This piece of research aims to obtain mobility profiles of the Swiss population. To that end, a survey of the Swiss Statistical Office (FSO) called Mobility and Transport Micro-census (MTMC) is utilized. Along with a qualitative method clustering, the respondents in the survey are clustered based on their mobility characteristics to obtain their profiles. The clustering, in particular acquiring medoids (centrotypes or exemplars), helps us then to generate a synthetic population of Switzerland. To gain medoids of each cluster, the k-Medoids clustering algorithm is utilized which partitions instances based on their positions in a latent space (symmetric distance matrix). Distances that shape this space can be generated by various metrics e.g. Euclidean, Gower, Manhattan. Since in this study features are mixed-type (e.g. numeric, categorical, etc.), the Gower distance metric is preferred. In this study, the default weights of the Gower distance are optimized to obtain a higher Average Silhouette Width (ASW) value of the clustering results. ASW can be used to measure the quality of clustering results in which high value leads to higher intra-cluster homogeneity and inter-cluster dissimilarity. So, maximizing the ASW value improves the quality of the clusters which is the goal of the optimization. At the end, this process helps us to obtain more accurate mobility profiles of the Swiss population.

The complex nature of agent-based modeling may reveal more descriptive accuracy than analytical tractability. That leads to an additional layer of methodological issues regarding empirical validation, which is an ongoing challenge. This paper offers a replicable method to empirically validate agent-based models, a specific indicator of “goodness-of-validation” and its statistical distribution, leading to a statistical test in some way comparable to the p value. The method involves an unsupervised machine learning algorithm hinging on cluster analysis. It clusters the ex-post behavior of real and artificial individuals to create meso-level behavioral patterns. By comparing the balanced composition of real and artificial agents among clusters, it produces a validation score in [0, 1] which can be judged thanks to its statistical distribution. In synthesis, it is argued that an agent-based model can be initialized at the micro-level, calibrated at the macro-level, and validated at the meso-level with the same data set. As a case study, we build and use a mobility mode-choice model by configuring an agent-based simulation platform called BedDeM. We cluster the choice behavior of real and artificial individuals with the same ex-ante given characteristics. We analyze these clusters’ similarity to understand whether the model-generated data contain observationally equivalent behavioral patterns as the real data. The model is validated with a specific score of 0.27, which is better than about 95% of all possible scores that the indicator can produce. By drawing lessons from this example, we provide advice for researchers to validate their models if they have access to micro-data.

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The author name was written incorrectly and has now been corrected to Schumann.No other changes to the article have been made.