Cluster analysis groups similar objects into respective categories using a statistical method. Cluster analysis determines patterns of association by grouping objects together. Cluster analysis in microsimulation develops multiple clusters that outline different types of travel conditions a road user might experience throughout the year. Examples of travel conditions include the following:

For cluster analysis, data from multiple sources is used to establish relationships between the different data types and determine different travel conditions. Therefore, FHWA recommends collecting up to a year’s worth of data for the different data types mentioned above. The FHWA 2019 Guidelines include a step-by-step process on how to perform cluster analysis. Perform a representative day analysis for one cluster (travel condition) and meet the calibration criteria for that. See

Figure 13-2

for a graphical representation of a cluster analysis. There may be multiple clusters derived from a dataset. Work with the TxDOT project manager to determine how many of the clusters to analyze.

See

Appendix N, Section 3 – Calibration Criteria Calculations Example

for a table on representative day selection. See

Appendix N, Section 5 – Lonestar Data in Cluster Analysis Case Study

for an example of how to use TxDOTs Lonestar data for cluster analysis