Volume 24, Issue 3 (10-2021)
jha 2021, 24(3): 67-78 |
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Moslehi S, Gholami A, Haghdoust Z, Abed H, Mohammadpour S, Moslehi M A. Prediction of traffic accidents based on weather conditions in Gilan province using artificial neural network. jha 2021; 24 (3) :67-78
URL: http://jha.iums.ac.ir/article-1-3800-en.html
URL: http://jha.iums.ac.ir/article-1-3800-en.html
Shandiz Moslehi1 
, Arsalan Gholami2 , Zahra Haghdoust 3, Hosein Abed4 , Saman Mohammadpour5 , Mohammad Ashkan Moslehi6
, Arsalan Gholami2 , Zahra Haghdoust 3, Hosein Abed4 , Saman Mohammadpour5 , Mohammad Ashkan Moslehi6
1- Assistant Professor, Department of Health in Disasters and Emergencies, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
2- M.Sc, Health Management and Economics Research Center,Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran.
3- Ph.D Student, Department of Health in Disasters and Emergencies, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran. ,Haghdoust.z@tak.iums.ac.ir
4- M.Sc,Meteorological Research Center, General Meteorological Department of Gilan Province, Gilan, Iran.
5- Ph.D Student, Department of Health Information Management and Technology, School of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
6- Assistant Professor, Department of Pediatrics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
2- M.Sc, Health Management and Economics Research Center,Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran.
3- Ph.D Student, Department of Health in Disasters and Emergencies, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran. ,
4- M.Sc,Meteorological Research Center, General Meteorological Department of Gilan Province, Gilan, Iran.
5- Ph.D Student, Department of Health Information Management and Technology, School of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
6- Assistant Professor, Department of Pediatrics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
Abstract: (2024 Views)
Introduction: Road traffic accidents are one of the leading causes of death worldwide, including Iran. There are several factors involved in the occurrence of them; using different models, these factors can be identified and the occurrence of road traffic accidents can be predicted. The purpose of this study was to predict road traffic accidents based on weather conditions using artificial neural network model.
Methods: In the present study, traffic data during the years 2014 to 2017, were examined using a multilayer perceptron network. Network input variables included minimum temperature, average temperature, average rainfall, maximum wind speed, glaciation, air pressure, fog concentration and output variable was the number of accidents.
Results: The designed network with seven neurons in the input layer, four neurons in the middle layer, and one neuron in the output layer with Lunberg-Marquardt optimization function and sigmoid tangent transfer function in the middle layer and linear transmission function in the output layer was selected as the optimal network. The results showed that the designed network with the correlation coefficient of 0.90 and mean square error of 0.01 has a high ability to predict road traffic accidents.
Conclusion: The results showed that the artificial neural network has a good performance for predicting road traffic accidents. Given the importance of predicting road traffic accidents and its role in promoting the health of people in such accidents, the results of this study can be used to expand more effective preventive measures for policy makers and researchers.
Methods: In the present study, traffic data during the years 2014 to 2017, were examined using a multilayer perceptron network. Network input variables included minimum temperature, average temperature, average rainfall, maximum wind speed, glaciation, air pressure, fog concentration and output variable was the number of accidents.
Results: The designed network with seven neurons in the input layer, four neurons in the middle layer, and one neuron in the output layer with Lunberg-Marquardt optimization function and sigmoid tangent transfer function in the middle layer and linear transmission function in the output layer was selected as the optimal network. The results showed that the designed network with the correlation coefficient of 0.90 and mean square error of 0.01 has a high ability to predict road traffic accidents.
Conclusion: The results showed that the artificial neural network has a good performance for predicting road traffic accidents. Given the importance of predicting road traffic accidents and its role in promoting the health of people in such accidents, the results of this study can be used to expand more effective preventive measures for policy makers and researchers.
Keywords: Forecasting, Traffic Accidents, Artificial Neural Network, Disaster Health, Health Management
Type of Study: Research |
Subject:
Health Information Management
Received: 2021/06/28 | Accepted: 2021/09/22 | Published: 2021/12/19
Received: 2021/06/28 | Accepted: 2021/09/22 | Published: 2021/12/19
References
1. World Health Organization. Global status report on road safety 2018: Summary. Geneva: World Health Organization; 2018. 403 p.
2. Nazmfar H, Eshghei Char Borj A, Alavi S, Jasaraty A. Spatial analysis of road accidents resulting in death approach to climate case study: Ardabil province. Geographical Data (SEPEHR). 2017;26(103):83-97. [In Persian]
3. Zhao Y, Andrey J, Deadman P. Whether conversion and weather matter to roundabout safety. J Safety Res. 2018 Jul 17;66:151-9. [DOI:10.1016/j.jsr.2018.07.001]
4. Haddon W. Advances in the epidemiology of injuries as a basis for public policy. Public Health Rep. 1980;95(5):411-21.
5. Ivajnsic D, Pintaric D, Grujic VJ, Ziberna I. A spatial decision support system for traffic accident prevention in different weather conditions. Acta geogr slov. 2021;61(1):75-92. [DOI:10.3986/AGS.9415]
6. Zou Y, Zhang Y, Cheng K. Exploring the impact of climate and extreme weather on fatal traffic accidents. Sustainability. 2021 Jan 14;13(1):1-14. [DOI:10.3390/su13010390]
7. Amin MSR, Zareie A, Amador-Jiménez LE. Climate change modeling and the weather-related road accidents in Canada. Transport Res Transport Environ. 2014;32:171-83. [DOI:10.1016/j.trd.2014.07.012]
8. Luo Z, Li J, Zhong M. Prediction of seasonal variation in traffic collisions on rural highways using neural network regression models: A case study in the province of british columbia. Proceedings of the 5th International Conference on Transportation Information and Safety (ictis 2019); 2019 Jul 14-17; Liverpool, UK. New York: IEEE; 2019 Oct 28. p. 281-5. [DOI:10.1109/ICTIS.2019.8883557]
9. Bartlett A, Lao W, Zhao Y, Sadek AW. Impact of inclement weather on hourly traffic volumes in Buffalo. Proceedings of the Transportation Research Board 92nd Annual Meeting [Internet]; 2013 Jan 13-17; Washington DC, United States. New York; 2013. p. 1-16. Available from: https://trid.trb.org/view/1241923
10. Sathiaraj D, Punkasem TO, Wang F, Seedah DPK. Data-driven analysis on the effects of extreme weather elements on traffic volume in Atlanta, GA, USA. Comput Environ Urban Syst. Forthcoming 2018. [DOI:10.1016/j.compenvurbsys.2018.06.012]
11. Hosseinian M, Najafi Moghaddam Gilani V, Tahmasbi Amoli H, Nikookar M, Orouei A. Presentation of analytical methods for better decision making about the most important factor influencing rural accidents. Math Probl Eng. 2021 Mar 20;2021:1-16. [DOI:10.1155/2021/5564269]
12. Hou G, Chen S. Study of work zone traffic safety under adverse driving conditions with a microscopic traffic simulation approach. Accid Anal Prev. 2020 Sep;145:1-46. [DOI:10.1016/j.aap.2020.105698]
13. Johal H, Schemitsch EH, Bhandari M. Why a decade of road traffic safety? J Orthop Trauma. 2014 Jun;28(6):S8-10. [DOI:10.1097/BOT.0000000000000104]
14. Andrey J, Olley R. The relationship between weather and road safety: Past and future research directions. Climatological Bulletin. 1990;24(3):123-37.
15. Andrey J, Yagar S. A temporal analysis of rain-related crash risk. Accid Anal Prev. 1993;25(4):465-72. [DOI:10.1016/0001-4575(93)90076-9]
16. Bertness J. Rain-related impacts on selected transportation activities and utility services in the chicago area. J Appl Meteorol Climatol. 1980 May;19(5):545-56.
https://doi.org/10.1175/1520-0450(1980)019<0545:RRIOST>2.0.CO;2 [DOI:10.1175/1520-0450(1980)0192.0.CO;2]
17. Irfan A, Rasyid RA, Handayani S. Data mining applied for accident prediction model in Indonesia toll road. AIP Conference Proceedings 1977 [Internet]. 2018. AIP Publishing LLC; 2018 Jun 26. p. 060001-9. Available from: https://aip.scitation.org/doi/10.1063/1.5043013 [DOI:10.1063/1.5043013]
18. Brodsky H, Hakkert AS. Risk of a road accident in rainy weather. Accid Anal Prev. 1988 Jun;20(3):161-76. [DOI:10.1016/0001-4575(88)90001-2]
19. Brijs T, Karlis D, Wets G. Studying the effect of weather conditions on daily crash counts using a discrete time-series model. Accid Anal Prev. 2008;40(3):1180-90. [DOI:10.1016/j.aap.2008.01.001]
20. Caliendo C, Guida M, Parisi A. A crash-prediction model for multilane roads. Accid Anal Prev. 2007 Jul;39(4):657-70. [DOI:10.1016/j.aap.2006.10.012]
21. Eisenberg D. The mixed effects of precipitation on traffic crashes. Accid Anal Prev. 2004 Jul;36(4):637-47. [DOI:10.1016/S0001-4575(03)00085-X]
22. Jung S, Qin X, Noyce DA. Rainfall effect on single-vehicle crash severities using polychotomous response models. Accid Anal Prev. 2010 Jan;42:213-24. [DOI:10.1016/j.aap.2009.07.020]
23. Yasaswini LNS, Mahesh G, Shankar RS, Srinivas LV. Identifying road accidents severity using convolutional neural networks. J Comput Sci Eng. 2018 Jul;6(7):354-60. [DOI:10.26438/ijcse/v6i7.354360]
24. Alkheder S, Taamneh M, Taamneh S. Severity prediction of traffic accident using an artificial neural network. J Forecast. 2016 Apr 27;36:100-8. [DOI:10.1002/for.2425]
25. Ertugrul S, Hizal NA. Neuro-fuzzy controller design via modeling human operator actions. J Intell Fuzzy Syst. 2005;16(2):133-40.
26. Adeli H, Park HS. Optimization of space structures by neural dynamics. Neural Network. 1995;8(5):769-81. [DOI:10.1016/0893-6080(95)00026-V]
27. Dougherty MS. Guest editorial applications of neural networks in transportation. Transport Res C Emerg Tech. 1997;5(5):255-7. [DOI:10.1016/S0968-090X(97)00013-2]
28. Chang LY. Analysis of freeway accident frequencies: Negative binomial regression versus artificial neural network. Saf Sci. 2005;43(8):541-57. [DOI:10.1016/j.ssci.2005.04.004]
29. Zeng Q, Huang H, Pei X, Wong SC. Modeling nonlinear relationship between crash frequency by severity and contributing factors by neural networks. Anal Methods Accid Res. 2016 Jun;10:12-25. [DOI:10.1016/j.amar.2016.03.002]
30. Behbahani H, Amiri AM, Imaninasab R, Alizamir M. Forecasting accident frequency of an urban road network: A comparison of four artificial neural network techniques. J Forecast. 2018 Aug;37(7):1-14. [DOI:10.1002/for.2542]
31. Deka L, Quddus M. Network-level accident-mapping: Distance based pattern matching using artificial neural network. Accid Anal Prev. 2014;65:105-13. [DOI:10.1016/j.aap.2013.12.001]
32. Xie Z, Klionsky DJ. Autophagosome formation: Core machinery and adaptations. Nat Cell Biol. 2007 Oct;9(10):1102-9. [DOI:10.1038/ncb1007-1102]
33. Jadaan KS, Al-Fayyad M, Gammoh HF. Prediction of road traffic accidents in jordan using artificial neural network (ANN). Journal of Traffic and Logistics Engineering. 2014 Jun;2(2):92-4. [DOI:10.12720/jtle.2.2.92-94]
34. Mussone L, Ferrari A, Oneta M. An analysis of urban collisions using an artificial intelligence model. Accid Anal Prev. 1999 Nov;31(6):705-18. [DOI:10.1016/S0001-4575(99)00031-7]
35. Mussone L, Rinelli S, Reitani G. Estimating the accident probability of a vehicular flow by means of an artificial neural network. Environ Plann B Plann Des. 1996;23(6):667-75. [DOI:10.1068/b230667]
36. Li L, Shrestha S, Hu G. Analysis of road traffic fatal accidents using data mining techniques. Proceedings of the 15th International Conference on Software Engineering Research, Management and Applications (SERA); 2017 Jun 7-9; London, UK. IEEE; 2017 Jul 3. p. 363-70. [DOI:10.1109/SERA.2017.7965753]
37. Sameen MI, Pradhan B. Severity prediction of traffic accidents with recurrent neural networks. Appl Sci. 2017 Jun 8;7(6):1-17. [DOI:10.3390/app7060476]
38. Chiou YC. An artificial neural network-based expert system for the appraisal of two-car crash accidents. Accid Anal Prev. 2006 Jul;38(4):777-85. [DOI:10.1016/j.aap.2006.02.006]
39. El Tayeb AA, Pareek V, Araar A. Applying association rules mining algorithms for traffic accidents in dubai. International Journal of Soft Computing and Engineering. 2015 Sep;5(4):1-12.
40. Najjarzadeh M, Korkeh Abadi Z, Kamyabi S. Modeling physical effective factors in promoting good governance through neural network (MLP)-case study of pedestrian roads in district 12 of Tehran. J Geogr Sci. 2020;16(32):93-106. [In Persian]
41. Keymanesh M, Baradaran Rahmanian N. Prediction of the road accidents severity using artificial neural network and comparing with multivariate analysis method. Road. 2021;106:1-9. [In Persian]
42. Asadi Farsani P, Fallah Tafti M. Artificial neural network model for short-term prediction of traffic flow and its application in Tehran-Karaj freeway. Proceedings of the International Conference on Civil Engineering, Architecture and Urban Infrastructure; 2015 Aug 28-29; Tabriz. Yazd University; 2015. [In Persian]
43. Machin M, Sanguesa JA, Garrido P, Martinez FJ. On the use of artificial intelligence techniques in intelligent transportation systems. Proceedings of the Wireless Communications and Networking Conference Workshops (WCNCW); 2018 April 15-18; Barcelona, Spain. IEEE; 2018 May 31. p. 332-7. [DOI:10.1109/WCNCW.2018.8369029]
44. Kunt MM, Aghayan I, Noii N. Prediction for traffic accident severity: Comparing the artificial neural network, genetic algorithm, combined genetic algorithm and pattern search methods. Transport. 2011;26(4):353-66. [DOI:10.3846/16484142.2011.635465]
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