This chapter discusses the process for using CUBE Analyst. Topics include:

• Input data: overview
• Outputs: overview
• Estimating large matrices (hierarchic estimation)
• Estimation process

Input data: overview

The data that can be used in estimating the new O-D matrix may include some or all of the following types of data:

• A prior (existing) trip matrix
• Traffic generations and attractions of zones
• Traffic counts on links and/or turns
• Modeled (multiple) paths between zones
• Cost of travel between zones
• Parameters of a calibrated trip distribution function
• Part-trip data, where trips are observed traveling between points which are not necessarily their ultimate origins and destinations

Outputs: overview

The outputs from CUBE Analyst are:

• The estimated O-D matrix
• Summary Reports, in the form of a print (*.prn) file, describing the differences between input data and corresponding values implied by the estimated matrix. The print file also provides a return code indicating problems during execution, or a successful completion.

For more information, see Reports.

• A set of files with information on:
  • Model parameter values
  • A log of the optimization steps
  • Internal gradient search and intercept data

Estimating large matrices (hierarchic estimation)

CUBE Analyst provides a hierarchic approach to estimation for use with very large matrices; typically more than 2,500 zones. This is required to make the process more manageable and less time consuming.

The basic approach is to estimate a general matrix, in which zones are automatically grouped into districts. This area-wide estimation is then used to control a set of detailed estimations, these build up to provide a fully-detailed estimate for the entire study area. This is discussed in detail in Chapter 7, “Hierarchic Estimation.”

Estimation process

The only program directly involved in the estimation process itself is CUBE Analyst, although other CUBE programs play an important part in the pre- and post processing of the data.

The data used may be some or all of the data described earlier in Input data: overview.

CUBE Analyst may also use model parameters, gradient search, and intercept files from a previous run of CUBE Analyst for the current estimation to ”warm start” the calculations.

Internally CUBE Analyst can be considered to be made up of two main parts each of which is executed alternately, namely:

• Estimation model

The function of this is, given some particular values of the model parameters, to calculate the estimated matrix, trip ends, screenline volumes, etc., and also to perform the likelihood calculation.

• Optimization step

This procedure attempts to change the values of the model parameters to improve the likelihood value (the objective function).

These two stages are carried out alternately in a series of iterations until no further improvement can be made.