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Road accident risk prediction using generalized regression neural network optimized with self-organizing map

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Abstract

A road accident risk map that is designed to locate high-risk areas is an efficient way to reduce road traffic injuries and fatalities. To produce an accurate road accident risk map, it is required to compute the probability of the accident's occurrence by considering the various variables that contribute to road accidents. To this end, this research proposed a generalized regression neural network tuned with self-organizing map to estimate the risk of road accidents. This hybrid predictive model estimates the road accident risk by considering 22 different predictor variables (features), including geographic characteristics, temporal conditions, weather conditions, road-related characteristics, vehicle-related characteristics, and driver characteristics calculated based on the authoritative data sources and volunteered geographic information (VGI). The required VGI was collected in this study by developing a third-party application that was run inside telegram messenger. To evaluate the performance and usability of the proposed model for estimation of the accident risk along the road, the developed model was applied to the Tabriz-Marand dual carriageway, Iran. In this sense, 30 different scenarios were designed, and for each scenario, the risk of the accident was predicted at 3008 points along the Tabriz-Marand dual carriageway. A quality assessment of the proposed approach for different scenarios demonstrated that the predicted accident risk had very high accuracy (average accuracy about 90.74%). According to the results of this research, distance from traffic control cameras, day of the week, driver’s age, weather, elevation, and vehicle type were the most effective factors in high-risk areas of the study area.

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References

  1. Peden M. (2008) World report on road traffic injury prevention: summary. DIANE Publishing. ISBN: 1437904068, 978143790406.

  2. World Health Organization (WHO) (2015) Global status report on road safety. WHO Press, Geneva, Switzerland

    Google Scholar 

  3. Yakar F (2015) Identification of accident-prone road sections by using relative frequency method. Traffic Transp 27(6):539–547

    Google Scholar 

  4. Li Y, Ma D, Zhu M, Zeng Z, Wang Y (2018) Identification of significant factors in fatal-injury highway crashes using genetic algorithm and neural network. Accid Anal Prev 111:354–363. https://doi.org/10.1016/j.aap.2017.11.028

    Article  Google Scholar 

  5. Le KhG, Liu P, Lin L-T (2019). Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam. Geo-spatial Inf Sci. Access 02 Dec 2019

  6. Ha HH, Thill JC (2011) Analysis of traffic hazard intensity: a spatial epidemiology case study of urban pedestrians. Comput Environ Urban Syst 35:230–240

    Article  Google Scholar 

  7. Delmelle EC, Thill JC, Ha HH (2012) Spatial epidemiologic analysis of relative crash risk factors among urban bicyclists and pedestrians. Transportation 39(2):433–448

    Article  Google Scholar 

  8. Steenberghen T, Dufays T, Thomas I, Flahaut B (2004) Intra-urban location and clustering of road accidents using GIS: a Belgian Case. Int J Geograph Inf Sci 18(2):169–181

    Article  Google Scholar 

  9. Sun X 2003. Identifying highway safety patterns and trends with a crash data analysis program. In: The International forum on traffic records and highway information systems, Denver CO.

  10. Shad R, Mesgari A, Moghimi R (2013) Extraction of accidents prediction maps modeling hot spots in geospatial information system. International archives of the photogrammetry. Remote Sens Spat Inf Sci, vol XL-1/W3, SMPR 2013, 5–8 October 2013, Tehran, Iran

  11. Gupta R, Singh M (2014) Accident black spot validation using GIS. In: 15th Esri India user conference

  12. Zhou H, Zhao J, Pour-Rouholamin M, Tobias PA (2015) Statistical characteristics of wrong-way driving crashes on Illinois freeways. Traffic Inj Prev 16(8):760–767. https://doi.org/10.1080/15389588.2015.1020421

    Article  Google Scholar 

  13. Ma L, Yan X (2014) Examining the nonparametric effect of drivers’ age in rear-end accidents through an additive logistic regression model. Accid Anal Prev 67:129–136. https://doi.org/10.1016/j.aap.2014.02.021

    Article  Google Scholar 

  14. Santamariña-Rubio E, Pérez K, Olabarria M, Novoa AM (2014) Gender differences in road traffic injury rate using time travelled as a measure of exposure. Accid Anal Prev 65:1–7. https://doi.org/10.1016/j.aap.2013.11.015

    Article  Google Scholar 

  15. Regev Sh, Rolison JJ, Moutari S (2018) Crash risk by driver age, gender, and time of day using a new exposure methodology. J Safety Res 66:131–140. https://doi.org/10.1016/j.jsr.2018.07.002

    Article  Google Scholar 

  16. Watanabe Y, Sato K, Takada H (2020) DynamicMap 2.0: a traffic data management platform leveraging clouds, edges and embedded systems. Int J Intell Transp Syst Res 18:77–89. https://doi.org/10.1007/s13177-018-0173-7

    Article  Google Scholar 

  17. Zhang M, Wo T, Xie T, Lin X, Liu Y (2017) CarStream: an industrial system of big data processing for internet-of-vehicles. The Proceedings of the VLDB Endowment 10(12):1766–1777

    Article  Google Scholar 

  18. Stülpnagel RV, Krukar J (2018) Risk perception during urban cycling: An assessment of crowdsourced and authoritative data. Accid Anal Prev 121:109–117. https://doi.org/10.1016/j.aap.2018.09.009

    Article  Google Scholar 

  19. Vahidi H, Klinkenberg B, Johnson BA, Moskal LM, Yan W (2018) Mapping the individual trees in urban orchards by incorporating volunteered geographic information and very high resolution optical remotely sensed data: a template matching-based approach. Remote Sens 10:1134. https://doi.org/10.3390/rs10071134

    Article  Google Scholar 

  20. PostgreSQL: Documentation:903: psql. Retrieved February 13, 2020. https://www.postgresql.org/docs/9.3/app-psql.html%20 (last%20online:)

  21. Hotelling H (1931) The generalization of student’s ratio. Ann Math Stat 2:360–378

    Article  Google Scholar 

  22. Peter King A, Eckersley RJ (2019) Inferential statistics V: multiple and multivariate hypothesis testing. Statistics for Biomedical Engineers and Scientists, Chapter. https://doi.org/10.1016/B978-0-08-102939-8.00017-7

    Article  Google Scholar 

  23. Smith LI (2002) A tutorial on principal component analysis. Department Science, University of California, San Diego

  24. Shanmugam R, Johnson Ch (2007) At a crossroad of data envelopment and principal component analyses. Omega 35:351–364

    Article  Google Scholar 

  25. Xiaodong X, Cheng Ch, Tiantian S, Gulinur M, Xinjuan W, Wenjuan Zh, Rui G, Fangfang Ch, Wei W, Yangyang F, Xiaoyi L, Guohua W (2020) Rapid, non-invasive screening of keratitis based on Raman spectroscopy combined with multivariate statistical analysis. Photodiagnosis Photodyn Ther; 31, 101932,ISSN 1572–1000,https://doi.org/10.1016/j.pdpdt.2020.101932.

  26. Imaizumi K, Bermejo E, Taniguchi K, Ogawa Y, Nagata T, Kaga K, Hayakawa H, Shiotani S (2020) Development of a sex estimation method for skulls using machine learning on three-dimensional shapes of skulls and skull parts. Forensic Imaging; 22, 200393. ISSN 2666–2256. https://doi.org/10.1016/j.fri.2020.200393.

  27. Si Y, Schlerf M, Zurita-Milla R, Skidmore A, Wang T (2012) Mapping spatio-temporal variation of grassland quantity and quality using MERIS data and the PROSAIL model. Remote Sens Environ; 121: 415–425. ISSN 0034–4257. https://doi.org/10.1016/j.rse.2012.02.011.

  28. Pearson K (1895) Notes on regression and inheritance in the case of two parents. In: Proceedings of the royal society of London, vol 58, pp 240–242

  29. Kursah MB (2017) GIS and correlation analysis of geo-environmental variables influencing malaria prevalence in the Saboba district of Northern Ghana. Ghana Journal of Geography 9(3):112–131

    Google Scholar 

  30. LeBlanc DC (2004) Statistics: concepts and applications for science. Jones & Bartlett Learning. ISBN-10: 0763746991

  31. Specht DE (1993) The general regression neural network-rediscovered. Neural Netw 6(7):1033–1034

    Article  Google Scholar 

  32. Xuecai X, Gui F, Yujingyang X, Ziqi Zh, Ping Ch, Baojun L, Song J (2018) Risk prediction and factors risk analysis based on IFOA-GRNN and apriori algorithms: application of artificial intelligence in accident prevention. Process Saf Environ Prot. https://doi.org/10.1016/j.psep.2018.11.019

    Article  Google Scholar 

  33. Ghritlahre HK, Prasad RK (2017) Prediction of thermal performance of unidirectional flow porous bed solar air heater with optimal training function using artificial neural network. Energy Procedia 109:369–376. https://doi.org/10.1016/j.egypro.2017.03.033

    Article  Google Scholar 

  34. Al-Mahasneh AJ, Anavatti SG, Garratt MA (2018) Review of applications of generalized regression neural networks in identification and control of dynamic systems. arXiv preprint https://arxiv.org/abs/1805.11236.

  35. Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2(6):568–576

    Article  Google Scholar 

  36. Wasserman PD (1993) Advanced methods in neural computing, 155–161. Van Nostrand Reinhold, New York

    Google Scholar 

  37. Shao S, Chen J (2011) A novel error concealment approach based on general regression neural network. In: International conference on consumer electronics, communications and Networks (CECNet), pp 4679–4682, https://doi.org/10.1109/CECNET.2011.5768232

  38. Ghritlahre HK, Prasad RK (2018) Investigation of thermal performance of unidirectional flow porous bed solar air heater using MLP, GRNN, and RBF models of ANN technique. Therm Sci Eng Prog 6:226–235

    Article  Google Scholar 

  39. Marek, G., 2015. Spread measures and their relation to aggregation functions. European Journal of Operational Research, Volume 241, Issue 2, 2015, Pages 469–477, ISSN 0377–2217, https://doi.org/10.1016/j.ejor.2014.08.034.

  40. Steven CH (2014) The Z-effect: Why good is good, but better is better. Int J Mark Stud; 6(3). ISSN 1918–719X E-ISSN 1918–7203. https://doi.org/10.5539/ijms.v6n3p1

  41. Tiago PC (2013) Systematics and evolution of the toothless knifefishes rhamphichthyoidea Mago-Leccia (Actinopterygii: Gymnotiformes): diversification in South American Freshwaters. Published by ProQuest LLC (2014)

  42. Zhang L, Lu G, Qi Y (2010) Permeability extracting using GRNN method. In: Asia-Pacific international symposium on electromagnetic compatibility, pp 1657–1659. https://doi.org/10.1109/APEMC.2010.5475720

  43. Pena BD, Blakely L, Reno MJ (2021) Parameter tuning analysis for phase identification algorithms in distribution system model calibration. IEEE Kansas Power and Energy Conference (KPEC), pp 1–6. https://doi.org/10.1109/KPEC51835.2021.9446218

  44. Zhou J, Chen Y, Wang J, Zhan W (2011) Maximum Nighttime Urban Heat Island (UHI) intensity simulation by integrating remotely sensed data and meteorological observations. IEEE J Sel Top Appl Earth Obs Remote Sens 4(1):138–146. https://doi.org/10.1109/JSTARS.2010.2070871

    Article  Google Scholar 

  45. Zeng J, Roussis PC, Mohammed AS, Maraveas C, Fatemi SA, Armaghani DJ, Asteris PG (2021) Prediction of peak particle velocity caused by blasting through the combinations of boosted-CHAID and SVM models with various kernels. Appl Sci 11(8):3705. https://doi.org/10.3390/app11083705

    Article  Google Scholar 

  46. Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1):59–69. https://doi.org/10.1007/bf00337288

    Article  MathSciNet  MATH  Google Scholar 

  47. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232

    Article  MathSciNet  Google Scholar 

  48. Herbert J, Yao JT (2005) A game-theoretic approach to competitive learning in self-organizing maps. In: ICNC'05: Proceedings of the First international conference on advances in natural computation, vol Part I, pp 129–138. https://doi.org/10.1007/11539087_15

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Authors and Affiliations

  1. Geomatic Department, Marand Technical Faculty, University of Tabriz, Tabriz, Iran

    Neda Kaffash Charandabi, Amir Gholami & Ali Abdollahzadeh Bina

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  1. Neda Kaffash Charandabi
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Correspondence to Neda Kaffash Charandabi.

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Kaffash Charandabi, N., Gholami, A. & Abdollahzadeh Bina, A. Road accident risk prediction using generalized regression neural network optimized with self-organizing map. Neural Comput & Applic 34, 8511–8524 (2022). https://doi.org/10.1007/s00521-021-06549-8

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