| Summary: | 碩士 === 國立交通大學 === 運輸與物流管理學系 === 106 === Physical condition of bus drivers is one of the major factors that affect passenger safety in public transportation. In addition to living habits and medical history, heavy workload, irregular work shifts (drivers’ schedule) and high pressure from driving under complex traffic conditions may all cause negative effects on the physical condition of bus drivers and increase their risk of suffering from cardiovascular disease. Previous relevant studies used periodical physical exam results to analyze the factors that affect cardiovascular conditions of bus drivers. Very few had considered the impact of driving workload and schedule on drivers’ cardiovascular conditions. To fill this research gap, this study applies several data mining techniques to explore the factors that may affect bus drivers’ cardiovascular conditions. We utilize the measurement data of drivers’ daily cardiovascular conditions (heartbeat rates and blood pressure) and driving schedule provided by a domestic intercity bus company and conduct a survey to collect the living habits and medical history data of the drivers in that company. The results show that the decision tree algorithm identifies 10 important factors, including medical history, late night habit and number of afternoon or evening duties. The association rule method obtains 17 important variables, including driving time in the afternoon and evening and number of duties in peak hours and weekends. This study further builds artificial neural networks to evaluate and compare the two sets of variables obtained by the decision tree algorithm and the association rule method, respectively. The result indicates that the prediction accuracy of the set of variables obtained by the decision tree algorithm is better than that obtained by the association rule method. The findings of this study can be used as a reference in driver health management and scheduling for bus companies to improve driving safety of public transportation.
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