Xu Yueru, Ye Zhirui, Wang Yuan, Wang Chao, Sun Cuicui
a Jiangsu Key Laboratory of Urban ITS , Southeast University , Nanjing , China.
b Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies , Southeast University , Nanjing , China.
Traffic Inj Prev. 2018;19(6):601-606. doi: 10.1080/15389588.2018.1471599. Epub 2018 Aug 7.
This article focuses on the effect of road lighting on road safety at accesses to quantitatively analyze the relationship between road lighting and road safety.
An artificial neural network (ANN) was applied in this study. This method is one of the most popular machine learning methods and does not require any predefined assumptions. This method was applied using field data collected from 10 road segments in Nanjing, Jiangsu Province, China.
The results show that the impact of road lighting on road safety at accesses is significant. In addition, road lighting has a greater influence when vehicle speeds are higher or the number of lanes is greater. A threshold illuminance was also found, and the results show that the safety level at accesses will become stable when reaching this value.
Improved illuminance can decrease the speed variation among vehicles and improve safety levels. 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.
本文聚焦于道路照明对出入口处道路安全的影响，以定量分析道路照明与道路安全之间的关系。
本研究应用了人工神经网络（ANN）。该方法是最流行的机器学习方法之一，且无需任何预先设定的假设。使用从中国江苏省南京市10个路段收集的现场数据应用此方法。
结果表明，道路照明对出入口处道路安全的影响显著。此外，当车速较高或车道数量较多时，道路照明的影响更大。还发现了一个阈值照度，结果表明当达到该值时，出入口处的安全水平将趋于稳定。
提高照度可减少车辆间的速度差异并提高安全水平。此外，等级较高的道路在出入口处需要更好的照度。还可根据相关变量获得一个阈值，并用于为交通管理机构制定科学指南。
