A techno-economic model is developed based on road-load simulation results expressed in relation to slip energy (SE) at the tire–pavement interface and the repair and maintenance (R&M) cost obtained from published sources and data from state agencies. Tire SE allows for the consideration of aggressive acceleration and deceleration, high torque conditions (for instance, driving an upslope grade), and roadway curvature. Tire SE data used in this effort were generated using physics-based simulation models of different vehicle types for arrays of road conditions (e.g., grades, curvatures) and driving cycles (i.e., vehicle speed profiles). R&M costs were estimated for various vehicle categories and accumulated vehicle mileage. The approach is based on relating the probability density functions (PDFs) of SE and R&M costs. Asymptotic series expansion for an incomplete gamma function was used to approximate the gamma functions and to determine the gamma ratio function that is used as the coefficient to SE to estimate R&M costs. The average R&M cost per mile results from the model compared with the arithmetic mean R&M cost data from fleet operators and published data. The model can serve as a method for predicting R&M cost as a function of road load to vehicle fleet.

基于道路载荷模拟结果开发了一种技术经济模型，该结果与轮胎-路面界面的滑移能量（SE）以及从公开来源和国家机构提供的数据获得的维修费用（R＆M）有关。轮胎SE可以考虑剧烈的加速和减速，高扭矩条件（例如，驾驶上坡坡度）和道路曲率。使用针对道路条件（例如，坡度，曲率）和行驶周期（即，车辆速度曲线）的数组的不同车辆类型的基于物理的仿真模型，生成了此工作中使用的轮胎SE数据。估算了各种车辆类别和累计车辆行驶里程的R＆M成本。该方法基于关联SE和R＆M成本的概率密度函数（PDF）。使用不完整伽玛函数的渐近级数展开来逼近伽玛函数，并确定伽玛比函数，将其用作SE的系数以估计R＆M成本。该模型得出的每英里平均R＆M成本与车队运营商的算术平均R＆M成本数据和已发布的数据进行了比较。该模型可以用作预测R＆M成本随车队道路负载变化的方法。