人工神经网络在水平曲线上的服务水平导出：埃及案例研究

Abstract

PurposeMulti-lane highways represent the majority of the total length of highway network at many countries. The geometric design of such facilities and the traffic volume which includes heavy vehicles percentage (HV) are considered the most important factors affecting the level of service (LOS), especially on the horizontal curves.MethodsThis paper aims to explore the relationship between the road geometric characteristics including horizontal curve properties and traffic volume including average annual daily traffic (AADT) and HV in one hand and LOS in the other hand by two methods. First is generalized linear modeling (GLM) procedure and second is the artificial neural networks (ANNs) procedure. In this research, the traffic and road geometric data are collected on 78 horizontal curves that are distributed on four multi-lane highways in Egypt. Two of them are located in desert area and the others in agricultural area.ResultsThe Analyses showed that the ANNs procedure give the best models for estimating LOS. Also, the most influential variables on LOS are AADT, HV, and radius of horizontal curve (R). Finally, the derived models have statistics within the acceptable regions and, also, they are conceptually reasonable.ConclusionsThe previous inferences are so important for road authorities in Egypt as they can consider them as a first step for origination of Egyptian highway capacity manual.