Abstract
Millions of people are dying, and billions of properties are damaged by road traffic accidents each year worldwide. In the case of our country Ethiopia, the effect of traffic accidents is even more by causing injuries, death, and property damage. Forecasting road traffic accident and predicting the severity of road traffic accident contributes a role indirectly in reducing road traffic accidents. This study deals with forecasting the number of accident and prediction of the severity of an accident in the Oromia Special Zone using deep artificial neural network models. Around 6170 road traffic accidents data are collected from Oromia Police Commission Excel data and Oromia Special Zone Traffic Police Department; the dataset consists of accidents in the Special Zone of Oromia Districts (Woredas) from 2005 to 2012 with 15 accidents attributes. 5928 or (80%) of the dataset was used for the training model, and 1482 or (20%) of the dataset was used for the testing model. This study proposed six different neural network architectures such as backpropagation neural network (BPNN), feedforward neural network (FFNN), multilayer perceptron neural network (MLPNN), recurrent neural networks (RNN), radial basis function neural network (RBFNN) and long short-term memory (LSTM) models for accident severity prediction and the LSTM model for a time serious forecasting of number accidents within specified years. The models will take input data, classify accidents, and predict the severity of an accident. Accident predictor GUI has been created using Python Tkinter library for easy accident severity prediction. According to the model performance results, RNN model showed the best prediction accuracy of 97.18%, whereas MLP, LTSM, RBFNN, FFNN, and BPNN models showed the accuracy of 97.13%, 91.00%, 87.00%, 80.56%, and 77.26%, respectively. LTSM model forecasted accident for three years which is 3555 where the actual accident number is 3561. The prediction and forecast result obtained from the model will be helpful in planning and management of road traffic accidents.
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References
Abegaz T et al (2014) Road traffic deaths and injuries are under-reported in ethiopia: a capture-recapture method. PLoS ONE 9:7
Abu Arqub O, Singh J, Alhodaly M (2021) Adaptation of kernel functions-based approach with Atangana–Baleanu–Caputo distributed order derivative for solutions of fuzzy fractional Volterra and Fredholm integrodifferential equations. Math Methods Appl Sci. https://doi.org/10.1002/mma.7228
Abu Arqub O, Singh J, Maayah B, Alhodaly M (2021) Reproducing kernel approach for numerical solutions of fuzzy fractional initial value problems under the Mittag-Leffler kernel differential operator. Math Methods Appl Sci. https://doi.org/10.1002/mma.7305
AFRO factsheet (2013)
Al-Khalifa K, Hamouda AMS (2012) Prediction of road accidents in Qatar 2022. Qatar foundation annual research forum proceedings. AHP31. https://doi.org/10.5339/qfarf.2012.AHP31
Alkheder S, Taamneh M, Taamneh S (2017) Severity prediction of traffic accidents using an artificial neural network. J Forecast 36(1):100–108
Almamlook R, Kwayu K, Frefer A (2019) Comparison of machine learning algorithms for predicting traffic accident severity. In: 2019 IEEE Jordan international joint conference on electrical engineering and information technology (JEEIT), pp 272–276. IEEE. https://doi.org/10.1109/JEEIT.2019.8717393
Al-Moqri T, Haijun X, Namahoro JP, Alfalahi EN, Alwesabi I (2020) Exploiting machine learning algorithms for predicting crash injury severity in Yemen: hospital case study. Appl Comput Math 9(5):155–164. https://doi.org/10.11648/j.acm.20200905.12
Al-Zuhairi M, Pradhan B, Shafri H, Hamid H (2019) Applications of deep learning in severity prediction of traffic accidents. In: GCEC 2017: Proceedings of the 1st global civil engineering conference 1, pp 793–808, Springer https://doi.org/10.1007/978-981-10-8016-6_58
Al-Zuhairi M, Pradhan B (2017) Severity prediction of traffic accidents with recurrent neural networks. Appl Sci. https://doi.org/10.3390/app7060476
Amdeslasie F, Kidanu M, Lerebo W, Ali D (2016) Patterns of trauma in patient seen at the emergency clinics of public hospitals in Mekelle, northern Ethiopia. Ethiop Med J 54(2):63–68
Arhin S, Gatiba A (2019) Predicting injury severity of angle crashes involving two vehicles at unsignalized intersections using artificial neural networks. Eng Technol Appl Sci Res 9:3871–3880. https://doi.org/10.48084/etasr.2551
Assi K (2020) Traffic crash severity prediction-a synergy by hybrid principal component analysis and machine learning models. Int J Environ Res Public Health 17(20):7598. https://doi.org/10.3390/ijerph17207598
Chen S, Kuhn M, Prettner K, Bloom DE (2019) The global macroeconomic burden of road injuries: estimates and projections for 166 countries. Lancet Planet Health 3(9):e390-398
Chong M, Abraham A, Paprzycki M (2005) Traffic accident analysis using machine learning paradigms. Inform (slovenia) 29:89–98
Chuerubim ML, Valejo A, Bezerra BS, da Silva I (2019) Artificial neural networks restriction for road accidents severity classification in unbalanced database. Sigma J Eng Nat Sci 37(3):927–940
Çodur M, Tortum A (2015) An artificial neural network model for highway accident prediction: a case study of Erzurum, Turkey. PROMET Traffic Transp. https://doi.org/10.7307/ptt.v27i3.1551
Doğan E, Akgüngör A (2008) Estimating road accidents of turkey based on regression analysis and ANN approach. Adv Transp Stud Int J 16:11–22
Economic Commission for Africa (ECA) and the Secretariat of the UN Secretary-General’s Special Envoy for Road Safety, Mr. Jean Todt. URL: https://www.fanabc.com/english/about-13-people-die-in-road-traffic-accident-in-ethiopia-each-day/
Abagaz T, Gebremedhin S (2018) Magnitude of road traffic accident-related injuries and fatalities in Ethiopia. PLoS ONE 14(1):e0202240. https://doi.org/10.1101/382333
Farhat Z, Karouni A, Chauvet P, Daya B, Hamadeh N (2020) Traffic accidents severity prediction using SVM models. J Eng Des Technol 9:1345–1350. https://doi.org/10.35940/ijitee.F4393.059720
Farhat Z, Karouni A, Daya B, Chauvet P (2019) Comparative study between decision trees and neural networks to predict fatal road accidents in Lebanon. In: 5th International conference on computer science, information technology, Aircc Publishing Corporation, pp 01–14 https://doi.org/10.5121/csit.2019.91101
García de Soto B, Bumbacher A, Deublein M, Adey BT (2018) Predicting road traffic accidents using artificial neural network models. Infrastruct Asset Manage 5(4):132–144. https://doi.org/10.1680/jinam.17.00028
Global health estimates. Geneva: World Health Organization (2014) (http://www.who.int/healthinfo/global_burden_disease/estimates/en/index1.html, http://www.who.int/healthinfo/global_burden_disease/projections/en, Accessed 15 September 2015).
Hartika HA, Ramli MZ, Abidin MZZ, Zawawi MH (2017) Study of road accident prediction model at accident blackspot area at Selangor. Int J Sci Res Sci Eng Technol (IJSRSET) 3(5):466–470
Hochreiter S, Schmidhuber J (1997a) Long short-term memory. Neural Comput 9:1735–1780
Hochreiter S, Schmidhuber J (1997b) Long short-term memory. Neural Comput 9(8):1735–1780
Huang H, Zeng Q, Pei X, Wong SC, Xu P (2016a) Predicting crash frequency using an optimized radial basis function neural network model. Transp A Transp Sci 12:1–24. https://doi.org/10.1080/23249935.2015.1136008
Huang H, Zeng Q, Pei X, Wong SC, Xu P (2016b) Predicting crash frequency using an optimised radial basis function neural network model. Transp Metrica Transp Sci 12(4):330–345
Jadaan K, Alkhaledi Q, Najjar A. Development of An Accident Prediction Model Using Artificial Neural Network. Available at: https://www.psd.gov.jo/images/jti/docs/
Kotsiantis S, Kanellopoulos D, Pintelas P (2006) Data preprocessing for supervised learning. Int J Comput Sci 1:111–117
Kunt M, Aghayan I, Noii N (2011) Prediction of traffic accident severity: Comparing the ANN, genetic algorithm, combined genetic algorithm, and pattern search methods. Transport 26:353–366. https://doi.org/10.3846/16484142.2011.635465
Ławrynowicz A, Tresp V (2014) Introducing machine learning. Perspect Ontol Learn 12:19
Lee J, Yoon T, Kwon S, Lee J (2019) Model evaluation for forecasting traffic accident severity in rainy seasons using machine learning algorithms in Seoul City. Appl Sci 10:129. https://doi.org/10.3390/app10010129
Loewe M, Rippin N (2015) The Sustainable Development Goals of the Post-2015 Agenda: Comments on the OWG and SDSN Proposals, 2015
Mackay GM, Wodzin E. Global priorities for vehicle safety. International conference on vehicle safety 2002: IMechE conference transactions. London, Institution of Mechanical Engineers, 2002:3−9 Persson (2008), Road traffic accidents in Ethiopia: Magnitude, causes and possible interventions
Marius P, Balas V, Perescu-Popescu L, Mastorakis N (2009) Multilayer perceptron and neural networks. WSEAS Trans Circ Syst 8(7):579–588
Mashhadi R, Mahdi M, Nazneen S, Ksaibati K (2020) Application of deep learning techniques in predicting motorcycle crash severity. Eng Rep. https://doi.org/10.1002/eng2.12175
McCulloch WS, Pitts W (1943) Logical Calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133
Minister of Transport NR (2011) National Road Safety strategic plan of Ethiopia 2011 to 2020. AA
Moghaddam FR, Afandizadeh S, Ziyadi M (2011) Prediction of accident severity using ANN. IJCE. 9(1):41–48
FDRE MoH (2015) HSTP: Health Sector Transformation Plan, 2015/16–2019/20, 2015, The Federal Democratic Republic of Ethiopia Ministry of Health (FDRE MoH): Addis Ababa, Ethiopia
Momani S, Abo-Hammour ZS, Alsmadi OM (2016) Solution of inverse kinematics problem using genetic algorithms. Appl Math Inform Sci 10(1):225. https://doi.org/10.1016/j.ins.2014.03.128
Moor J (2006) The dartmouth college artificial intelligence conference: the next fifty years. Artif Intell Mag 27(4):87–91
Murray CJL, Lopez AD (eds) (2020) The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020. Boston
Odhiambo JN, Wanjoya AK, Waititu AG (2015) Modeling road traffic accident injuries in nairobi county: model comparison approach. Am J Theor Appl Stat 4(3):178–184. https://doi.org/10.11648/j.ajtas.20150403.24
Olutayo VA, Eludire AA (2014) Traffic accident analysis using decision trees and neural networks. Int J Inf Technol Comput Sci (IJITCS) 6(2):22–28. https://doi.org/10.5815/ijitcs.2014.02.03
Road traffic accidents in Ethiopia (2008) Magnitude, causes and possible interventions. Adv Transp Stud 15:5–16
Sazli M (2006) A brief review of feed-forward neural networks. Communications, Faculty of Science, University of Ankara. 50: 11–17. https://doi.org/10.1501/0003168
Schwenker F, Kestler HA, Palm G (2001) Three learning phases for radial-basis-function networks. Neural Netw 14(4–5):439–458
Shaik M, Hossain, Q (2018) An artificial neural network model for road accident prediction: a case study of Khulna Metropolitan City
Siamidoudaran M, İşçioğlu E (2019) Injury Severity prediction of traffic collision by applying a series of neural networks: The City of London case study. PROMET 31(6):643–654
Song C, Li Q (2011) The prediction model of macro-road traffic accident basing on radial basis function. Appl Mech Mater 97–98:981–984
Urbanization in Small Cities and Their Significant Implications on Landscape Structures: The Case in Ethiopia, retrieved February 8, 2020
Walczak B, Massart DL (2000) Local modelling with radial basis function networks. Chemom Intell Lab Syst 50(2):179–198
Werbos PJ (1988) Generalization of backpropagation with application to a recurrent gas market model. NeuralNetwork 1:339–356
Werbos PJ (1990) Backpropagation Through time: what it does and how to do it. Proc IEEE 78(10):1550–1560
WHO Global Status Report on Road Safety (2015) 20 avenue Appia, 1211 Geneva 27: World Health Organization; 2015
World Health Organization (WHO). Global Status Report on Road Safety (2018) https://www.who.int/violence_injury_prevention/road_safety_status/2018/en/
World Health Organization (2019) Report on causes of death. Available from URL: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
World Report on Road Traffic Injury Prevention (2015)
World’s first road death. London, Road Peace (2003) (http://www.roadpeace.org/articles/WorldFirstDeath.html, Accessed on 17 November 2003)
Yohannis D (2019) The Impact of Road Traffic Accident on Economic Growth in Ethiopia, Munich, GRIN Verlag, https://www.grin.com/document/507875
Yousif J, AlRababaa S (2013) Neural technique for predicting traffic accidents in Jordan. J Am Sci 9:347–358
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Raja, K., Kaliyaperumal, K., Velmurugan, L. et al. Forecasting road traffic accident using deep artificial neural network approach in case of Oromia Special Zone. Soft Comput 27, 16179–16199 (2023). https://doi.org/10.1007/s00500-023-08001-6
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DOI: https://doi.org/10.1007/s00500-023-08001-6