Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-23T06:26:01.508Z Has data issue: false hasContentIssue false
  • English
  • Français

Matching Synthetic Populations with Personas: A Test Application for Urban Mobility

Published online by Cambridge University Press:  26 May 2022

F. Vallet ,
S. Hörl  and
T. Gall
F. Vallet
Affiliation:
CentraleSupélec, France IRT SystemX, France
S. Hörl
Affiliation:
IRT SystemX, France
T. Gall*
Affiliation:
CentraleSupélec, France IRT SystemX, France
*
[email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Design is increasingly influenced by digitalisation yet differs largely across domains. We present synergies between the works of UX designers and data scientists. We can utilise personas to represent users and their behaviours, or synthetic populations to represent agent groups. Despite sharing characteristics, their synergies have not been explored so far. We propose a workflow and test it in the urban mobility context to link a synthetic population of Paris with a set of contextual personas. This builds the basis for an integrated approach for designing urban mobility across fields.

Keywords

user-centred designdata-driven designinclusive designpersonasynthetic population
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1795 - 1804
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

References

Adlin, T. and Pruitt, J. (2010) The Essential Persona Lifecycle: Your Guide to Building and Using Personas. Morgan Kaufmann/Elsevier.Google Scholar
Al Maghraoui, O., Vallet, F., Puchinger, J. and Yannou, B. (2019) Modelling traveller experience for designing urban mobility systems, Design Science, 5, pp. 1–24. 10.1017/dsj.2019.6/Google Scholar
Anda, C., Ordonez Medina, S. A., and Axhausen, K. W. (2021) Synthesising digital twin travellers: Individual travel demand from aggregated mobile phone data. Transp. Res. Part C Emerg. Technol. 128, 103118. 10.1016/j.trc.2021.103118Google Scholar
Bornet, C. and Brangier, É. (2013) La méthode des personas : principes, intérêts et limites, Bulletin de psychologie, Numéro 524(2), p. 115. 10.3917/bupsy.524.0115/Google Scholar
Borysov, S. S., Rich, J. and Pereira, F.C. (2019) How to generate micro-agents? A deep generative modeling approach to population synthesis. Transp. Res. Part C Emerg. Technol. 106, pp. 73–97. 10.1016/j.trc.2019.07.006CrossRefGoogle Scholar
Cantamessa, M. et al. . (2020) Data-driven design: the new challenges of digitalization on product design and development, Design Science. 2020/11/05, 6, p. e27. 10.1017/dsj.2020.25/CrossRefGoogle Scholar
Cooper, A. (1999) The inmates are running the asylum. Macmillan. New York.Google Scholar
Durán-Heras, A., García-Gutiérrez, I. and Castilla-Alcalá, G. (2018) Comparison of Iterative Proportional Fitting and Simulated Annealing as synthetic population generation techniques: Importance of the rounding method. Comput. Environ. Urban Syst. 68, 78–88. 10.1016/j.compenvurbsys.2017.11.001CrossRefGoogle Scholar
Elioth, Egis Group (2017) Paris, an air of change. Towards carbon neutrality in 2050. [Online] https://paris2050.elioth.com/en/ [accessed 02 September 21]Google Scholar
Gargiulo, E., Giannantonio, R., Guercio, E., Borean, C. and Zenezini, G. (2015) Dynamic Ride Sharing Service: Are Users Ready to Adopt it? Procedia Manufacturing, 3, pp. 777–784. 10.1016/j.promfg.2015.07.329Google Scholar
Goodman-Deane, J., Bradley, M., Waller, S. and Clarkson, P. (2021) Developing Personas to help Designers to understand digital Exclusion. Proceedings of the Design Society, 1, pp. 1203–1212. 10.1017/pds.2021.120/CrossRefGoogle Scholar
Grudin, J. and Pruitt, J. (2002) Personas, Participatory Design and Product Development: An Infrastructure for Engagement, in Proceedings of Participation and Design Conference (PDC2002), Sweden, pp. 144–161.Google Scholar
Hermes, K. and Poulsen, M. (2012) A review of current methods to generate synthetic spatial microdata using reweighting and future directions. Comput. Environ. Urban Syst. 36, pp. 281–290. 10.1016/j.compenvurbsys.2012.03.005CrossRefGoogle Scholar
Hörl, S. and Balac, M. (2021) Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data. Transp. Res. Part C Emerg. Technol. 130, 103291. 10.1016/j.trc.2021.103291Google Scholar
Hörl, S., Balac, M. and Axhausen, K.W. (2019) Dynamic demand estimation for an AMoD system in Paris, in: 2019 IEEE Intelligent Vehicles Symposium (IV). Presented at the 2019 IEEE Intelligent Vehicles Symposium (IV), IEEE, Paris, France, pp. 260–266. 10.1109/IVS.2019.8814051CrossRefGoogle Scholar
Hörl, S., Becker, F. and Axhausen, K.W. (2021) Simulation of price, customer behaviour and system impact for a cost-covering automated taxi system in Zurich. Transp. Res. Part C Emerg. Technol. 123, 102974.CrossRefGoogle Scholar
INSEE (2021) Population suivant le sexe et la catégorie socio-professionnelle. Données annuelles de 2014 à 2020. [Online] https://www.insee.fr/fr/statistiques/2381478 [published 18 March 2021]Google Scholar
Joubert, J.W. and de Waal, A., 2020. Activity-based travel demand generation using Bayesian networks. Transp. Res. Part C Emerg. Technol. 120, 102804. 10.1016/j.trc.2020.102804Google Scholar
Kong, P., Cornet, H. and Frenkler, F. (2018). Personas and Emotional Design for Public Service Robots: A Case Study with Autonomous Vehicles in Public Transportation. 2018 International Conference on Cyberworlds (CW), pp. 284–287. 10.1109/CW.2018.00058Google Scholar
Le Bescond, V., Can, A., Aumond, P. and Gastineau, P. (2021) Open-source modelling chain for the dynamic assessment of road traffic noise exposure. Transp. Res. Part Transp. Environ. 94, 102793. 10.1016/j.trd.2021.102793CrossRefGoogle Scholar
Lu, J., Gomez Ortega, A., Gonçalves, M. and Bourgeois, J. (2021) The Impact of Data on the Role of Designers and their Process, Proceedings of the Design Society. 2021/07/27, 1, pp. 3021–3030. 10.1017/pds.2021.563/Google Scholar
Nguyen, J., Farrenkopf, T., Guckert, M., Powers, S. and Urquhart, N. (2021) Using Semantic Technology to Model Persona for Adaptable Agents. Proceedings of ECMS 2021, 35th ECMS International Conference on modelling and simulation. 10.7148/2021-0172/Google Scholar
Panos, E. and Margelou, S. (2019) Long-Term Solar Photovoltaics Penetration in Single- and Two-Family Houses in Switzerland. Energies 12, 2460. 10.3390/en12132460CrossRefGoogle Scholar
Ramadan, O. E. and Sisiopiku, P., V. (2020) A Critical Review on Population Synthesis for Activity- and Agent-Based Transportation Models, in: Luca, De, Di Pace, S., Djordjevic, R., B. (Eds.), Transportation Systems Analysis and Assessment. IntechOpen. 10.5772/intechopen.86307Google Scholar
Rasouli, S. and Timmermans, H. (2014) Activity-based models of travel demand: promises, progress and prospects. Int. J. Urban Sci. 18, pp. 31–60. 10.1080/12265934.2013.835118Google Scholar
Saadi, I., Farooq, B., Mustafa, A., Teller, J. and Cools, M. (2018) An efficient hierarchical model for multi-source information fusion. Expert Syst. Appl. 110, pp. 352–362. 10.1016/j.eswa.2018.06.018Google Scholar
Saadi, I., Mustafa, A., Teller, J., Farooq, B. and Cools, M. (2016) Hidden Markov Model-based population synthesis. Transp. Res. Part B Methodol. 90, pp. 1–21. 10.1016/j.trb.2016.04.007Google Scholar
Salminen, J., Santos, J. M. and Jung, S.-G. (2020) Persona Transparency: Analyzing the Impact of Explanations on Perceptions of Data-Driven Personas, International Journal of Human-Computer Interaction, 36(8), pp. 788–800. 10.1080/10447318.2019.1688946CrossRefGoogle Scholar
Schäfer, K., Rasche, P., Bröhl, C., Theis, S., Barton, L., Brandl, C., Wille, M., Nitsch, V. and Mertens, A. (2019) Survey-based personas for a target-group-specific consideration of elderly end users of information and communication systems in the German healthcare sector, International Journal of Medical Informatics, Vol. 132/103924, pp. 1–7. 10.1016/j.ijmedinf.2019.07.003Google Scholar
Stevenson, P., and Mattson, C. (2019) The Personification of Big Data. Proceedings of the Design Society: International Conference on Engineering Design, 1(1), pp. 4019–4028. 10.1017/dsi.2019.409/Google Scholar
Sun, L. and Erath, A. (2015) A Bayesian network approach for population synthesis. Transp. Res. Part C Emerg. Technol. 61, pp. 49–62. 10.1016/j.trc.2015.10.010Google Scholar
Vallet, F., Puchinger, J., Millonig, A., Lamé, G. and Nicolaï, I. (2020) Tangible futures: Combining scenario thinking and personas – A pilot study on urban mobility. Futures, Elsevier, 2020/117. 10.1016/j.futures.2020.102513Google Scholar
Yameogo, B.F., Vandanjon, P.-O., Gastineau, P. and Hankach, P. (2021) Generating a Two-Layered Synthetic Population for French Municipalities: Results and Evaluation of Four Synthetic Reconstruction Methods. J. Artif. Soc. Soc. Simul. 24, 5. 10.18564/jasss.4482CrossRefGoogle Scholar