تاثیرات اجتماعی در محل سکونت ، قابلیت تحرک و انتخاب فعالیت در مدل های یکپارچه شبیه سازی کوچک

Agent-based approaches to simulating long-term location and mobility decisions and short-term activity and travel decisions of households and individuals are receiving increasing attention in land-use and transportation interaction (LUTI) models to predict land-use changes and travel behaviour in mutual interaction. Social interactions between households and between individuals potentially have an influence on a wide range of the long-term and short-term choices involved in these systems. In this paper we identify the areas in which social interactions play a role and address the question how these influences can be modelled in the context of agent-based LUTI models. We distinguish impacts on activity participation (joint activity participation, support-and-help activities) and impacts on decision making (information exchange, social adaptation of preferences and aspirations) as the two main areas of social influence. A prototype of a LUTI model is proposed that accounts for impacts of the social network on longer-term mobility decision making through information exchange and social adaptation of preferences and aspirations. The model is demonstrated in a numerical simulation.

The use of land use and transportation models to support policy makers has become widespread over the past decades. During this period, a development has taken place in which traditional zone-based models (e.g. Lowry, 1963) have been replaced by more sophisticated models, such as models of urban development based on resource allocation theories (e.g. Anas and Xu, 1999). Recently, various scholars have proposed the use of micro-simulation models such as UrbanSim (Waddell, 2002), ILUTE (Miller et al., 2004), Ilumass (Strauch et al., 2005) and PUMA (Ettema et al., 2007). Whereas traditional zone-based models describe spatial development and traffic flows as the outcome of interactions between zones, micro-simulation models describe land use and transport processes at the level of the individual decision makers, such as households, individuals, firms and land owners. Although different in the details, the aforementioned models have in common that they describe how synthetic populations of households and jobs/firms are subject to demographic processes, economic development and deliberately take decisions about spatial behaviour such as locational decisions and daily travel. To this end, disaggregate models (e.g. discrete choice models or rule based models) are applied to the individual agents in the synthetic population. An important characteristic is that individual households and firms respond to changes in the aggregate conditions (e.g. prices) stemming from the aggregate decisions made by other agents. The use of micro-simulation models offers some important advantages over aggregate, zone-based models. First, micro-simulation models are dynamic in nature, in contrast to traditional economic models that assume equilibrium and only describe the end state of a spatial system. In a policy environment where one is interested not only in the outcome of policies but also in the societal processes leading to this outcome, this is an important advantage.

This paper has explored the potential role of social interactions and social networks in LUTI models. It is concluded that social interactions may influence behaviours modelled in LUTI models in various ways. First, social networks trigger social interactions, leading to activities and travel. Second, the social network is a source of information when making complex decisions under limited information. Information regarding the existence/availability of alternatives, their characteristics and the utility one derives from them are all exchanged through social interactions. Finally, the valuation of alternatives may depend on one’s own position relative to that of peers in the social network. This paper focuses on the role of social networks in longer-term mobility decisions. Since it is assumed that decisions on various longer-term dimensions are interrelated, the state space for such problems is very large and households are expected to have only fragmented information about all possible outcomes. Therefore, we assume that the social network will play a role in dispersing information about available alternatives, their characteristics and their valuation. A model was developed to test the potential impact of social networks on choice outcomes, which includes social learning mechanisms. These imply that the evaluation of allocation options is based on peers’ experiences and on a mental simulation of the new allocation option. The weight of peers’ evaluations is dependent on the degree of similarity between households.