In this study, 98 regression models were specified for easily estimating shortest distances based on great circle distances along the U.S. interstate highways nationwide and for each of the continental 48 states. This allows transportation professionals to quickly generate distance, or even distance matrix, without expending significant efforts on complicated shortest path calculations. For simple usage by all professionals, all models are present in the simple linear regression form. Only one explanatory variable, the great circle distance, is considered to calculate the route distance. For each geographic scope (i.e., the national or one of the states), two different models were considered, with and without the intercept. Based on the adjusted R-squared, it was observed that models without intercepts generally have better fitness. All these models generally have good fitness with the linear regression relationship between the great circle distance and route distance. At the state level, significant variations in the slope coefficients between the state-level models were also observed. Furthermore, a preliminary analysis of the effect of highway density on this variation was conducted.

在这项研究中，指定了 98 个回归模型，以便根据美国全国州际公路沿线的大圆距离以及大陆 48 个州中的每一个州的大圆距离轻松估计最短距离。这允许运输专业人员快速生成距离，甚至距离矩阵，而无需在复杂的最短路径计算上花费大量精力。为了所有专业人士的简单使用，所有模型都以简单的线性回归形式存在。计算路径距离时只考虑一个解释变量，即大圆距离。对于每个地理范围（即国家或州之一），考虑了两种不同的模型，有截距和没有截距。基于调整后的 R 平方，观察到没有截距的模型通常具有更好的适应度。所有这些模型一般都具有良好的适应度，大圆距离和路线距离之间的线性回归关系。在州一级，还观察到州级模型之间斜率系数的显着变化。此外，对公路密度对这种变化的影响进行了初步分析。