This article addresses the role of spatial interaction in social networks. We analyse empirical data describing a network of leisure contacts and show that the probability to accept a person as a contact scales in distance with ∼ d −1.4. Moreover, the analysis reveals that the number of contacts an individual possesses is independent from its spatial location and the spatial distribution of opportunities. This means that individuals living in areas with a low accessibility to other persons (rural areas) exhibit at average the same number of contacts compared to individuals living in areas with high accessibility (urban areas). Low accessibility is thus compensated with a higher background probability to accept other candidates as social contacts. In addition, we propose a model for large-scale social networks involving a spatial and social interaction between individuals. Simulation studies are conducted using a synthetic population based on census data as input. The results show that the model is capable of reproducing the spatial structure, but, however, fails to reproduce other topological characteristics. Both, the analysis of empirical data and the simulation results provide a further evidence that spatial interaction is a crucial aspect of social networks. Yet, it appears that spatial proximity does only explain the spatial structure of a network but has no significant impact on its topology.
Much research has been conducted to obtain insights into the basic laws governing human travel behaviour. While the traditional travel survey has been for a long time the main source of travel data, recent approaches to use GPS data, mobile phone data or the circulation of bank notes as a proxy for human travel behaviour are promising. The present study proposes a further source of such proxy-data: the social network. We collect data using an innovative snowball sampling technique to obtain details on the structure of a leisure-contacts network. We analyse the network with respect to its topology, the individuals' characteristics, and its spatial structure. We further show that a multiplication of the functions describing the spatial distribution of leisure contacts and the frequency of physical contacts results in a trip distribution that is consistent with data from the Swiss travel survey.
A multimodeling approach to large-scale, activity-based, multiagent simulation of travel demand is introduced. MATSIM is a full activity-based transport simulation. Its greatest current performance limitation is the network loading simulation, currently a queue simulation (QSim). QSim is iteratively executed for the entire agent population for evaluating the effects of random mutations on the activity plans of a fraction of the population. After each QSim, poorly performing plans are discarded, good plans are kept, and the agents slowly learn what works best for their individual activity needs. In the application presented, the system periodically replaces QSim for a number of iterations with a simplified pseudosimulation that runs approximately two orders of magnitude faster. The pseudosimulation uses travel time information from the preceding QSim iteration to estimate how well an agent day plan might perform. Repeated iterations of the pseudosimulation produce better-performing plans in a short time. These plans are passed to the QSim for updating of network travel time information, and the process repeats. The technique is tested in a scenario for Zurich, Switzerland, and incorporates mode choice, road pricing, secondary activity location choice, activity timing adjustment, and dynamic routing. The technique dramatically improves convergence rates for such complex, large-scale simulations and fully exploits modern multicore computer architectures.
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