Road Accidents Prediction with Multilayer Perceptron MLP modelling Case Study: Roads of Qazvin, Zanjan and Hamadan

Document Type : Original Article

Authors

DepartmentofCivilEngineering,IranUniversityofScienceandTechnology,Tehran,Iran.

Abstract

Demand growth this has increased the incidence of road accidents and the resulting casualties, including injuries and deaths. In this study, six of the rural two lane roads were selected as the study area and crash data was collected in the roads for 2013-2016 years. In this study, multi-la ered perceptron model was used for modeling crashes for different roads. The purpose of the multi-layered perceptron model training is to find the optimal value of weights and biases in such a way as to minimize network error. With this view, multi-layered perceptron modeling is an optimization issue with a number of specific parameters. Based on the collected data, the studied roads included Hamadan to Avaj, Hamedan to Qorveh, Hamadan to Malayer and Hamadan to Bijar in the area of the protection of Hamadan province, as well as Abhar to Qeydar in the area of protection of Zanjan province and the old road to Abeek to Qazvin, in Qazvin province. An appropriate model for the roads of Qazvin, Zanjan and Hamadan was architectu e. Approximately good results were obtained from the network. The value of the r2 statistic that was calculated was 0.83. The value of the MSE parameter equals to 0.59, which indicates the accuracy of the results in the training phase. For the roads of the Qazvin region, the value of r2 was 0.94. The value of the MSE parameter was also 0.33, which was very good, and showed the accuracy of the results in the training phase.

Keywords

Main Subjects


1. Norheim OF, Jha P, Admasu K, Godal T, Hum RJ, Kruk ME, et al. Avoiding 40% of the premature deaths in each country, 2010–30: review of national mortality trends to help quantify the UN Sustainable Development Goal for health. The  Lancet. 2015;385(9964):239-52.
2. Mackenbach JP, Kulhánová I, Menvielle G, Bopp M, Borrell C, Costa G, et al. Trends in inequalities in premature mortality: a study of 3.2 million deaths in 13 European countries. J Epidemiol Community Health. 2015;69(3):207-17.
3. Wiegmann DA, Shappell SA. A human error approach to aviation accident analysis: The human factors analysis and classification system: Routledge; 2017.
4. Goniewicz K, Goniewicz M, Paw┼éowski W, Fiedor P, Lasota D. Risk of road traffic     accidents     in     children.     Medical     Studies/Studia Medyczne. 2017;33(2):155-60.
5. Zhang G, Yau KK, Zhang X, Li Y. Traffic accidents involving fatigue driving and their extent of casualties. Accident Analysis & Prevention. 2016;87:34-42.
6. Mansuri FA, Al-Zalabani AH, Zalat MM, Qabshawi RI. Road safety and road traffic accidents in Saudi Arabia: a systematic review of existing evidence. Saudi medical journal. 2015;36(4):418.
7. Administration NHTS. Traffic safety facts 2011 data--pedestrians. Annals of emergency medicine. 2013;62(6):612.
8. Shaker h, bigdeli rh. evaluation and simulation of new roundabouts traffic parameters by aimsun software. 2018.
9. Abdi A, Bigdeli Rad H, Azimi E, editors. Simulation and analysis of traffic flow for traffic calming. Proceedings of the Institution of Civil Engineers- Municipal Engineer; 2016: Thomas Telford Ltd.
10. Nabipour AR, Nakhaee N, Khanjani N, Moradlou HZ, Sullman MJ. The road user behaviour of school students in Iran. Accident Analysis & Prevention. 2015;75:43-54.
11. Bigdeli Rad H, Bigdeli Rad V. A Survey on the Rate of Public Satisfaction about Subway Facilities in the City of Tehran Using Servqual Model. Space Ontology International Journal. 2018;7(1):11-7.
12. Huang H, Song B, Xu P, Zeng Q, Lee J, Abdel-Aty M. Macro and micro models for zonal crash prediction with application in hot zones identification. Journal of transport geography. 2016;54:248-56.
13. Kong ZGJGL. The road safety situation investigation and characteristics analysis of black spots of arterials highways. red. 2003;1:0000.
14. Consiglio W, Driscoll P, Witte M, Berg WP. Effect of cellular telephone conversations and other potential interference on reaction time in a braking response. Accident Analysis & Prevention. 2003;35(4):495-500.
15. Ansari S, Akhdar F, Mandoorah M, Moutaery K. Causes and effects of road traffic accidents in Saudi Arabia. Public health. 2000;114(1):37-9.
16. Zakeri H, Kadkhodazadeh K. Review of Contributing Factors in Road Traffic Accidents in Iran. 2015 284060423X.
17. Chen Y, Li Y, King M, Shi Q, Wang C, Li P. Identification methods of key contributing factors in crashes with high numbers of fatalities and injuries in China. Traffic injury prevention. 2016;17(8):878-83. 
18. Badawy A, Morsy N, Abdelhafez S, El-Gilany A, Shafey M. Role of Sleepiness in Road Traffic Accidents among Young Egyptian Commercial Drivers. SM J Sleep Disord. 2016;2(1):1002.
19. Zeng H, Schrock SD. Safety Effectiveness of Various Types of Shoulders on Rural Two-Lane Roads in Winter and Non-winter Periods. 2013.
20. Turner B, Steinmetz L, Lim A, Walsh K. Effectiveness of road safety engineering treatments2012.
21.  Turner  B,  Affum  J,  Tziotis  M,  Jurewicz  C.  Review  of  iRAP  Risk Parameters. ARRB Group Contract Report for iRAP. 2009.
22. Jones B, Janssen L, Mannering F. Analysis of the frequency and duration of   freeway   accidents   in   Seattle.   Accident   Analysis   &   Prevention. 1991;23(4):239-55.
23.  Shankar  V,  Mannering  F,  Barfield  W.  Statistical  analysis  of  accident severity on rural freeways. Accident Analysis & Prevention. 1996;28(3):391- 401.
24. Abdel-Aty M, Pande A. Identifying crash propensity using specific traffic speed conditions. Journal of safety Research. 2005;36(1):97-108.
Volume 2, Issue 4
December 2018
Pages 181-192
  • Receive Date: 17 June 2018
  • Revise Date: 30 August 2021
  • Accept Date: 11 October 2018
  • First Publish Date: 01 December 2018