Drivers have distinctively diverse behaviors when operating vehicles in natural traffic flow, such as preferred pedal position, car-following distance, preview time headway, etc. These highly personalized behavioral variations are known to impact vehicle fuel economy qualitatively. Nevertheless, the quantitative relationship between driving behaviors and vehicle fuel consumption remains obscure. Addressing this critical missing link will contribute to the improvement of transportation sustainability, as well as understanding drivers’ behavioral diversity. This study proposed an integrated microscopic driver behavior and fuel consumption model to assess and predict vehicle fuel economy with naturalistic highway and local commuting traffic data. Through extensive Monte Carlo simulations, significant correlation results are revealed between specific individual driving preferences and fuel economy over drivers’ frequent commuting routes. Correlation results indicate that the differences in fuel consumption incurred by various driving behaviors, even in the same traffic conditions, can be as much as 29% for a light-duty truck and 15% for a passenger car. A Gaussian Process Regression model is further trained, validated, and tested under different traffic and vehicle conditions to predict fuel consumption based on drivers’ personalized behaviors. Such a quantitative and personalized model can be used to identify and recommend fuel-friendly driving behaviors and routes, demonstrating a strong incentive for relevant stakeholders.

中文翻译：

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现实通勤交通中的个性化驾驶行为和燃油经济性：建模、关联和预测


在自然交通流中驾驶车辆时，驾驶员有明显不同的行为，例如首选踏板位置、跟车距离、预览车头时距等。众所周知，这些高度个性化的行为变化会定性地影响车辆的燃油经济性。然而，驾驶行为与车辆燃油消耗之间的定量关系仍然模糊。解决这一关键缺失环节将有助于提高交通可持续性，并了解驾驶员的行为多样性。本研究提出了一种集成的微观驾驶员行为和燃油消耗模型，利用自然高速公路和当地通勤交通数据来评估和预测车辆燃油经济性。通过广泛的蒙特卡罗模拟，揭示了特定的个人驾驶偏好与驾驶员频繁通勤路线的燃油经济性之间的显着相关性结果。相关结果表明，即使在相同的交通条件下，不同的驾驶行为所产生的油耗差异，轻卡可达29%，客车可达15%。高斯过程回归模型在不同的交通和车辆条件下经过进一步训练、验证和测试，以根据驾驶员的个性化行为预测油耗。这种定量和个性化的模型可用于识别和推荐节能驾驶行为和路线，为相关利益相关者展示强大的激励作用。