The paper focuses on transportation trip generation models based on mixed-use and transport infrastructure near the site. Transport trip generation models are considered with an aim to improve the accuracy of transport generated trips. Information systems are reviewed, and "smart growth" criteria that could affect the accuracy of trip generation models are also identified. Experimental results of transport generated trips based on linear regression equations and "smart growth" tools are demonstrated.

-Mode choice analysis has received the most attention among discrete choice problems in travel behavior literature. Most traditional mode choice models are based on the principle of random utility maximization derived from econometric theory. This paper investigates performance of mode choice analysis with classification methods -decision trees, discriminant analysis and multinomial logit. Experimental results have demonstrated satisfactory quality of classification.

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-An important problem in the application of cluster analysis is the decision regarding how many clusters should be derived from the data. The aim of the paper is to determine a number of clusters with a distinctive breaking point (elbow), calculating variance ratio criterion (VRC) by Calinski and Harabasz and J-index in order to check robustness of cluster solutions. Agglomerative hierarchical clustering was used to group a data set that is characterized by a complex structure, which makes it difficult to identify a structure of homogeneous groups. Stability of cluster solutions was performed by using different similarity measures and reordering cases in the dataset.

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