Abstract: Communications are received from a transportation service provider and are parsed for data indicating capacity data. The data is then matched to a stored format for capacity data and the parsed data is extracted from the data matched to the stored format to a preferred data format, and reviewed to determine if the stored data reflects capacity indicated by the received communication. The data is then combined with preference data and performance data concerning the transportation service data and used to automatically match a demand for transportation services with the capacity to provide the transportation services.

Abstract: Communications are received from a transportation service provider and are parsed for data indicating capacity data. The data is then matched to a stored format for capacity data and the parsed data is extracted from the data matched to the stored format to a preferred data format, and reviewed to determine if the stored data reflects capacity indicated by the received communication. The data is then combined with preference data and performance data concerning the transportation service data and used to automatically match a demand for transportation services with the capacity to provide the transportation services.

Abstract: A method of calculating a prepayment score which summarizes the effects of one or more other factors which affect the prepayment propensity on a mortgage loan but which are normally ignored comprises: (1) analyzing a population of loans and selecting a class of loans which have similar characteristics of coupon rate, loan type, age and weighted average maturity and calculating a prepayment model using said characteristics which define the class as input arguments along with vectors of 30-year and 15-year projected mortgage rates or other interest rate projections reflective of mortgage interest rates, with the differences in the loans in said class being variations in one or more other factors which are to be summarized in one or more prepayment scores, said other factors being onew which are ignored by most prepayment model calculations of the prior art; (2) determine the differences or errors between the predicted prepayment propensity calculated in step 1 for said selected class of loans and the actual his