Cube Analyst
Methodology
Stage One: Data Preparation
The type and quantity of data input to the estimation process is left to the user to determine. As a rule, the more data provided, the more accurate the resulting estimated matrix will be, but it is possible to achieve worthwhile results with limited data. Data used by the estimation can come from several sources, including:
- existing trip matrices either whole or from a sector of the study area
- traffic count data obtained manually or from automatic counters
- trip end data obtained from parking surveys or from trip generation equations
- partial matrix data such as cordon surveys
- boarding and alighting trip surveys for public transit
- routing data, calculated by Cube Voyager, TP+ or TRIPS
The assumption is made in Cube Analyst that all input data are distributed using a Poisson distribution to represent error
Stage Two: Data Variability
The treatment of the inherent variability of transport data as an integral part of matrix estimation is a distinctive feature of Cube Analyst. The variability in the quality of the data is handled using confidence levels. Confidence levels are set for each observation or for each group of observations. Cube Analyst facilities help users to judge the effect of altered quality and confidence levels on the estimated matrix.
Two Dimensional Schematic View of Variations in Objective Function in Cube Analyst
Stage Three: Estimation
The matrix is estimated by the software. This is a computationally intensive phase, but requires minimal attention from the user. Cube Analyst performs a set of iterative calculations which will automatically determine the statistically most likely matrix for the set of input data values provided. The first time Cube Analyst is run, it creates a set of files which can be used to reduce the run times of subsequent runs of Cube Analyst. This ability to benefit from a previous run of Cube Analyst is usually used to assist in analyzing the consequences of changes in data values.
Stage Four: Quality Assessment
The approach to analyzing the quality of the estimated matrix is:
Besides information output by Cube Analyst itself, extensive use is made of other Cube programs for creating tabulations and graphic displays which highlight different characteristics of the estimated matrix. Information comparing input data with corresponding values derived from the estimated matrix are readily accessible. A number of facilities characterize the extent of changes and help focus attention on areas of significant change between input and estimated information. These capabilities are especially valuable for large matrices.
Stage Five: Improving the Matrix
Deficiencies in the quality of the estimated matrix, when they are signaled by the results of the analysis phase, are remedied by improving the quality or quantity, or both, of the input data. The analysis phase can provide strong pointers as to which data are contributing to quality problems and hence where the user can focus attention.
The matrix is estimated by the software. This is a computationally intensive phase, but requires minimal attention from the user. Cube Analyst performs a set of iterative calculations which will automatically determine the statistically most likely matrix for the set of input data values provided. The first time Cube Analyst is run, it creates a set of files which can be used to reduce the run times of subsequent runs of Cube Analyst. This ability to benefit from a previous run of Cube Analyst is usually used to assist in analyzing the consequences of changes in data values.
Stage Four: Quality Assessment
The approach to analyzing the quality of the estimated matrix is:
- comparing the estimated results with input data values
- checking the sensitivity of the results if data values are altered
- analyzing the estimation calculations
Besides information output by Cube Analyst itself, extensive use is made of other Cube programs for creating tabulations and graphic displays which highlight different characteristics of the estimated matrix. Information comparing input data with corresponding values derived from the estimated matrix are readily accessible. A number of facilities characterize the extent of changes and help focus attention on areas of significant change between input and estimated information. These capabilities are especially valuable for large matrices.
Stage Five: Improving the Matrix
Deficiencies in the quality of the estimated matrix, when they are signaled by the results of the analysis phase, are remedied by improving the quality or quantity, or both, of the input data. The analysis phase can provide strong pointers as to which data are contributing to quality problems and hence where the user can focus attention.
Find a product
