Transport policy analysis using multi-agent-based simulation

Abstract: This thesis explores how multi-agent-based simulation can be used for transport policy analysis. Transport policies are often used as a means to reach governmental goals, such as environmental targets to reduce the impact of transportation. To predict how transportation is influenced by policies, public authorities often make use of simulation models. A structured review of such models is made focussing on important transport chain characteristics. We argue that to properly predict the actual environmental, economic, and logistical effects of transport policies, the logistical decisions made in transport chains must be modelled appropriately. Such decisions, e.g., concern the choice of producer and traffic mode, planning of transportation, production, and terminal handling. The review concludes that models currently used for transport policy analysis fail to capture many of these characteristics. We argue that agent-based models have the potential to include these aspects since they are able to explicitly model the actual decision making in transport chains. We have identified a set of generic roles in transport chains where each role is responsible for certain decisions. A multi-agent-based simulator, TAPAS, has been developed in which these roles are modelled as agents. Thus, the decision making in transport chains and its influence by the application of transport policies are captured. The decisions lead to the execution of the logistical operations which in turn have consequences on the logistics, economic, and environmental performance. The usage of TAPAS is illustrated by presenting two scenarios based on realworld transport chains. Simulation experiments of the scenarios have been performed where different types of transport policies are introduced. The simulation results are analysed, e.g., by comparing the results to similar studies and by sensitivity analysis of input parameters. To facilitate the validation and generalisation of simulation results we suggest making use of typical transport chains and roles characterised by, e.g., product type and geographical locations. The type of studies that TAPAS can support are described and compared to studies typically made with traditional models. Transport policies which are relevant to examine are described and their potential influence on transport chains are analysed. The possible usage of TAPAS is discussed and related to different types of users. Public authorities can, e.g., use TAPAS to complement studies using traditional models. This can improve the accuracy of the simulation results by the inclusion of more logistical aspects. Large companies are another type of user which, e.g., can use TAPAS to analyse new market segments, such as new product types or new consumers, where historical data is not available.