1Microsimulation of urban systems evolution requires synthetic population as a key input. Currently, the focus is on treating synthesis as a tting problem and thus various techniques have been developed, including Iterative Proportional Fitting (IPF) and Combinatorial Optimization based techniques. The key shortcomings of these procedures include: a) tting of one contingency table, while there may be other solutions matching the available data b) due to cloning rather than true synthesis of the population, losing the heterogeneity that may not have been captured in the microdata c) over reliance on the accuracy of the data to determine the cloning weights d) poor scalability with respect to the increase in number of attributes of the synthesized agents. In order to overcome these shortcomings, we propose a Markov Chain Monte Carlo (MCMC) simulation based approach. Partial views of the joint distribution of agent's attributes that are available from various data sources can be used to simulate draws from the original distribution. The real population from Swiss census is used to compare the performance of simulation based synthesis with the standard IPF. The standard root mean square error statistics indicated that even the worst case simulation based synthesis (SRMSE = 0.35) outperformed the best case IPF synthesis (SRMSE=0.64). We also used this methodology to generate the synthetic population for Brussels, Belgium where the data

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.

No abstract

This article describes a new approach to the macroscopic first order modeling and simulation of traffic flow in complex urban road intersections. The framework is theoretically sound, operational, and comprises a large body of models presented so far in the literature.Working within the generic node model class of Tampere et al. (forthcoming), the approach is developed in two steps. First, building on the incremental transfer principle of Daganzo et al. (1997), an incremental node model for general road intersections is developed. A limitation of this model (as of the original incremental transfer principle) is that it does not capture situations where the increase of one flow decreases another flow, e.g., due to conflicts. In a second step, the new model is therefore supplemented with the capability to describe such situations. A fixed-point formulation of the enhanced model is given, solution existence and uniqueness are investigated, and two solution algorithms are developed. The feasibility and realism of the new approach is demonstrated through both a synthetic and a real case study.

No abstract