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DESIGNING A DATA VISUALISATION AND ANALYSIS TOOL FOR SUPPORTING DECISION-MAKING WITH PUBLIC TRANSPORTATION NETWORK

Published online by Cambridge University Press:  27 July 2021

Flore Vallet*
Affiliation:
Université Paris-Saclay, CentraleSupélec, Laboratoire Genie Industriel, France IRT SystemX, Paris-Saclay, France
Mostepha Khouadjia
Affiliation:
IRT SystemX, Paris-Saclay, France
Ahmed Amrani
Affiliation:
IRT SystemX, Paris-Saclay, France
Juliette Pouzet
Affiliation:
SNCF Innovation & Research, France
*
Vallet, Flore, IRT SystemX, Territoires Intelligents, France, flore.vallet@irt-systemx.fr

Abstract

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Massive data are surrounding us in our daily lives. Urban mobility generates a very high number of complex data reflecting the mobility of people, vehicles and objects. Transport operators are primary users who strive to discover the meaning of phenomena behind traffic data, aiming at regulation and transport planning. This paper tackles the question "How to design a supportive tool for visual exploration of digital mobility data to help a transport analyst in decision making?” The objective is to support an analyst to conduct an ex post analysis of train circulation and passenger flows, notably in disrupted situations. We propose a problem-solution process combined with data visualisation. It relies on the observation of operational agents, creativity sessions and the development of user scenarios. The process is illustrated for a case study on one of the commuter line of the Paris metropolitan area. Results encompass three different layers and multiple interlinked views to explore spatial patterns, spatio-temporal clusters and passenger flows. We join several transport network indicators whether are measured, forecasted, or estimated. A user scenario is developed to investigate disrupted situations in public transport.

Type
Article
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), 2021. Published by Cambridge University Press

References

Abi Akle, A., Minel, S. & Yannou, B. (2017), “Information visualization for selection in Design by Shopping”, Res Eng Design, Vol. 28, pp. 99117. https://doi.org/10.1007/s00163-016-0235-2CrossRefGoogle Scholar
Amrani, A., Pasini, K., Khouadjia, M. (2020), “Enhance Journey Planner with Predictive Travel Information for Smart City Routing Services”, Forum on Integrated and Sustainable Transportation Systems 2020, Delft.Google Scholar
Andrienko, G., Andrienko, N., Chen, W., Maciejewski, R. and Zhao, Y. (2017), “Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions”, IEEE Transactions on Intelligent Transportation Systems, pp.99, https://dx.doi.org/10.1109/TITS.2017.2683539CrossRefGoogle Scholar
Barry, M, Card, B. (2014), “Visualizing MBTA Data - An interactive exploration of Boston's subway system”, Available at http://mbtaviz.github.io/#trains (Accessed 01/09/19).Google Scholar
Beghelli, A., Huerta-Cánepa, G. and Segal, R. (2019), “Data Materialisation: A New Undergraduate Course for a Data Driven Society”, Proceedings of the Design Society: International Conference on Engineering Design. Cambridge University Press, Vol. 1(1), pp. 20612070. https://dx.doi.org/10.1017/dsi.2019.212.CrossRefGoogle Scholar
Chandola, V, Banerjee, A, Kumar, V (2009) Anomaly detection: A survey. ACM computing surveys (CSUR) 41(3), pp.1-58.10.1145/1541880.1541882CrossRefGoogle Scholar
Chen, W., Guo, F., Wang, F.Y. (2015), “A Survey of Traffic Data Visualization”, IEEE Trans. Intell. Transp. Syst., Vol.16, pp. 29702984. https://dx.doi.org/10.1109/TITS.2015.2436897CrossRefGoogle Scholar
Cross, N. (2008), Engineering design methods: strategies for product design. Chichester, John Wiley.Google Scholar
Daraio, C., Diana, M., Di Costa, F., Leporelli, C., Matteucci, G. and Nastasi, A. (2016), “Efficiency and effectiveness in the urban public transport sector: A critical review with directions for future research”, Eur. J. Oper. Res., Vol. 248, No 1, pp. 120, https://dx.doi.org/10.1016/j.ejor.2015.05.059.CrossRefGoogle Scholar
Dimanche, V., Goupil, A., Philippot, A., Riera, B., Urban, A., Gabriel, G. (2017), “Massive Railway Operating Data Visualization; a Tool for RATP Operating Expert”, IFAC-PapersOnLine, Vol. 50, Issue 1, pp. 1584115846.10.1016/j.ifacol.2017.08.2324CrossRefGoogle Scholar
Gkiotsalitis, K., Wu, Z. and Cats, O. (2019), “A cost-minimization model for bus fleet allocation featuring the tactical generation of short-turning and interlining options”, Transp. Res. Part C Emerg. Technol., Vol. 98, pp. 1436, https://dx.doi.org/10.1016/j.trc.2018.11.007.CrossRefGoogle Scholar
Han, J., Forbes, H., Shi, F., Hao, J. and Schaefer, D. (2020), “A data-driven approach for creative concept generation and evaluation”, Proceedings of the Design Society: DESIGN Conference. Cambridge University Press, Vol.1, pp. 167176. https://dx.doi.org/10.1017/dsd.2020.5.CrossRefGoogle Scholar
Itoh, M., Yokoyama, D., Toyoda, M., Tomita, Y., Kawamura, S., Kitsuregawa, M. ( 2016), “Visual Exploration of Changes in Passenger Flows and Tweets on Mega-City Metro Network,” IEEE Transactions on Big Data, Vol. 2, No. 1, pp. 8599, https://dx.doi.org/10.1109/TBDATA.2016.2546301.CrossRefGoogle Scholar
Ji, S.Y., Jeong, B.K., Jeong, D.H. (2020). “Evaluating visualization approaches to detect abnormal activities in network traffic data”. International Journal of Information Security. https://doi.org/10.1007/s10207-020-00504-9CrossRefGoogle Scholar
Nagel, T., Maitan, M., Duval, E., Vande Moere, A., Klerkx, J., Kloeckl, K., and Ratti, C. (2014), “Touching transport - a case study on visualizing metropolitan public transit on interactive tabletops”. Proceedings of the 2014 International Working Conference on Advanced Visual Interfaces (AVI '14). Association for Computing Machinery, New York, NY, USA, pp. 281288. DOI:https://doi.org/10.1145/2598153.2598180CrossRefGoogle Scholar
Pasini, K., Khouadjia, M., Samé, A., Ganansia, F., Oukhellou, L. (2019), “LSTM Encoder-Predictor for Short-Term Train Load Forecasting”. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science, Vol. 11908. Springer, Cham. https://doi.org/10.1007/978-3-030-46133-1_32Google Scholar
Salminen, J., Jung, S.G., Kamel, A. M., Santos, J. M., Kwak, H., An, J., and Jansen, B. J. (2020) Using Artificially Generated Pictures in Customer-facing Systems: An Evaluation Study with Data-Driven Personas. Behaviour & Information Technology. https://dx.doi.org/10.1080/0144929X.2020.1838610CrossRefGoogle Scholar
Stevenson, P. D. and Mattson, C. A. (2019), “The Personification of Big Data,” Proceedings of the Design Society: International Conference on Engineering Design. Cambridge University Press, Vol. 1(1), pp. 40194028. https://dx.doi.org/10.1017/dsi.2019.409.CrossRefGoogle Scholar
Sobral, T., Galvão, T., & Borges, J. (2019), “Visualization of Urban Mobility Data from Intelligent Transportation Systems”. Sensors (Basel, Switzerland), Vol. 19(2), 332. https://doi.org/10.3390/s19020332CrossRefGoogle ScholarPubMed
Tao, F., Qi, Q., Liu, A., Kusiak, A. (2018). “Data-driven smart manufacturing”. Journal of Manufacturing Systems, Vol. 48, pp.157-169. https://dx.doi.org/10.1016/j.jmsy.2018.01.006CrossRefGoogle Scholar
Toqué, F., Khouadjia, M., Come, E., Trepanier, M., Oukhellou, L. (2017). “Short long term forecasting of multimodal transport passenger flows with machine learning methods”, IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 560566, https://dx.doi.org/10.1109/ITSC.2017.8317939.CrossRefGoogle Scholar
Vasconcelos, L., & Crilly, N. (2016), “Inspiration and fixation: Questions, methods, findings, and challenges”, Design Studies, Vol. 42, pp.1-32. https://doi.org/10.1016/j.destud.2015.11.001.CrossRefGoogle Scholar
Yao, X., Han, B., Yu, D., & Ren, H. (2017). Simulation-Based Dynamic Passenger Flow Assignment Modelling for a Schedule-Based Transit Network. Discrete Dynamics in Nature and Society, 2017, 1-15.Google Scholar
Zhao, X. et al. (2020), “Interactive Visual Exploration of Human Mobility Correlation Based on Smart Card Data,” IEEE Transactions on Intelligent Transportation Systems, https://dx.doi.org/10.1109/TITS.2020.2983853.CrossRefGoogle Scholar
Zeng, W., Fu, C.-W., Arisona, S.M. and Qu, H. (2013), “Visualizing Interchange Patterns in Massive Movement Data”, Computer Graphics Forum, Vol. 32, pp. 271280. https://doi.org/10.1111/cgf.12114CrossRefGoogle Scholar