駕駛工作為具有連續性的工作性質，任何一時的疏忽即會造成交通意外事故發生。對國道客運公司而言，駕駛員精神狀況不佳，不但會對行車安全造成威脅並且影響服務的品質，若發生交通意外事故，還會對國道客運公司的形象產生負面的作用。因此，若能瞭解可能會造成駕駛疲勞的因素，並事先進行管理，將可以有效降低疲勞駕駛事故的發生。
本研究擬透過問卷訪談與數位式行車記錄器資料庫，收集過去研究認為可能會影響駕駛員疲勞的資訊，使用類神經網路與羅吉斯迴歸模型來建立兩種疲勞模式，比較使用不同方法建構模式的預測結果，以及影響駕駛疲勞的因子，作為管理者進行駕駛員管理與調度時考量的依據。
研究結果指出，使用類神經網路來建構疲勞模式時，在準確率方面的表現稍微高於羅吉斯迴歸模型的表現。另外，根據羅吉斯迴歸模型的結果發現，影響國道客運駕駛員疲勞的因素為開車前的睡覺時數與今日有無在午夜到清晨這段時間開車；而影響國道客運駕駛員感覺疲勞程度不同的因素為服務年資、開車前睡覺醒來次數、打鼾、過去一個禮拜累積開車時數與過去一個禮拜在午夜到清晨時段開車的次數。

Driving is a continuous work nature, any negligence of the driver will have impact on traffic safety and even cause a traffic accident. As to the inter-city bus company, if the spiritual state of the driver is not well, it can threaten the traffic safety and influence the quality of service, and once the traffic accident occurs, it will have negative effect on the image of the inter-city bus company. Therefore, if we understand the factors to fatigue and take effective countermeasures in advance, the number of accidents to fatigue driving will be reduced.
This study pursuant past research experience to collect relative information of drivers by questionnaire interview and database of digital vehicle recorder then uses Artificial Neural Network (ANN) and logistics regression to establish two kinds of fatigue models. After the model has been established, the accuracy performance of the models established differently can be compared and the factors of driver fatigue can be determined.
This study conclusion has pointed out, the performance accuracy of ANN is slightly higher than the performance accuracy of logistics regression when constructing a fatigue pattern. In addition, according to the result of logistics regression, the sleep hours of the driver prior to driving and whether having to drive from mid-night to early morning hours on the same day are significant factors to driver fatigue. And the seniority of the driver, number of times awaken during the sleep, snoring, the accumulated driving hours in the previous week and amount of times driving between mid-night to early morning hours are significant factors to degrees of fatigue.

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