@article{ref1,
title="Evaluating the influence of road lighting on traffic safety at accesses using an artificial neural network",
journal="Traffic injury prevention",
year="2018",
author="Xu, Yueru and Ye, Zhirui and Wang, Yuan and Wang, Chao and Sun, Cuicui",
volume="19",
number="6",
pages="601-606",
abstract="OBJECTIVES: This paper focuses on the effect of road lighting on road safety at accesses and tries to quantitatively analyze the relationship between road lighting and road safety. <br><br>METHODS: An Artificial Neural Network (ANN) was applied in this study. This method is one of the most popular machine-learning methods in recent years and does not require any pre-defined assumptions. This method was applied using field data collected from ten road segments in Nanjing, Jiangsu Province, China. <br><br>RESULTS: The results show that the impact of road lighting on road safety at accesses is significant. In addition, road lighting has greater influence when vehicle speeds are higher or the number of lanes is larger. A threshold illuminance was also found in this paper, and the results show that the safety level at accesses will become stable when reaching this value. <br><br>CONCLUSIONS: The improvement of illuminance can decrease the speed variation among vehicles and improve the safety level. In addition, high-grade roads need better illuminance at accesses. A threshold value can also be obtained based on related variables and used to develop scientific guidelines for traffic management organizations.<p /> <p>Language: en</p>",
language="en",
issn="1538-9588",
doi="10.1080/15389588.2018.1471599",
url="http://dx.doi.org/10.1080/15389588.2018.1471599"
}