Mindenkit szeretettel várunk az MTA TK "Lendület" RECENS  hálózati előadás-sorozatának következő alkalmára 2016. október 18-án (kedden), melyen Elena Vallino (University of Torino) tart előadást "An application of agent-based models on urban passenger mobility: the case study of Varese” címmel. 

 

Az előadás megrendezésére az MTA TK keresztszárny földszinti tárgyalótermében (1014 Budapest, Országház utca 30.) kerül sor 15 órai kezdettel.

 

Az előadás kivonata:

Elena Maggi (University of Insubria) and Elena Vallino (University of Torino)

 

In this paper we present an agent-based model which reproduces transport choices of a sample of 5000 citizens of the city of Varese (northern Italy) and the corresponding PM emissions of their daily commutes. The aim of the model is testing the impact of public policies willing to foster commuting choices with lower PM emissions. Our model structure takes inspiration from the model developed in Natalini and Bravo (2014). The model considers one main process, the commuters‘ decision about what means of transport to utilize. The agents owns a set of preferences—one for each mode of transport (private car, bicycle, public transport)—that have been assigned to them. Throughout the process, these preferences are influenced by the relative price of the different means of transport, by social influence and by the intensity of the policies applied. Each agent is embedded in a social network: neighborhoods are formed according to the closeness of the initial attitudes of the agents. The agent decides its mean of transportation on the basis of the interplay of different factors: relative price of the means of transportation, satisfaction of social needs and uncertainty level. Two kinds of policies are implemented in order to give incentives to the agents to shift to means of transportation with lower PM emissions. The first is market-based and the second aims at influencing the intrinsic motivations of the agents. We test each policy alone, at different intensity levels, and different combinations of the two.  The initial preferences for each modes of transport which are assigned to the agents are derived from distributions inspired to the data from the Italian National Institute of Statistics 2011. We utilize the software NetLogo. Preliminary results suggest that preference-based policies are more effective if compared to price-based ones. However, the application of a mix of different policies seems to give the best outputs: the same amount of resources in terms of policy intensity produce much better results if they are allocated at the same time to two policies, then to one only.