Evaluation Of The Effectiveness Of Neural Network Models In The Modeling Of Intra-City Highway Accidents

Document Type : Original Article

Author
Ebrahim moorvari
Transportation engineering expert of Garme Municipality;Garme ; Iran
10.22034/el.2022.362191.1002
Abstract
This paper discusses research evaluating the efficiency of neural network models in modeling inner-city highway accidents. As a case study, accident data from urban highways in Mashhad and variables related to traffic flow and road geometry are used as input variables for neural network modeling. Neural network modeling involves three steps: determining the neural network architecture, determining the transfer functions, training and errors, and creating neural network models.
In this research, two neural network models were presented to estimate the number of financial and fatal accidents on inner-city highways. To evaluate the efficiency and accuracy of the models, the number of accidents estimated by the models was compared with the observed number and the value of R was used. On this basis, the presented models are suitable for estimating the number of financial and fatal accidents.

Keywords

Subjects

1] Caliendo, C., Guida, M. and Parisi, A. (2007) "A crash-prediction model for multilane roads", Accident Analysis and prevention 39, 657-6. 
[2] Ayati, E., Abbasi, E. (2011) "Investigation on the role of traffic volume in accidents on urban highways", Safety research 42, 209-214.
[3] Lord, D., Guikema, S.D., Geedipally, S.R. (2008) "Application of the Conway-Maxwell-Poisson generalized linear model for analyzing motor vehicle crashes", Accident Analysis and Prevention 40(3), 1123-1134.
[4] Lord, D., Park, Y.-J. (2008) "Investigating the effects of the fixed and varying dispersion parameters of Poisson-gamma models on empirical Bayes estimates", Accident Analysis and Prevention 40(4), 1441-1457.
[5] Cafiso, S., Graziano, A.D., Silvestro, G.D., Cava, G.L., and Persaud, B. (2010) "Development of comprehensive accident models for two-lane rural highways using exposure, geometry, consistency and context variables", Accident Analysis and Prevention 42, 1072-1079.
[6] EI-Basyouny, K., and Sayed, T. (2009) "Accident prediction models with random corridor Parameters", Accident Analysis and Prevention 41, 1118-1123.
[7] Ozbay, K., and Noyan, N. (2006) "Estimation of incident clearance times using Bayes Network Approach", Accident Analysis and Prevention 38, 542-555.
[8] Chang, L.-Y. (2005) "Analysis of freeway accident frequencies: Negative binomial regression versus artificial neural network", Safety Science 43, 541–557.
[9] Delen, D., Sharada, R., and Bessonov, M. (2006) "Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks", Accident Analysis and Prevention 38, 434-444.
[10] Meng, X.H., Zheng, L., and Qing, G.M. (2009) "Traffic accidents prediction and prominent influencing factors analysis based on fuzzy logic", Journal of Transportation Systems and Information Technology 9(2), 87-92.
[11] De Brabander, B., E. Nuyts and L. Vereeck. 2005. Road safety effects of roundabouts in Flanders. Journal of Safety Research 36(3): 289-296.
[12] Quek, C., M.Pasquier, and B.Lim. 2009. A novel self-organizing fuzzy rule-based system for modelling traffic flow behaviour. Expert Systems with Applications.
[13] Zheng, L., and Meng, X. (2011) "An approach to predict road accident frequencies: Application of Fuzzy Neural Network", 3rd International Conference on Road Safety and Simulation, September 14-16, 2011, Indianapolis, USA.
[14] Liao, Z., Wang, B., Xia, X., and Hannam, P.M. (2012) "Environmental emergency decision support system based on Artifial Neural Network", Safety Research 50, 150–163.
[15] کاوه، علی؛ ثروتی، همایون (1380). "شبکه های عصبی مصنوعی در تحلیل و طراحی سازه ها"؛ انتشارات مرکز تحقیقات ساختمان و مسکن.
[16]  افندی زاده، شهریار ؛ احمدی نژاد، محمود و عبدالمنافی، سید ابراهیم (1386) "مقایسه نتایج مدل های آماری و شبکه عصبی در پیش بینی شمار تصادفات در تقاطعات"، پژوهشنامه حمل و نقل، سال چهارم، شماره 4، ص. 355-339.
[17] Mazloumi, E., Rose, G., Currie, G., and Moridpour, S. (2011) "Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction", Engineering Applications of Artificial Intelligence 24, 534–542.
[18] Ung, S. T., Williams, V., Wang, B. J. (2006) "Test case based risk predictions using artificial neural ", Journal of Safety Research 37, 245–260.
[19] دفتر مطالعات جامع حمل و نقل؛ هفتمین آمارنامه حمل و نقل شهر مشهد؛ سازمان حمل و نقل و ترافیک مشهد، 1390.
Human Ecology
Volume 1, Issue 1
Autumn 2022
Pages 42-53