長期以來，交通事故的預防一直是運輸領域的重要議題。歷史統計資料顯示，涉及貨車的事故總數並不比其他車種多，但其肇事率和傷亡率卻較一般小客車要高得多。除了人員傷亡外，貨車事故亦會帶來龐大的財務損失及交通延滯，而人為因素是導致車輛失事的最主要原因。因此，貨車公司必須通過實施具有成本效益的措施來管理卡車司機。本研究以先前研究的駕駛風險評估流程為基礎，進行了一些調整與修改。研究結合了Jenks 自然斷裂優化 (JNBO) 和模糊邏輯的概念於風險等級的評估中，提高評估流程中的的邏輯性與靈活性，並以加權的方式考量偏差駕駛行為對駕駛風險的不同影響。此外，本研究還通過分類和回歸樹 (CART) 建立了決策樹模型。透過這些模型，我們可以更直觀、更清楚地從決策的角度知悉駕駛行為對風險等級的影響，並省略繁瑣的計算更迅速地對駕駛員進行風險分級。

Prevention of traffic accidents has been a big issue in transportation system for a long time. Historical statistics show that the number of accidents involving trucks is not many compared to those involving other vehicle types, yet the crash rate and the casualty rate of truck crashes are much higher than those of sedans. Truck crashes also cause huge financial losses and traffic jams apart from casualties. Therefore, truck companies must manage truck drivers by implementing cost-effective measures. This study proposed a revised approach based upon previous studies assessing driving risk of commercial vehicles. Combining Jenks natural breaks optimization (JNBO) and fuzzy logic has increased the logicality and flexibility of the evaluation system. Different effects on accident risk of aberrant driving behaviors are taken into account by weighting mechanism. Moreover, this study establishes decision tree models through the Classification and regression tree (CART). We could more intuitively and clearly illustrate the impact of each driving behavior on the risk level from the perspective of decision-making by the decision tree models. These tree models provide a quick and visible tool to grade the driving risk levels of drivers.

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