Presentation of machine learning methods to determine the most important factors affecting road traffic accidents on rural roads

doi:10.1007/s11709-022-0827-z

Front. Struct. Civ. Eng.

2022, Vol. 16

Issue (5) : 657-666 https://doi.org/10.1007/s11709-022-0827-z

RESEARCH ARTICLE

Presentation of machine learning methods to determine the most important factors affecting road traffic accidents on rural roads

Hamid MIRZAHOSSEIN¹(

), Milad SASHURPOUR², Seyed Mohsen HOSSEINIAN¹, Vahid Najafi Moghaddam GILANI²

¹. Civil–Transportation Planning Department, Faculty of Technical and Engineering, Imam Khomeini International University (IKIU), Qazvin 34148-96818, Iran
². School of Civil Engineering, Iran University of Science and Technology (IUST), Tehran 13114-16846, Iran

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Abstract

The purpose of this research was to develop statistical and intelligent models for predicting the severity of road traffic accidents (RTAs) on rural roads. Multiple Logistic Regression (MLR) was used to predict the likelihood of RTAs. For more accurate prediction, Multi-Layer Perceptron (MLP) and Radius Basis Function (RBF) neural networks were applied. Results indicated that in MLR, the model obtained from the backward method with the correct percent of 84.7% and R² value of 0.893 was the best method for predicting the likelihood of RTAs. Also, MLR showed that the variables of not paying attention to the front not paying attention to the frontroad ahead, followed byand then vehicle-motorcycle/bike accidents were the greatest problems. Among the models, MLP had a better performance, so that the prediction accuracy of MLR, MLP, and RBF were 84.7%, 96.7%, and 92.1%, respectively. MLP model, due to higher accuracy, showed that the variable of reason of accident had the highest effect on the prediction of accidents, and considering MLR results, the variables of not paying attention to the front and then vehicle-motorcycle/bike accidents had the most influence on the occurrence of accidents. Therefore, motorcyclists and cyclists are more prone to accidents, and appropriate solutions should be adopted to enhance their safety.

Keywords safety rural accidents multiple logistic regression artificial neural networks

Corresponding Author(s): Hamid MIRZAHOSSEIN

About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.

Just Accepted Date: 23 June 2022 Online First Date: 01 August 2022 Issue Date: 30 August 2022

Cite this article:

Hamid MIRZAHOSSEIN,Milad SASHURPOUR,Seyed Mohsen HOSSEINIAN, et al. Presentation of machine learning methods to determine the most important factors affecting road traffic accidents on rural roads[J]. Front. Struct. Civ. Eng., 2022, 16(5): 657-666.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-022-0827-z
https://academic.hep.com.cn/fsce/EN/Y2022/V16/I5/657

Tab.1 Specifications of variables in this research

Tab.2 Summary of the Multiple Logistic Regression methods

Tab.3 Backward stepwise model result

Tab.4 Classification in MLR model

Tab.5 Multiple logistic analysis result in the fifteenth step

Tab.6 Classifications in MLP model

Fig.1 ROC curve of MLP model.

Fig.2 Independent variable importance chart in MLP method.

Tab.7 Independent variable importance for MLP model

Tab.8 Classifications in RBF model

Fig.3 ROC curve of RBF model.

Fig.4 Independent variable importance chart in RBF method.

Tab.9 Independent variable importance for RBF model

Tab.10 The comparison of the models used in this research

1	A Banstola, J Kigozi, P Barton, J Mytton. Economic burden of road traffic injuries in Nepal. International Journal of Environmental Research and Public Health, 2020, 17( 12): 4571 https://doi.org/10.3390/ijerph17124571
2	Y Zou, Y Zhang, K Cheng. Exploring the impact of climate and extreme weather on fatal traffic accidents. Sustainability (Basel), 2021, 13( 1): 390 https://doi.org/10.3390/su13010390
3	Health Organization World. Global Status Report on Road Safety 2018. 2018
4	S Heydari, A Hoseinzadeh, F Ghaffarpasand, A Hedjazi, M Zarenezhad, G Moafian, M R Aghabeigi, A Foroutan, Y Sarikhani, P Peymani, S M Ahmadi, H Joulaei, M Dehghankhalili, K B Lankarani. Epidemiological characteristics of fatal traffic accidents in Fars province, Iran: A community-based survey. Public Health, 2013, 127( 8): 704– 709 https://doi.org/10.1016/j.puhe.2013.05.003
5	J K Abledu, G Abledu. Multiple logistic regression analysis of predictors of musculoskeletal disorders and disability among bank workers in Kumasi. Journal of Ergonomics, 2012, 2( 4): 2 https://doi.org/10.4172/2165-7556.1000111
6	H Y Wang, J Peng, B K Wang, X Lu, J Z Zheng, K J Wang, X M Tu, C Y Feng. Inconsistency between univariate and multiple logistic regressions. Shanghai Archives of Psychiatry, 2017, 29( 2): 124
7	H Zhang, H Xing, D Yao, L Liu, D Xue, F Guo. The multiple logistic regression recognition model for mine water inrush source based on cluster analysis. Environmental Earth Sciences, 2019, 78( 20): 612 https://doi.org/10.1007/s12665-019-8624-2
8	V Kwigizile, T Sando, D Chimba. Inconsistencies of ordered and unordered probability models for pedestrian injury severity. Transportation Research Record: Journal of the Transportation Research Board, 2011, 2264( 1): 110– 118 https://doi.org/10.3141/2264-13
9	R Garrido, A Bastos, A de Almeida, J P Elvas. Prediction of road accident severity using the ordered probit model. Transportation Research Procedia, 2014, 3 : 214– 223 https://doi.org/10.1016/j.trpro.2014.10.107
10	G Fountas, P C Anastasopoulos. Analysis of accident injury-severity outcomes: The zero-inflated hierarchical ordered probit model with correlated disturbances. Analytic Methods in Accident Research, 2018, 20 : 30– 45 https://doi.org/10.1016/j.amar.2018.09.002
11	E B Piedra-Bonilla, D A da Cunha, M J Braga. Climate variability and crop diversification in Brazil: An ordered probit analysis. Journal of Cleaner Production, 2020, 256 : 120252 https://doi.org/10.1016/j.jclepro.2020.120252
12	U Barua, R Tay. Severity of urban transit bus crashes in Bangladesh. Journal of Advanced Transportation, 2010, 44( 1): 34– 41 https://doi.org/10.1002/atr.104
13	S Yasmin, N Eluru, S V Ukkusuri. Alternative ordered response frameworks for examining pedestrian injury severity in New York City. Journal of Transportation Safety & Security, 2014, 6( 4): 275– 300 https://doi.org/10.1080/19439962.2013.839590
14	Q Zeng, W Gu, X Zhang, H Wen, J Lee, W Hao. Analyzing freeway crash severity using a Bayesian spatial generalized ordered logit model with conditional autoregressive priors. Accident Analysis and Prevention, 2019, 127 : 87– 95 https://doi.org/10.1016/j.aap.2019.02.029
15	G Azimi, A Rahimi, H Asgari, X Jin. Severity analysis for large truck rollover crashes using a random parameter ordered logit model. Accident Analysis and Prevention, 2020, 135 : 105355 https://doi.org/10.1016/j.aap.2019.105355
16	F Ye, D Lord. Comparing three commonly used crash severity models on sample size requirements: Multinomial logit, ordered probit and mixed logit models. Analytic Methods in Accident Research, 2014, 1 : 72– 85 https://doi.org/10.1016/j.amar.2013.03.001
17	K Haleem, P Alluri, A Gan. Analyzing pedestrian crash injury severity at signalized and non-signalized locations. Accident Analysis and Prevention, 2015, 81 : 14– 23 https://doi.org/10.1016/j.aap.2015.04.025
18	F Chen, S Chen, X Ma. Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data. Journal of Safety Research, 2018, 65 : 153– 159 https://doi.org/10.1016/j.jsr.2018.02.010
19	P Liu, W Fan. Exploring injury severity in head-on crashes using latent class clustering analysis and mixed logit model: A case study of North Carolina. Accident Analysis and Prevention, 2020, 135 : 105388 https://doi.org/10.1016/j.aap.2019.105388
20	L Y Chang. Analysis of freeway accident frequencies: Negative binomial regression versus artificial neural network. Safety Science, 2005, 43( 8): 541– 557 https://doi.org/10.1016/j.ssci.2005.04.004
21	A Abdulhafedh. Crash frequency analysis. Journal of Transportation Technologies, 2016, 6( 4): 169– 180 https://doi.org/10.4236/jtts.2016.64017
22	S Mokhtarimousavi, J C Anderson, A Azizinamini, M Hadi. Factors affecting injury severity in vehicle-pedestrian crashes: A day-of-week analysis using random parameter ordered response models and artificial neural networks. International Journal of Transportation Science and Technology, 2020, 9( 2): 100– 115
23	C Riviere, P Lauret, J F M Ramsamy, Y Page. A Bayesian neural network approach to estimating the energy equivalent speed. Accident Analysis and Prevention, 2006, 38( 2): 248– 259 https://doi.org/10.1016/j.aap.2005.08.008
24	Y Xie, D Lord, Y Zhang. Predicting motor vehicle collisions using Bayesian neural network models: An empirical analysis. Accident Analysis and Prevention, 2007, 39( 5): 922– 933 https://doi.org/10.1016/j.aap.2006.12.014
25	Oña J de, R O Mujalli, F J Calvo. Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks. Accident Analysis and Prevention, 2011, 43( 1): 402– 411 https://doi.org/10.1016/j.aap.2010.09.010
26	H Behbahani, A M Amiri, R Imaninasab, M Alizamir. Forecasting accident frequency of an urban road network: A comparison of four artificial neural network techniques. Journal of Forecasting, 2018, 37( 7): 767– 780 https://doi.org/10.1002/for.2542
27	W Zhu, J Miao, J Hu, L Qing. Vehicle detection in driving simulation using extreme learning machine. Neurocomputing, 2014, 128 : 160– 165 https://doi.org/10.1016/j.neucom.2013.05.052
28	A B Parsa, H Taghipour, S Derrible, A K Mohammadian. Real-time accident detection: Coping with imbalanced data. Accident Analysis and Prevention, 2019, 129 : 202– 210 https://doi.org/10.1016/j.aap.2019.05.014
29	B Yu, Y Chen, S Bao, D Xu. Quantifying drivers’ visual perception to analyze accident-prone locations on two-lane mountain highways. Accident Analysis and Prevention, 2018, 119 : 122– 130 https://doi.org/10.1016/j.aap.2018.07.014
30	H Omrani. Predicting travel mode of individuals by machine learning. Transportation Research Procedia, 2015, 10 : 840– 849 https://doi.org/10.1016/j.trpro.2015.09.037
31	N Slimani, I Slimani, N Sbiti, M Amghar. Traffic forecasting in Morocco using artificial neural networks. Procedia Computer Science, 2019, 151 : 471– 476 https://doi.org/10.1016/j.procs.2019.04.064
32	S Amin. Backpropagation––Artificial Neural Network (BP-ANN): Understanding gender characteristics of older driver accidents in West Midlands of United Kingdom. Safety Science, 2020, 122 : 104539 https://doi.org/10.1016/j.ssci.2019.104539
33	A M Amiri, A Sadri, N Nadimi, M Shams. A comparison between Artificial Neural Network and Hybrid Intelligent Genetic Algorithm in predicting the severity of fixed object crashes among elderly drivers. Accident Analysis and Prevention, 2020, 138 : 105468 https://doi.org/10.1016/j.aap.2020.105468
34	T Richter, S Ruhl, J Ortlepp, E Bakaba. Prevention of overtaking accidents on two-lane rural roads. Transportation Research Procedia, 2016, 14 : 4140– 4149 https://doi.org/10.1016/j.trpro.2016.05.385
35	L Lombardo, M Cama, C Conoscenti, M Märker, E Rotigliano. Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: Application to the 2009 storm event in Messina (Sicily, southern Italy). Natural Hazards, 2015, 79( 3): 1621– 1648 https://doi.org/10.1007/s11069-015-1915-3
36	S Saeidi, M Mohammadzadeh, A Salmanmahiny, S H Mirkarimi. Performance evaluation of multiple methods for landscape aesthetic suitability mapping: A comparative study between Multi-Criteria Evaluation, Logistic Regression and Multi-Layer Perceptron neural network. Land Use Policy, 2017, 67 : 1– 12 https://doi.org/10.1016/j.landusepol.2017.05.014
37	S H Wang, T M Zhan, Y Chen, Y Zhang, M Yang, H M Lu, H N Wang, B Liu, P Phillips. Multiple sclerosis detection based on biorthogonal wavelet transform, RBF kernel principal component analysis, and logistic regression. IEEE Access: Practical Innovations, Open Solutions, 2016, 4 : 7567– 7576 https://doi.org/10.1109/ACCESS.2016.2620996
38	H Yu, J Tao, C Qin, M Liu, D Xiao, H Sun, C Liu. A novel constrained dense convolutional autoencoder and DNN-based semi-supervised method for shield machine tunnel geological formation recognition. Mechanical Systems and Signal Processing, 2022, 165 : 108353 https://doi.org/10.1016/j.ymssp.2021.108353
39	Y Jin, C Qin, J Tao, C Liu. An accurate and adaptative cutterhead torque prediction method for shield tunneling machines via adaptative residual long-short term memory network. Mechanical Systems and Signal Processing, 2022, 165 : 108312 https://doi.org/10.1016/j.ymssp.2021.108312
40	W Zhou, J Liu, J Lei, L Yu, J N Hwang. GMNet: Graded-feature multilabel-learning network for RGB-thermal urban scene semantic segmentation. IEEE Transactions on Image Processing, 2021, 30 : 7790– 7802 https://doi.org/10.1109/TIP.2021.3109518
41	Y Li, P Che, C Liu, D Wu, Y Du. Cross-scene pavement distress detection by a novel transfer learning framework. Computer-Aided Civil and Infrastructure Engineering, 2021, 36( 11): 1398– 1415 https://doi.org/10.1111/mice.12674
42	Z Lv, Y Li, H Feng, H Lv. Deep Learning for security in digital twins of cooperative intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems, 2021, 1– 10 https://doi.org/10.1109/TITS.2021.3113779
43	B Alizadeh Kharazi, A H Behzadan. Flood depth mapping in street photos with image processing and deep neural networks. Computers, Environment and Urban Systems, 2021, 88 : 101628 https://doi.org/10.1016/j.compenvurbsys.2021.101628
44	B Alizadeh D Li Z Zhang A H Behzadan. Feasibility study of urban flood mapping using traffic signs for route optimization. 2021, arXiv:2109.11712
45	M S Yamany, T U Saeed, M Volovski, A Ahmed. Characterizing the performance of interstate flexible pavements using artificial neural networks and random parameters regression. Journal of Infrastructure Systems, 2020, 26( 2): 04020010 https://doi.org/10.1061/(ASCE)IS.1943-555X.0000542
46	T Fawcett. An introduction to ROC analysis. Pattern Recognition Letters, 2006, 27( 8): 861– 874 https://doi.org/10.1016/j.patrec.2005.10.010
47	H T Abdelwahab, M A Abdel-Aty. Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections. Transportation Research Record: Journal of the Transportation Research Board, 2001, 1746( 1): 6– 13 https://doi.org/10.3141/1746-02
48	D Chimba, T Sando. The prediction of highway traffic accident injury severity with neuromorphic techniques. Advances in Transportation Studies, 2009, 19 : 17– 26
49	H T Abdelwahab, M A Abdel-Aty. Artificial neural networks and logit models for traffic safety analysis of toll plazas. Transportation Research Record: Journal of the Transportation Research Board, 2002, 1784( 1): 115– 125 https://doi.org/10.3141/1784-15
50	D Delen, R Sharda, M Bessonov. Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accident Analysis and Prevention, 2006, 38( 3): 434– 444 https://doi.org/10.1016/j.aap.2005.06.024
51	V Siskind, D Steinhardt, M Sheehan, T O’Connor, H Hanks. Risk factors for fatal crashes in rural Australia. Accident Analysis and Prevention, 2011, 43( 3): 1082– 1088 https://doi.org/10.1016/j.aap.2010.12.016
52	X Yan, E Radwan, M Abdel-Aty. Characteristics of rear-end accidents at signalized intersections using multiple logistic regression model. Accident Analysis and Prevention, 2005, 37( 6): 983– 995 https://doi.org/10.1016/j.aap.2005.05.001
53	P P Shrestha K J Shrestha. Factors associated with crash severities in built-up areas along rural highways of Nevada: A case study of 11 towns. Journal of Traffic and Transportation Engineering (English Edition), 2017, 4(1): 96− 102
54	F Sherafati, E H Rad, A Afkar, R Gholampoor-Sigaroodi, S Sirusbakht. Risk factors of road traffic accidents associated mortality in northern iran: A single center experience utilizing oaxaca blinder decomposition. Bulletin of Emergency and Trauma, 2017, 5( 2): 116
55	N Casado-Sanz, B Guirao, D Gálvez-Pérez. Population ageing and rural road accidents: Analysis of accident severity in traffic crashes with older pedestrians on Spanish crosstown roads. Research in Transportation Business & Management, 2019, 30 : 100377 https://doi.org/10.1016/j.rtbm.2019.100377
56	P Intini, N Berloco, P Colonna, V Ranieri, E Ryeng. Exploring the relationships between drivers’ familiarity and two-lane rural road accidents: A multi-level study. Accident Analysis and Prevention, 2018, 111 : 280– 296 https://doi.org/10.1016/j.aap.2017.11.013
57	S Pourfalatoun, E E Miller. User perceptions of automated Truck-Mounted attenuators: Implications on work zone safety. Traffic Injury Prevention, 2021, 22( 5): 413 https://doi.org/10.1080/15389588.2021.1925116
58	E E Miller S Pourfalatoun. Evaluating the Human-Automated Maintenance Vehicle Interaction for Improved Safety and Facilitating Long-Term Trust. Denver: Applied Research and Innovation Branch, 2021
59	M Waseem, A Ahmed, T U Saeed. Factors affecting motorcyclists’ injury severities: An empirical assessment using random parameters logit model with heterogeneity in means and variances. Accident Analysis and Prevention, 2019, 123 : 12– 19 https://doi.org/10.1016/j.aap.2018.10.022
60	S Chen, T U Saeed, M Alinizzi, S Lavrenz, S Labi. Safety sensitivity to roadway characteristics: A comparison across highway classes. Accident Analysis and Prevention, 2019, 123 : 39– 50 https://doi.org/10.1016/j.aap.2018.10.020
61	N Ahmad A Ahmed B Wali T U Saeed. Exploring factors associated with crash severity on motorways in Pakistan. Proceedings of the Institution of Civil Engineers—Transport, 2020
62	S Chen, T U Saeed, S D Alqadhi, S Labi. Safety impacts of pavement surface roughness at two-lane and multi-lane highways: Accounting for heterogeneity and seemingly unrelated correlation across crash severities. Transportmetrica A: Transport Science, 2019, 15( 1): 18– 33 https://doi.org/10.1080/23249935.2017.1378281
63	S Chen, T U Saeed, S Labi. Impact of road-surface condition on rural highway safety: A multivariate random parameters negative binomial approach. Analytic Methods in Accident Research, 2017, 16 : 75– 89 https://doi.org/10.1016/j.amar.2017.09.001
64	T U Saeed, T Hall, H Baroud, M J Volovski. Analyzing road crash frequencies with uncorrelated and correlated random-parameters count models: An empirical assessment of multilane highways. Analytic Methods in Accident Research, 2019, 23 : 100101 https://doi.org/10.1016/j.amar.2019.100101
65	D Tong, Y Yuan, X Wang. The coupled relationships between land development and land ownership at China’s urban fringe: A structural equation modeling approach. Land Use Policy, 2021, 100 : 104925 https://doi.org/10.1016/j.landusepol.2020.104925
66	H Dong, B Zhao, Y Deng. Instability phenomenon associated with two typical high speed railway vehicles. International Journal of Non-linear Mechanics, 2018, 105 : 130– 145 https://doi.org/10.1016/j.ijnonlinmec.2018.06.006
67	Y Bie, J Ji, X Wang, X Qu. Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption. Computer-Aided Civil and Infrastructure Engineering, 2021, 36( 12): 1530– 1548 https://doi.org/10.1111/mice.12684

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