PURPOSES : In this study, the main factors affecting the severity of traffic accidents among elderly drivers were reviewed, and accident factors with a high accident risk were analyzed. This provided basic data for preparing a traffic safety system for elderly drivers and establishing policies. METHODS : Based on machine learning, the major factors influencing accident severity (from the analysis of traffic accident data for elderly drivers) were analyzed and compared with existing statistical analysis results. The machine learning algorithm used the Scikit-learn library and Python 3.8. A hyperparameter optimization process was performed to improve the safety and accuracy of the model. To establish the optimal state of the model, the hyperparameters were set (K = 5) using K-fold cross-validation. The hyperparameter search applied the most widely utilized grid search method, and the performance evaluation derived the optimal hyperparameter value using neutral squared error indicators. RESULTS : The traffic laws, road sections of traffic accidents, and time zones of accidents were analyzed for accidents involving elderly drivers in Daejeon Metropolitan City, and the importance of the variables was examined. For the analysis, a linear regression model, machine learning-based decision tree, and random forest model were used, and based on the root mean square error, the random forest accuracy performance was found to be the best. Ultimately, 18 variables were analyzed, including traffic violations, accident time zones, and road types. The variables influencing the accident severity were the speed, signal violation, intersection section, late-night driving, and pedestrian protection violation, with the relative importance of the variables in the order of speed (0.3490966), signal violation (0.285967), and late-night driving (0.173108). These can be seen as variables related to the expansion of life damage owing to physical aging and reduced judgment abilities arising from decreases in cognitive function. CONCLUSIONS : Restricting the driving of the elderly on the expressway and at night is reasonable, but specific standards for driving restrictions should be prepared based on individual driving capabilities.

PURPOSES : Presently, it is impossible to evaluate the actual number of operational plans and punctuality of bus routes. In this study, a methodology for separating bus stops and sorting departure orders was developed using city bus operation information, and its field applicability was examined. METHODS : Based on current bus data, the "moving speed (km/h)," "Lag data," and "Lead data" were generated. The number of stops was classified based on the location where the order of the stops changed for each vehicle, and an arbitrary ID was assigned for each number of stops. In the case of a preceding vehicle, for each rotation information, a time interval was calculated based on the same point, and the rotation with the smallest time interval was determined as the preceding vehicle. RESULTS : An evaluation was conducted on Bus Route 201 in Daejeon. The vehicles were classified based on the operation data of the previous day. From analyzing the operation time and distance for each operation based on the last stop and starting point, it was found that both the number of operation plans and performance were consistent. The punctuality was analyzed and was found to be inversely proportional to the difference between the stop arrival time and preset arrival time, preset dispatch interval, and actual dispatch interval. The on-time(regularity) ratio was determined as 98.0% and the early departure ratio was 99.3%, indicating very good regularity. CONCLUSIONS : The research results can be used by local governments with insufficient levels of public transportation-based data management, and can be used as basic data for establishing public transportation policies. The results of this study can be referred to when evaluating the transparency of financial support for public transportation operators and operations, with the aim of enhancing public transportation services.