Evaluating the influence of road lighting on traffic safety at accesses using an artificial neural network

Traffic Injury Prevention
Yueru XuCuicui Sun

Abstract

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.

References

Nov 1, 2005·Accident; Analysis and Prevention·Letty Aarts, Ingrid van Schagen
Sep 2, 2008·Accident; Analysis and Prevention·Christopher M Monsere, Edward L Fischer
Dec 31, 2008·Accident; Analysis and Prevention·Per Ole Wanvik
May 20, 2009·Traffic Injury Prevention·Per Ole Wanvik
Feb 5, 2013·Accident; Analysis and Prevention·John D BulloughMark S Rea
Sep 15, 2015·Journal of Sleep Research·Royette TavernierEmma K Adam

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Citations

Nov 27, 2019·Sensors·María García-CastellanoVentura Pérez-Mira
Mar 9, 2021·Environmental Science and Pollution Research International·Chao WangHui Bi

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