The Effects of Different Interaction Protocols in Agent-Based Simulation of Social Activities

The Effects of Different Interaction Protocols in Agent-Based Simulation of Social Activities

Nicole Ronald (Eindhoven University of Technology, The Netherlands), Theo Arentze (Eindhoven University of Technology, The Netherlands) and Harry Timmermans (Eindhoven University of Technology, The Netherlands)
Copyright: © 2011 |Pages: 15
DOI: 10.4018/jats.2011040102
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Decision making in models of activity and travel behaviour is usually individual-based and focuses on outcomes rather than the decision process. Using agent-based modelling techniques and incorporating interaction protocols into the model can assist in modelling decision-making in more detail. This paper describes an agent-based model of social activity generation and scheduling, in which utility-based agents interact with each other to schedule activities. Six different protocols are tested. The authors show that the model outcomes reflect minor changes in the protocol, while changing the order of the protocol leads to significantly different outcomes, hence the protocol plays a large role in the simulation results and should be studied in more detail.
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2. Background

Agent-based modelling and multi-agent based systems are of interest to researchers in travel and transport modelling. A look at the latest proceedings of the AAMAS workshop on Agents in Traffic and Transportation (Klügl et al., 2010) shows that popular applications are travel simulations and freight handling and routing. Larger scale frameworks, such as MATSIM (, can incorporate activity scheduling along with travel modelling and therefore have the ability to model activity choice in more detail.

2.1. Activity-Travel Modelling

Human activities are generated due to “physiological, psychological and economical needs” (Wen & Koppelman, 2000). A distinction is commonly made between subsistence (e.g., work-related), maintenance (e.g., keeping the household running), and leisure activities.

Non-discretionary activities such as work and school can be partly explained by the traveller's sociodemographic characteristics and generalised travel costs (Hackney & Marchal, 2007), as well as long-term decisions such as a decision to move to a particular town. Participation in and scheduling of, other activities is not as easily predicted. Social and leisure activities are the reported purpose for a large number of trips, ranging from 25 to 40% for various countries (Axhausen, 2006).

In current state-of-the-art activity-travel models, social activities, if at all scheduled, are assigned to random locations and times (Arentze & Timmermans, 2004) and do not take into account the constraints or preferences of friends. Being able to model these activities could lead to better prediction of activity schedules and forecasts of travel patterns and demand for urban facilities, in particular those relating to social and leisure activities.

A theory currently being explored for generating discretionary activities is based on needs. Activities both satisfy and generate needs and needs grow over time (Arentze & Timmermans, 2006). Maslow's hierarchy of needs has been proposed as a starting point (Miller, 2005), however it is difficult to collect data for model validation. A separate set of needs was proposed by Arentze and Timmermans (2006) which could be identified through empirical research.

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