in the context of multi-day activity-travel patterns: model development and empirical results. Transportmetrica A: Transport Science 9(2), 107–123 (2013). doi:10.1080/18128602.2010.538870

Ettema, D., Borgers, A.W.J., Timmermans, H.J.P.: Simulation model of activity scheduling behavior. Transportation Research Record 1413, 1–11 (1993). URL

Fesenmaier, D.R.: Integrating activity patterns into destination choice models. Journal of Leisure Research 20(3), 175–191 (1988)

Kurose, S., Borgers, A.W.J., Timmermans, H.J.P.: Classifying Pedestrian Shopping Behaviour According to Implied Heuristic Choice Rules. Environment and Planning B: Planning and Design 28(3), 405–418 (2001). doi:10.1068/b2622

Ton, D.: Navistation: A study into the route and activity location choice behaviour of departing pedestrians in train stations. Ph.D. thesis, Delft University of Technology (2014)

van der Hagen, X., Borgers, A.W.J., Timmermans, H.J.P.: Spatiotemporal Sequencing Processes of Pedestrian in Urban Retail Environments. The Journal of the RSAI 70(1), 37–52 (1991). doi:10.1007/BF01463442

Zhu, W., Timmermans, H.J.P., De, W.: Temporal Variation in Consumer Spatial Behavior in Shopping Streets. Journal of Urban Planning and Development 132(3), 166–171 (2006). doi:10.1061/(ASCE)0733-9488(2006)132:3(166)

Fahmy, S.A., Alablani, B.A., Abdelmaguid, T.F.: Shopping center design using a facility layout assignment approach. In: 2014 9th International Conference on Informatics and Systems, pp. ORDS–1–ORDS–7. IEEE, Cairo, Egypt (2014). doi:10.1109/INFOS.2014.7036689

Yang, Y., Fik, T., Zhang, J.: Modeling Sequential Tourist Flows:Where is the Next Destination? Annals of Tourism Research 43, 297–320 (2013). doi:10.1016/j.annals.2013.07.005

Dijkstra, J., Timmermans, H.J.P., de Vries, B.: Activation of Shopping Pedestrian Agents—Empirical Estimation Results. Applied Spatial Analysis and Policy 6(4), 255–266 (2013). doi:10.1007/s12061-012-9082-3

Greenwood, D., Sharma, S., Johansson, A.: Mobility Modelling in a Process Constrained Environment: Modelling the Movements of Nurses in a Neonatal Intensive Care Unit. In: Chraibi, M., Boltes, M., Schadschneider, A., Seyfried, A. (eds.) Traffic and Granular Flow ’13, pp. 233–241. Springer International Publishing, Cham (2015). URL

R Core Team: R: A language and environment for statistical computing (2020). URL

Cisco Meraki: Understanding Wireless Performance and Coverage. Technical (2020). URL

Chilipirea, C., Dobre, C., Baratchi, M., Steen, M.v.: Identifying Movements in Noisy Crowd Analytics Data. In: 2018 19th IEEE International Conference on Mobile Data Management (MDM), pp. 161–166 (2018). doi:10.1109/MDM.2018.00033. ISSN: 2375-0324

Kern, W.: Solid Mensuration With Proofs (1938). URL

Vincenty, T.: Direct and Inverse Solutions of Geodesics on the Ellipsoid with Application of Nested Equations. Survey Review 23(176), 88–93 (1975). doi:10.1179/sre.1975.23.176.88. Publisher: Taylor & Franci

National Geospatial-intelligence Agency: NGA Geomatics - WGS 84. URL

Zhai, Y., Baran, P.K., Wu, C.: Spatial distributions and use patterns of user groups in urban forest parks: An examination utilizing GPS tracker. Urban Forestry & Urban Greening 35, 32–44 (2018). doi:10.1016/j.ufug.2018.07.014

Cover image




How to Cite

King, C., & Bode, N. (2023). Towards a Reference Database for Pedestrian Destination Choice Model Development. Collective Dynamics, 7, 1–39.