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SUMMARYThis study evaluated the technical efficiency of shippers using data envelopment analysis (DEA) techniques. The data including 7365 shipping firms in Korea collected from the 2005 commodity flow survey were used in the analysis. Several DEA models were applied to determine efficiency scores based on both input-and output-orientations which each includes variable returns, constant returns, and non-increasing returns models. Accordingly, the technical and scale efficiency scores of each evaluated shipper in the same industry were measured and the best-practice firms for each industry were identified. Further, in order to handle the number of observed decision units and the diversity of data, the paper performed the cluster-base analysis, including industry-, business-, and spatial-based, to identify the effectiveness of the cluster-based DEA in estimating the efficiency score.

Historically, real-time intelligent transportation systems data are aggregated into discrete periods, typically of 5 to 10 min duration, and are subsequently used for travel time estimation and forecasting. In a previous study of link and corridor travel time estimation and forecasting by using probe vehicles, it was shown that the optimal aggregation interval size is a function of the traffic condition and the application. It is expected that traffic management centers will continue to collect travel time statistics (e.g., mean and variance) from probe vehicles and archive this data at a minimum time interval. Statistical models are developed for estimating the mean and variance of the link and route or corridor travel time for a larger interval by using only the observed mean travel time and variance for each smaller or basic interval. The proposed models are demonstrated by using travel time data obtained from Houston, Texas, which were collected as part of the automatic vehicle identification system of the Houston TranStar system. It was found that the proposed models for estimating link travel time mean and variance for a larger interval were easy to implement and provided results that had minimal error. The route or corridor travel time mean and variance model had considerable error compared with the link travel time mean and variance models.

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