This study explored the question of how to minimize older drivers' accidents and to identify at-risk drivers by analyzing their driving performance. Previous traffic research reported that there were two factors involved in risky driving, namely driving risk perception and risky driving attitude. We investigated these two factors as indicators of an at-risk driver by using large-scale test data from license renewal tests that are obligatory for Japanese drivers who are 70 years of age or older. The tests include a driving simulator test, an on-road test, and a cognitive screening test. By using these assessments and predictions made with renewal driving tests, we were able to indicate the possibility of identifying at-risk drivers.

We developed a simple, practical, adaptive data compression algorithm of the LZ78 class. According to the Lempel-Ziv greedy parsing, a string boundary is not related to the statistical history modeled by finite-state sources. We have already reported an algorithm classifying data into subdictionaries (CSD), which uses multiple subdictionaries and conditions the current string by using the previous one to obtain a higher compression ratio. In this paper, we present a practical implementation of this method suitable for any kinds of data, and show that CSD is more efficient than the LZC which is the method used by the program compress available on UNIX systems. The CSD compression performance was about 10% better than that of LZC with the practical dictionary size, an 8k-entry dictionary when the test data was from the Calgary Compression Corpus. With hashing, the CSD processing speed became as fast as that of LZC, although the CSD algorithm was more complicated than LZC.