This paper describes an approach for the development of prediction models for the estimation of mileage-related vehicle depreciation that can be used in the estimation of the benefits derived from transportation network improvements. The approach takes advantage of published online data for vehicle valuations. A new asymmetric logistic prediction model for total vehicle depreciation, including initial and mileage-related depreciations, is proposed and fitted to collected valuations data. The added benefit of this prediction model is that it takes into consideration both vehicle age (i.e., years since manufacture) and vehicle usage (i.e., miles of travel). Six small light-duty vehicles (SLDVs), five large light-duty vehicles (LLDVs), three two-axle trucks, one single-unit truck, and two combination trucks were considered in this study. Vehicle fuel sources included gasoline, diesel, gasoline-ethanol blend of up to 85% ethanol (E85), and hybrid-electric, resulting in 26 combinations of vehicle type and fuel source. Additionally, the developed models were adjusted to account for the effects of average speed of vehicle and roadway characteristics (e.g., grade, curvature) on vehicle depreciation. The practicality of the developed models for large sport utility vehicles (SUVs) and midsize cars was illustrated using select examples highlighting the models’ sensitivity to vehicle average speed and roadway characteristics.

本文介绍了一种开发用于估算与里程相关的车辆折旧的预测模型的方法，该方法可用于估算交通网络改善带来的收益。该方法利用已发布的在线数据进行车辆评估。提出了一种新的用于车辆总折旧的不对称逻辑预测模型，包括初始折旧和与里程相关的折旧，并将其拟合到所收集的估值数据中。该预测模型的附加好处是，它同时考虑了车辆使用年限（即自制造以来的年数）和车辆使用情况（即行驶里程）。在这项研究中，考虑了六辆小型轻型车辆（SLDV），五辆大型轻型车辆（LLDV），三辆两轴卡车，一辆单体卡车和两辆组合卡车。车辆燃料来源包括汽油，柴油，高达85％的乙醇（E85）的汽油-乙醇混合物和混合动力汽车，从而形成了26种车辆类型和燃料来源的组合。此外，对开发的模型进行了调整，以考虑平均车辆速度和道路特性（例如坡度，曲率）对车辆折旧的影响。通过选择示例说明了大型运动型多功能车（SUV）和中型车的已开发模型的实用性，这些示例突出了模型对车辆平均速度和道路特性的敏感性。对开发的模型进行了调整，以考虑平均车辆速度和道路特性（例如坡度，曲率）对车辆折旧的影响。通过选择示例说明了大型运动型多功能车（SUV）和中型车的已开发模型的实用性，这些示例突出了模型对车辆平均速度和道路特性的敏感性。对开发的模型进行了调整，以考虑平均车辆速度和道路特性（例如坡度，曲率）对车辆折旧的影响。使用精选示例说明了大型运动型多功能车（SUV）和中型车的已开发模型的实用性，这些示例突出了模型对车辆平均速度和道路特性的敏感性。