Abstract
Traffic can be viewed as a complex adaptive system in which systemic patterns arise as emergent phenomena. Global behaviour is a result of behavioural patterns of a large set of individual travellers. However, available traffic simulation models lack of concepts to comprehensibly capture preferences and personal objectives as determining factors of individual decisions. This limits predictive power of such simulation models when used to estimate the consequences of new traffic policies. Effects on individuals must not be ignored as these are the basic cause of how the system changes under interventions. In this paper, we present a simulation framework in which the self-interested individual and its decision-making is placed at the center of attention. We use semantic reasoning techniques to model individual decision-making on the basis of personal preferences that determine traffic relevant behaviour. As this initially makes the simulations more complex and opaque the simulation framework also comprises tools to inspect rule evaluation providing a necessary element of explainability. As proof of concept we discuss an example scenario and demonstrate how this type of modelling could help in evaluating the effects of new traffic policies on individual as well as global system behaviour.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Adnan, M.,et al.: Simmobility: a multi-scale integrated agent-based simulation platform. In: 95th Annual Meeting of the Transportation Research Board Forthcoming in Transportation Research Record (2016)
Bazzan, A., do Amarante, M., Sommer, T., Benavides, A.: Itsumo: an agent-based simulator for its applications. In: Proceedings of the 4th Workshop on Artificial Transportation Systems and Simulation, p. 8. IEEE (2010)
Berger, M., Müller, C., Nüske, N.: Digital nudging in online grocery stores : towards ecologically sustainable nutrition. In: Proceedings of the 41st International Conference on Information Systems (ICIS 2020). AIS Electronic Library, Hyderabad, India (Dezember 2020). https://eref.uni-bayreuth.de/57729/
Dimitropoulos, A., Oueslati, W., Sintek, C.: The rebound effect in road transport: a meta-analysis of empirical studies. Energy Econ. 75, 163–179 (2018). https://doi.org/10.1016/j.eneco.2018.07.021, https://www.sciencedirect.com/science/article/pii/S0140988318302718
Égert, B., Koźluk, T., Sutherland, D.: Infrastructure and growth (mar 2009). https://doi.org/10.1787/225682848268, https://www.oecd-ilibrary.org/content/paper/225682848268
Engel, U., Pötschke, M.: Mobilität und verkehrsmittelwahl 1999/2000. GESIS Datenarchiv, Köln. ZA4203 Datenfile Version 1.0.0 (2013). https://doi.org/10.4232/1.11591
Erol, K., Levy, R., Wentworth, J.: Application of agent technology to traffic simulation. In: Proceedings of Complex Systems, Intelligent Systems and Interfaces (1998)
Farrenkopf, T.: Applying semantic technologies to multi-agent models in the context of business simulations. Ph.D. thesis, Edinburgh Napier University (2017)
GfK Consumer Panels: consumers’ choice ’17 - neue Muster in der Ernährung: die Verbindung von Genuss, Gesundheit und Gemeinschaft in einer beschleunigten Welt : eine Publikation anlässlich der Anuga 2017. GfK Consumer Panels and Bundesvereinigung der Deutschen Ernährungsindustrie e.V. (2017), https://www.bve-online.de/presse/infothek/publikationen-jahresbericht/consumers-choice-2017
Guarino, N., Oberle, D., Staab, S.: What Is an ontology? In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. IHIS, pp. 1–17. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-92673-3_0
Hirschauer, N., Lehberger, M., Musshoff, O.: Happiness and utility in economic thought–or: what can we learn from happiness research for public policy analysis and public policy making? Soc. Indicat. Res. 121(3), 647–674 (2015)
Hotten, R.: Bmw and daimler invest 1bn in new car venture (2019). https://www.bbc.com/news/business-47332805, Accessed 30 Nov 2019
Jung, J., Koo, Y.: Analyzing the effects of car sharing services on the reduction of greenhouse gas (GHG) emissions. Sustainability 10(2), 539 (2018). https://doi.org/10.3390/su10020539
Kahneman, D., Wakker, P., Sarin, R.: Back to Bentham? Explorations of experienced utility. Q. J. Eono. 112(2), 375–406 (1997)
Kolata, G.: What if they closed 42d street and nobody noticed. New York Times 25, 38 (1990)
Marczuk, K., et al.: Autonomous mobility on demand in SimMobility: case study of the central business district in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). pp. 167–172. IEEE, July 2015. https://doi.org/10.1109/iccis.2015.7274567
MAVS: Agent-based intelligent traffic simulation system (2015). https://www.utdmavs.org/matisse/. Accessed 25 May 2020
Nguyen, J., Powers, S., Urquhart, N., Farrenkopf, T., Guckert, M.: An overview of agent-based traffic simulators (2021)
Nguyen, J., Powers, S., Urquhart, N., Farrenkopf, T., Guckert, M.: Using semantic technology to model persona for adaptable agents. In: ECMS (in press, 2021)
Oltermann, P.: Activists try to stop autobahn being built through German forest (2020). https://www.theguardian.com/world/2020/oct/04/activists-try-to-stop-autobahn-being-built-through-german-forest. Accessed 3 Jan 2021
Rossetti, R., Liu, R.: Advances in Artificial Transportation Systems and Simulation. Academic Press, San Diego (2014)
Statista: Lebensmittelkauf in deutschland (2020). https://de.statista.com/statistik/studie/id/12521/dokument/einkauf-und-konsum-von-lebensmitteln-statista-dossier/
Statistische Ämter des Bundes und der Länder: Zensus 2011: Methoden und Verfahren. Statistisches Bundesamt. Wiesbaden (2015)
Thulasidasan, S., Kasiviswanathan, S., Eidenbenz, S., Galli, E., Mniszewski, S., Romero, P.: Designing systems for large-scale, discrete-event simulations: experiences with the fasttrans parallel microsimulator. In: 2009 International Conference on High Performance Computing (HiPC), pp. 428–437. IEEE, December 2009. https://doi.org/10.1109/hipc.2009.5433183
Torabi, B., Al-Zinati, M., Wenkstern, R.Z.: MATISSE 3.0: a large-scale multi-agent simulation system for intelligent transportation systems. In: Demazeau, Y., An, B., Bajo, J., Fernández-Caballero, A. (eds.) PAAMS 2018. LNCS (LNAI), vol. 10978, pp. 357–360. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94580-4_38
Ziemke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSIM to increase the transferability of transport demand models. Transportation Research Record: J. Transp. Res. Board 2493(1), 117–125 (2015). https://doi.org/10.3141/2493-13
Acknowledgement
This research has been supported by a grant from the Karl-Vossloh-Stiftung (Project Number S0047/10053/2019).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen, J., Powers, S.T., Urquhart, N., Farrenkopf, T., Guckert, M. (2021). Modelling Individual Preferences to Study and Predict Effects of Traffic Policies. In: Dignum, F., Corchado, J.M., De La Prieta, F. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection. PAAMS 2021. Lecture Notes in Computer Science(), vol 12946. Springer, Cham. https://doi.org/10.1007/978-3-030-85739-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-85739-4_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-85738-7
Online ISBN: 978-3-030-85739-4
eBook Packages: Computer ScienceComputer Science (R0)