Regression Analysis for Transport Trip Generation Evaluation

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

Clustering Algorithm for Travel Distance Analysis

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

Regression Analysis for Transport Trip Generation Evaluation

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

Transport Simulation Model Calibration with Two-Step Cluster Analysis Procedure

The calibration results of transport simulation model depend on selected parameters and their values. The aim of the present paper is to calibrate a transport simulation model by a two-step cluster analysis procedure to improve the reliability of simulation model results. Two global parameters have been considered: headway and simulation step. Normal, uniform and exponential headway generation models have been selected for headway. Application of two-step cluster analysis procedure to the calibration procedure has allowed reducing time needed for simulation step and headway generation model value selection

Transport Simulation Model Calibration with Two-Step Cluster Analysis Procedure

The calibration results of transport simulation model depend on selected parameters and their values. The aim of the present paper is to calibrate a transport simulation model by a two-step cluster analysis procedure to improve the reliability of simulation model results. Two global parameters have been considered: headway and simulation step. Normal, uniform and exponential headway generation models have been selected for headway. Application of two-step cluster analysis procedure to the calibration procedure has allowed reducing time needed for simulation step and headway generation model value selection