CHAPTER 6 CONCLUSION AND RECOMMENDATION
6.2 R ECOMMENDATION
Although this study has taken a step forward in the direction of examining accident characteristics and causality from chain perspective, some limitations should be noticed and some findings are worth further studies.
1. This study is a new attempt to apply rough sets as a complementary tool for accident analyses. A lot of information is still embedded in the derived rules that might provide useful knowledge for researchers and analysts and requires further exploration.
Advanced models, however, should be considered in the future to improve and to address the issues related to performance of rule extraction and case validation. For example, besides the AND operator, one could consider other logic operators such as OR or NOT into rule generations.
2. The proposed approaches can be adopted in other datasets or be used to analyze different accident outcomes. These approaches were analyzed in analyzing single auto-vehicle accidents. Such accidents involve only a single vehicle and thus the underlying process of accident occurrence would be much simpler than other accident types such as multi-vehicle accidents. One should carefully examine her or his on-hand data to determine which subjects to examine.
3. Comparisons between rough sets and other methodologies would be very interesting.
However, it is necessary to have a very careful design to conduct these comparisons;
particularly, the nature of rough sets theory is quite different from other methodologies.
4. Possible aggregation biases and the overwhelming numbers of rules have limited this research. The derived rules could help reveal the aggregation bias in the process of retrieving contributing factors. To resolve the issue of aggregation bias and shed light on the whole features of accidents by using the rule based approach, however, needs further research.
5. Although this approach allows the control of all relevant factors, it does not mean that the findings under this approach must be the true causal factors. The primary reason is the limited information provided by accident databases. Accidents are observable only after they have occurred. Some information is thus difficult to obtain especially for the fatal accidents. For example, vehicle features are critical to accident severity, and exposure data are critical to claim the relatively high frequency of a rule. But it is a
pity that such information is not provided in the database. Consequently, the uncontrolled confounding factors should be carefully taken into account in ascertaining the findings and require further studies.
6. Experimental designs for exploring driving behaviors would be helpful to complement the aforementioned shortcoming. In particular, these designs could be based on the interested rules; for example, the most significant rule leads to fatal accidents. Since a rule contains rich information, the corresponding experimental design would be specific and effective.
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