One of the most valuble pieces of data in travel demand forecasting is the matrix representing existing travel. It is the basis for forecasting and for almost all important comparative analyses Cube Analyst is the Cube functional library developed specifically for estimating and updating base year automobile, truck and public transit trip matrices. Cube Analyst enables the user to exploit a wide variety of data that contribute to matrix updating and matrix development. Cube Analyst has been used successfully on many and varied studies around the world.

Cube Analyst can be used to estimate trip matrices for any number of zones. It has been used to estimate the regional base travel matrix for the New York Metropolitan Area with over 2500 zones.

Excellent results with limited data

Matrix Desire Lines Trip matrices are the backbone of travel forecasting. Cube Analyst helps you estimate highly accurate matrices using limited data. The graphics in Cube Base show the level of travel from zone to zone.

Cube Analyst uses mathematical techniques to find trip matrices that are consistent with observed transport demand and count data. It does what many do by hand, but in a much more accurate and efficient way.

Matrix Data The trip matrices are estimated on a cell-by-cell basis using the supplied data. Statistical summaries are output giving indicators of the quality of the estimation. The graphics in Cube Base show the binary matrix values.

Cube Analyst is given a set of observations concerning travel demand: trip end data such as a survey at a shopping center, traffic counts organized into screen- and cut-lines, movement or path data identifying the routes used by travellers going from origin to destination. In addition, and a key element in Cube ME, the user provides quality weights. These provide tolerance bands to each of the individual or groups of data observations. Cube Analyst uses maximum likelihood statitstical techniques to estimate the cell values for the matrix. These values are those that fit the best with the observations and their quality weights.

Key advantages of Cube Analyst
Exploits a wide range of low cost, readily available data:
- existing trip matrices either from surveys or from travel demand models
- flow counts from all types of counting devices
- trip end data from parking surveys
- public transit data from boarding and alighting surveys
- partial trip data from license plate and other cordon’ surveys
- electronic ticket data
- data of varied quality can be used without compromising overall quality
- software tools assist with the preparation of data
- use of confidence levels providing explicit consideration to the inherent variability in
the data

Rigorous Methodology
- Cube Analyst uses the maximum likelihood statistical method
- a powerful optimizer allows individual cells to be estimated with precision
- the calculation is self-calibrating

Integral Quality Assurance
- extensive reporting options enable users to establish their own confidence in the results
- effects and implications on the estimated matrix of different input data may be studied
- specialist tools indicate the quality of the estimated matrix
- quality analysis of estimated matrix guides cost effective and selective data surveys when required
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