The focus of this research is to model the influence of road, socioeconomic, and land-use characteristics on local road annual average daily traffic (AADT) and assess the model's predictability in non-covered location AADT estimation. Traditional ordinary least square (OLS) regression and geographically weighted regression (GWR) methods were explored to estimate AADT on local roads. Ten spatially distributed counties were considered for county-level analysis and modeling. The results indicate that road density, AADT at the nearest nonlocal road, and land use variables have a significant influence on local road AADT. The GWR model is found to be better at estimating the AADT than the OLS regression model. The developed county-level models were used for estimating AADT at non-covered locations in each county. The methodology, findings, and the AADT estimates at non-covered locations can be used to plan, design, build, and maintain the local roads in addition to meeting reporting requirements. The prediction error is found to be higher at urban areas and in counties with a smaller number of local road traffic count stations. Recommendations are made to account for influencing factors and enhance the local road count-based AADT sampling methodology.

这项研究的重点是对道路，社会经济和土地利用特征对当地道路年均日流量（AADT）的影响进行建模，并评估该模型在未覆盖位置AADT估算中的可预测性。探索了传统的普通最小二乘（OLS）回归和地理加权回归（GWR）方法来估计本地道路上的AADT。考虑了10个空间分布的县，以进行县级分析和建模。结果表明，道路密度，最近的非本地道路的AADT和土地使用变量对本地道路AADT都有显着影响。发现GWR模型比OLS回归模型在估计AADT方面更好。所开发的县级模型用于估计每个县未覆盖地点的AADT。方法，发现，除了满足报告要求外，AADT在未覆盖地区的估算值还可用于规划，设计，建造和维护当地道路。发现在城市地区和当地道路交通计数站点数量较少的县，预测误差较高。建议考虑到影响因素，并增强基于本地道路计数的AADT抽样方法。