A SEQUENTIAL APPROACH TO EXPLOITING THE COMBINED STRENGTHS OF SP AND RP DATA - APPLICATION TO FREIGHT SHIPPER CHOICE

The possibility of and procedure for pooling RP and SP data have been discussed in recent research work. In that literature, the RP data has been viewed as the yardstick against which the SP data must be compar ed. In this paper we take a fresh look at the two data types. Based on the peculiar strengths and weaknesses of each we propose a new, seque ntial approach to exploiting the strengths and avoiding the weaknesses of each data source. This approach is based on the premise that SP da ta, characterized by a well-conditioned design matrix and a less const rained decision environment than the real world, is able to capture re spondents' tradeoffs more robustly than is possible in RP data. (This, in turn, results in more robust estimates of share changes due to cha nges in independent variables.) The RP data, however, represent the cu rrent market situation better than the SP data, hence should be used t o establish the aggregate equilibrium level represented by the final m odel. The approach fixes the RP parameters for independent variables a t the estimated SP parameters but uses the RP data to establish altern ative-specific constants. Simultaneously, the RP data are rescaled to correct for error-in-variables problems in the RP design matrix vis-a- vis the SP design matrix. All specifications tested are Multinomial Lo git (MNL) models. The approach is tested with freight shippers' choice of carrier in three major North American cities. It is shown that the proposed sequential approach to using SP and RP data has the same or better predictive power as the model calibrated solely on the RP data (which is the best possible model for that data, in terms of goodness- of-fit figures of merit), when measured in terms of Pearson's Chi-squa red ratio and the percent correctly predicted statistic. The sequentia l approach is also shown to produce predictions with lower error than produced by the more usual method of pooling the RP and SP data.