• Journal of Korean Society of Transportation
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• v.25 no.4
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• pp.21-32
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• 2007

The households to be surveyed are usually huge number at the level of a city or metropolitan survey, not to mention a nationwide travel survey. Therefore, household travel surveys to figure out true origin-destination (O/D) trip patterns (population O/D) are conducted through a sampling method rather than by surveying all of the population in the system. Therefore, the population O/D pattern can only be estimated by expanding the sampled O/D patterns to the population. It is very difficult to avoid the errors involved in the process of sampling, surveying and expanding O/D data. In order to minimize such errors while estimating the true O/D patterns of the population, the validation and adjustment process should employed by doing a comparison between the expanded sample O/D data and observed link traffic volumes. This study suggests a method of validation and adjustment of the expanded sample O/D data by comparing observed link volumes at several screenlines. The study also suggests a practical technique to modify O/D pairs which are excluded in the screenline validation process by comparing observed traffic volume with the results of traffic assignment analysis. An empirical study was also conducted as an example applying the suggested methods of validation and adjustment with Korea's nationwide O/D data and highway network.

• Journal of Korean Society of Transportation
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• v.22 no.1 s.72
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• pp.43-62
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• 2004

Conventionally the estimation method of the origin-destination Matrix has been developed by implementing the expansion of sampled data obtained from roadside interview and household travel survey. In the survey process, the bigger the sample size is, the higher the level of limitation, due to taking time for an error test for a cost and a time. Estimating the O-D matrix from observed traffic count data has been applied as methods of over-coming this limitation, and a gradient model is known as one of the most popular techniques. However, in case of the gradient model, although it may be capable of minimizing the error between the observed and estimated traffic volumes, a prior O-D matrix structure cannot maintained exactly. That is to say, unwanted changes may be occurred. For this reason, this study adopts a conjugate gradient algorithm to take into account two factors: estimation of the O-D matrix from the conjugate gradient algorithm while reflecting the prior O-D matrix structure maintained. This development of the O-D matrix estimation model is to minimize the error between observed and estimated traffic volumes. This study validates the model using the simple network, and then applies it to a large scale network. There are several findings through the tests. First, as the consequence of consistency, it is apparent that the upper level of this model plays a key role by the internal relationship with lower level. Secondly, as the respect of estimation precision, the estimation error is lied within the tolerance interval. Furthermore, the structure of the estimated O-D matrix has not changed too much, and even still has conserved some attributes.