Artificial Neural Network and a Nonlinear Regression Model for Predicting Electrical Pole Crash
Abstract
This paper presents the investigation about a problem situation that Electric Distributor Companies are facing in Chile resulting from transit accidents. The number of vehicle crashes to power distribution poles and street lighting has grown. This situation causes discomfort to citizen and mainly to the neighbors due to power cuts and even on occasion , losses of human lives because of the accident that have occurred. Based on previous research, the accidents are not random nor chance dependent, but the majority of transit accident follow parameters or variables from the scenery where it occurs. In order to analyze the variables and the degree this variables affect the accidents, a model of Perceptron and Multipercetron Artificial Neural Networks and a Multiple Nonlinear Regression model are proposed. An empirical study was made; collecting data from a distributor company and from Chilean National Traffic Safety Commission, where the more frequent variables involved in accidents were determined to develop the mentioned models. These variables were investigated and also their influence on the occurrence of vehicle crashes to power distribution poles could be confirmed. With this data, the prediction of post crashes was developed, where through the application of the neural network and multiple nonlinear regression, revealed 95.7% of acceptable predictions. This study will bring benefits to power distribution companies considering a risk index in the streets, based on the number of crashes of poles per street; this will allow optimal decisions in future electrical distribution projects avoiding critical areas.References
[1] Ali, G.; Tayfour, A. (2012). Characteristics and Prediction of Traffic Accident Casualties In Sudan Using Statistical Modeling and Artificial Neural Networks, International Journal of Transportation Science and Technology, 1(4), 305 - 317, 2012.
https://doi.org/10.1260/2046-0430.1.4.305
[2] Amin, S. (2020). Backpropagation - Artificial Neural Network (BP-ANN): Understanding gender characteristics of older driver accidents in West Midlands of United Kingdom, Safety Science, 122, 104539, 2020.
https://doi.org/10.1016/j.ssci.2019.104539
[3] Basogain, X. (2008). Artificial Neural Networks and their applications. Higher School of Engineering of Bilbao, Department of systems and automatic engineering, EHU, 2008.
[4] Chilquinta Book of News the years 2016-2018, Valparaíso, 2018.
[5] Chis, V.; Barbulescu, C.; Kilyeni, S.; Dzitac, S. (2018). ANN based Short-Term Load Curve Forecasting, International Journal of Computers Communications & Control, 13(6), 938-955, 2018.
https://doi.org/10.15837/ijccc.2018.6.3385
[6] Ciupan, C.; Lungu, F.; Ciupan, C. (2014). ANN Method for Control of Robots to Avoid Obstacles, International Journal of Computers Communications & Control, 9(5), 539-554, 2014.
https://doi.org/10.15837/ijccc.2014.5.813
[7] Ciupan, E.; Lungu, F.; Ciupan, C. (2015). ANN Training Method with a Small Number of Examples Used for Robots Control, International Journal of Computers Communications & Control, 10(5), 643-653, 2015.
https://doi.org/10.15837/ijccc.2015.5.2027
[8] Deka, L.; Quddus, M. (2014). Network-level accident-mapping: Distance based pattern matching using artificial neural network, Accident Analysis and Prevention, 65, 105-113, 2014.
https://doi.org/10.1016/j.aap.2013.12.001
[9] Gagne, A.(2008). Evaluation of Utility Pole Placement and the Impact on Crash Rates, Proyecto de Tesis, Facultad de Ciencias, Worcester Polytechnic Institute, 2008.
[10] Good, M.C.; Fox, J.C.; Joubertm, P.N. (1987). An in-depth study of accidents involving collisions with utility pole, Accident Analysis & Prevention, 19(5), 397-413, 1987.
https://doi.org/10.1016/0001-4575(87)90024-8
[11] Moldovan, L.; Grif, H.-S.; Gligor, A. (2016). ANN Based Inverse Dynamic Model of the 6-PGK Parallel Robot Manipulator, International Journal of Computers Communications & Control, 11(1), 90-104, 2016.
https://doi.org/10.15837/ijccc.2016.1.1962
[12] Montt, C.; Rodriguez, N.; Valencia, A. (2015). Traffic accident prediction in the metropolitan region of Chile based on neural networks, Proceedings of the XVII Chilean Congress of Transportation Enginee, Concepción, Chile, 2015.
[13] Olden, J.D., Joy, M.K., Death, R. (2004). An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data, Ecological Modelling, 178(3- 4), 389-397, 2004.
https://doi.org/10.1016/j.ecolmodel.2004.03.013
[14] Oprea, M.; Mihalache, S.F.; Popescu, M. (2017). Computational Intelligence-based PM Air Pollution Forecasting, International Journal of Computers Communications & Control, 12(3), 365-380, 2017.
https://doi.org/10.15837/ijccc.2017.3.2907
[15] van Petegem, J.W.H. (Jan Hendrik); Fred Wegman, F. (2014). Analyzing road design risk factors for run-off-road crashes in the Netherlands with crash prediction models, Journal of Safety Research, 49, 121-127, 2014.
https://doi.org/10.1016/j.jsr.2014.03.003
[16] Rojas, M. (2015). Traffic Accident Prediction Using Artificial Neural Networks and Cuckoo Search, Thesis Project, Faculty of Computer Engineering, Pontifical Catholic University of Valparaíso, 2015.
[17] Slimani, N.; Slimani, I.; Sbit, N.; Amghar, M. (2019). Traffic forecasting in Morocco using artificial neural networks, Procedia Computer Science, 151, 471-476, 2019.
https://doi.org/10.1016/j.procs.2019.04.064
[18] Vasquez-Lopez, J.A.; Lopez-Juarez, I.; Peña-Cabrera, M.(2010). On the Use of the FuzzyARTMAP Neural Network for Pattern Recognition in Statistical Process Control using a Factorial Design, International Journal of Computers Communications & Control, 5(2), 205-2015, 2010.
https://doi.org/10.15837/ijccc.2010.2.2475
[19] Zhao, F. (2020). A Neural Network Classification Model Based on Covering Algorithm and Immune Clustering Algorithm, International Journal of Computers Communications & Control, 15(1), 1008, 2020.
https://doi.org/10.15837/ijccc.2020.1.3739
[20] [Online]. Available: https://www.conaset.cl/normativa-velocidad/. Last accessed (2018).
[21] [Online]. Available: https://la.mathworks.com/solutions/deep-learning/convolutional-neuralnetwork. html. Last accessed (2018).
https://doi.org/10.1260/2046-0430.1.4.305
[2] Amin, S. (2020). Backpropagation - Artificial Neural Network (BP-ANN): Understanding gender characteristics of older driver accidents in West Midlands of United Kingdom, Safety Science, 122, 104539, 2020.
https://doi.org/10.1016/j.ssci.2019.104539
[3] Basogain, X. (2008). Artificial Neural Networks and their applications. Higher School of Engineering of Bilbao, Department of systems and automatic engineering, EHU, 2008.
[4] Chilquinta Book of News the years 2016-2018, Valparaíso, 2018.
[5] Chis, V.; Barbulescu, C.; Kilyeni, S.; Dzitac, S. (2018). ANN based Short-Term Load Curve Forecasting, International Journal of Computers Communications & Control, 13(6), 938-955, 2018.
https://doi.org/10.15837/ijccc.2018.6.3385
[6] Ciupan, C.; Lungu, F.; Ciupan, C. (2014). ANN Method for Control of Robots to Avoid Obstacles, International Journal of Computers Communications & Control, 9(5), 539-554, 2014.
https://doi.org/10.15837/ijccc.2014.5.813
[7] Ciupan, E.; Lungu, F.; Ciupan, C. (2015). ANN Training Method with a Small Number of Examples Used for Robots Control, International Journal of Computers Communications & Control, 10(5), 643-653, 2015.
https://doi.org/10.15837/ijccc.2015.5.2027
[8] Deka, L.; Quddus, M. (2014). Network-level accident-mapping: Distance based pattern matching using artificial neural network, Accident Analysis and Prevention, 65, 105-113, 2014.
https://doi.org/10.1016/j.aap.2013.12.001
[9] Gagne, A.(2008). Evaluation of Utility Pole Placement and the Impact on Crash Rates, Proyecto de Tesis, Facultad de Ciencias, Worcester Polytechnic Institute, 2008.
[10] Good, M.C.; Fox, J.C.; Joubertm, P.N. (1987). An in-depth study of accidents involving collisions with utility pole, Accident Analysis & Prevention, 19(5), 397-413, 1987.
https://doi.org/10.1016/0001-4575(87)90024-8
[11] Moldovan, L.; Grif, H.-S.; Gligor, A. (2016). ANN Based Inverse Dynamic Model of the 6-PGK Parallel Robot Manipulator, International Journal of Computers Communications & Control, 11(1), 90-104, 2016.
https://doi.org/10.15837/ijccc.2016.1.1962
[12] Montt, C.; Rodriguez, N.; Valencia, A. (2015). Traffic accident prediction in the metropolitan region of Chile based on neural networks, Proceedings of the XVII Chilean Congress of Transportation Enginee, Concepción, Chile, 2015.
[13] Olden, J.D., Joy, M.K., Death, R. (2004). An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data, Ecological Modelling, 178(3- 4), 389-397, 2004.
https://doi.org/10.1016/j.ecolmodel.2004.03.013
[14] Oprea, M.; Mihalache, S.F.; Popescu, M. (2017). Computational Intelligence-based PM Air Pollution Forecasting, International Journal of Computers Communications & Control, 12(3), 365-380, 2017.
https://doi.org/10.15837/ijccc.2017.3.2907
[15] van Petegem, J.W.H. (Jan Hendrik); Fred Wegman, F. (2014). Analyzing road design risk factors for run-off-road crashes in the Netherlands with crash prediction models, Journal of Safety Research, 49, 121-127, 2014.
https://doi.org/10.1016/j.jsr.2014.03.003
[16] Rojas, M. (2015). Traffic Accident Prediction Using Artificial Neural Networks and Cuckoo Search, Thesis Project, Faculty of Computer Engineering, Pontifical Catholic University of Valparaíso, 2015.
[17] Slimani, N.; Slimani, I.; Sbit, N.; Amghar, M. (2019). Traffic forecasting in Morocco using artificial neural networks, Procedia Computer Science, 151, 471-476, 2019.
https://doi.org/10.1016/j.procs.2019.04.064
[18] Vasquez-Lopez, J.A.; Lopez-Juarez, I.; Peña-Cabrera, M.(2010). On the Use of the FuzzyARTMAP Neural Network for Pattern Recognition in Statistical Process Control using a Factorial Design, International Journal of Computers Communications & Control, 5(2), 205-2015, 2010.
https://doi.org/10.15837/ijccc.2010.2.2475
[19] Zhao, F. (2020). A Neural Network Classification Model Based on Covering Algorithm and Immune Clustering Algorithm, International Journal of Computers Communications & Control, 15(1), 1008, 2020.
https://doi.org/10.15837/ijccc.2020.1.3739
[20] [Online]. Available: https://www.conaset.cl/normativa-velocidad/. Last accessed (2018).
[21] [Online]. Available: https://la.mathworks.com/solutions/deep-learning/convolutional-neuralnetwork. html. Last accessed (2018).
Published
2020-08-30
How to Cite
MONTT, Cecilia et al.
Artificial Neural Network and a Nonlinear Regression Model for Predicting Electrical Pole Crash.
INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 15, n. 5, aug. 2020.
ISSN 1841-9844.
Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3879>. Date accessed: 22 jan. 2021.
doi: https://doi.org/10.15837/ijccc.2020.5.3879.
Section
Articles
Keywords
Artificial neural networks, prediction, nonlinear regression
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