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78 Hazardous Materials Transportation Incident Data for Root Cause Analysis Table 4-25. Relationship between functional class and interchange in LTCCS. Interchange Functional Class No Yes Rural local 2 Rural minor arterial 4 Rural principal arterial Interstate 1 1 Rural principal arterial other 3 Urban minor arterial 2 Urban principal arterial Interstate 15 10 Urban principal arterial other 2 In a typical data analysis, it is difficult to analyze just a few accidents. Thus, while it is possible to look at the decision factors associated with those 11 interchange accidents, the statistical uncertainty regarding the conclusion will be very high. Clearly, to identify significant differences would require more hazmat truck accidents in the dataset. Just as the LTCCS targeted large truck accidents involving a serious injury or fatality, a com- parable study that focused on hazmat accidents would provide a similar benefit. Rather than doing a two-year study of 1,000 truck accidents, there appears to be merit to doing a continual study of fewer truck accidents, perhaps 100 to 200 per year. To look for differences between haz- mat truck accidents and regular truck accidents, it would be important to have data for both, perhaps a sample of 100 regular truck accidents and 100 hazmat truck accidents. If such a study were performed on an annual basis, it is important to have weighting factors to enable the find- ings from a limited sample of accidents to be related to the universe of accidents occurring annu- ally. These can be developed as part of the sampling methodology or come from other databases such as MCMIS and HMIRS. 4.5.9 Summary and Potential Measures to Improve Root Cause Analysis The analysis of the data from the LTCCS is still ongoing, so the following summary is based on its status as of the time of this report. The potential measures are prepared to focus on the objectives of this project. Summary The LTCCS represents a comprehensive analysis of serious, large truck crashes. The variables captured in the 967 accidents investigated by contributing cause category are shown in Table 4-26. As shown, all of the contributing factors listed under the categories for Vehicle and Situational and most of the contributing factors under the categories for Driver and Infrastructure are covered. The Infrastructure category's factors are actually known by the LTCCS analysts, but have been coded to prevent these data from being known by those outside the LTCCS program. Thus, the training and experience of the driver were the only contributing causes that are not captured under the Driver category. The Packaging category is not well captured, since package behavior was not the focus of the LTCCS. Potential Improvements Based on the LTCCS Experience Comprehensive studies, such as the LTCCS, are needed to obtain contributing and root causes of accidents. Similar to the LTCCS, these detailed analyses can be focused on a sample of all the accidents occurring in the United States, provided that the weighting of the sampling is known.