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.
Nguyen, J., Powers, S., Urquhart, N., Farrenkopf, T., & Guckert, M. (2021). Modelling Individual Preferences to Study and Predict Effects of Traffic Policies. In Advances in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection 19th International Conference, PAAMS 2021, Salamanca, Spain, October 6–8, 2021, Proceedings (163-175). https://doi.org/10.1007/978-3-030-85739-4_14