Spatial modelling of accidents risk caused by driver drowsiness with data mining algorithms

References

• Al-Omari A, Shatnawi N, Khedaywi T, Miqdady T. 2020. Prediction of traffic accidents hot spots using fuzzy logic and GIS. Appl Geomat. 12(2):149–113.
   Web of Science ®Google Scholar
• Al-Radaideh QA, Daoud EJ. 2018. Data mining methods for traffic accident severity prediction. Int J Neural Netw Adv Appl. 5:1–12.
   Google Scholar
• Al-Turaiki I, Aloumi M, Aloumi N, Alghamdi K. 2016. Modeling traffic accidents in Saudi Arabia using classification techniques. 4th Saudi International Conference on Information Technology (Big Data Analysis) (KACSTIT); Nov 6–9; Riyadh, Saudi Arabia. IEEE.
   Google Scholar
• Azami-Aghdash S, Gorji HA, Sadeghi-Bazargani H, Shabaninejad H. 2016. Epidemiology of road traffic injuries in Iran: based on the data from Disaster Management Information System (DMIS) of the Iranian Red Crescent. Iran Red Crescent Med J. 19(1):1–12.
   Web of Science ®Google Scholar
• Beshah T, Hill S. 2010. Mining road traffic accident data to improve safety: role of road-related factors on accident severity in Ethiopia. Artificial Intelligence for Development, Papers from the 2010 AAAI Spring Symposium, Technical Report SS-10-01, Mar 22–24; Stanford, California, USA.
   Google Scholar
• Bhalla K, Naghavi M, Shahraz S, Bartels D, Murray CJ. 2009. Building national estimates of the burden of road traffic injuries in developing countries from all available data sources: Iran. Inj Prev. 15(3):150–156.
   PubMed Web of Science ®Google Scholar
• Breiman L. 2001. Random forests. Mach Learn. 45(1):5–32.
   Web of Science ®Google Scholar
• Budzyński M, Kustra W, Okraszewska R, Jamroz K, Pyrchla J. 2018. The use of GIS tools for road infrastructure safety management. E3S Web of Conf EDP Sci. 26:00009.
   Google Scholar
• Carrasco OC. 2019. Support Vector Machines for Classification. Towards data science; [accessed 2020 May 21]. https://towardsdatascience.com/support-vector-machines-for-classification-fc7c1565e3.
   Google Scholar
• Chai T, Draxler RR. 2014. Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geosci Model Dev. 7(3):1247–1250.
   Web of Science ®Google Scholar
• Chiou Y-C, Hwang C-C, Chang C-C, Fu C. 2013. Modeling two-vehicle crash severity by a bivariate generalized ordered probit approach. Accid Anal Prev. 51:175–184.
   PubMed Web of Science ®Google Scholar
• Chun HJ. 2017. The effect of ‘drowsy shelters’ in preventing traffic accidents in South Korea..Lexington: University of Kentucky.
   Google Scholar
• Clarke SM, Griebsch JH, Simpson TW. 2005. Analysis of support vector regression for approximation of complex engineering analyses. J. Mech. Des. 127(6):1077–1087.
   Web of Science ®Google Scholar
• Couto A, Ferreira S. 2011. A note on modeling road accident frequency: a flexible elasticity model. Accid Anal Prev. 43(6):2104–2111.
   PubMed Web of Science ®Google Scholar
• Danjuma KJ. 2015. Performance evaluation of machine learning algorithms in post-operative life expectancy in the lung cancer patients. IJCSI Int J Comp Sci Issues. 12(2):11 pp.
   Google Scholar
• Debrabant B, Halekoh U, Bonat WH, Hansen DL, Hjelmborg J, Lauritsen J. 2018. Identifying traffic accident black spots with Poisson-Tweedie models. Accid Anal Prev. 111:147–154.
   PubMed Web of Science ®Google Scholar
• Dereli MA, Erdogan S. 2017. A new model for determining the traffic accident black spots using GIS-aided spatial statistical methods. Transp Res A. 103:106–117.
   Google Scholar
• Driss M, Benabdeli K, Saint-Gerand T, Hamadouche M. 2015. Traffic safety prediction model for identifying spatial degrees of exposure to the risk of road accidents based on fuzzy logic approach. Geocarto Int. 30(3):243–257.
   Web of Science ®Google Scholar
• Du H, Zhao X, Zhang X, Zhang Y, Rong J. 2015. Effects of fatigue on driving performance under different roadway geometries: a simulator study. Traffic Inj Prev. 16(5):468–473.
   PubMed Web of Science ®Google Scholar
• Erdogan S, Yilmaz I, Baybura T, Gullu M. 2008. Geographical information systems aided traffic accident analysis system case study: city of Afyonkarahisar. Accid Anal Prev. 40(1):174–181.
   PubMed Web of Science ®Google Scholar
• Farahmand B, Boroujerdian AM. 2018. Effect of road geometry on driver fatigue in monotonous environments: a simulator study. Transp Res F. 58:640–651.
   Google Scholar
• Geurts K, Thomas I, Wets G. 2005. Understanding spatial concentrations of road accidents using frequent item sets. Accid Anal Prev. 37(4):787–799.
   PubMed Web of Science ®Google Scholar
• Goktepe AB, Lav AH. 2003. Method for balancing cut-fill and minimizing the amount of earthwork in the geometric design of highways. J Transp Eng. 129(5):564–571.
   Google Scholar
• Gregoriades A, Chrystodoulides A. 2018. Extracting traffic safety knowledge from historical accident data. Adjunct Proceedings of the 14th International Conference on Location Based Services; Jan 15–17; ETH Zurich, Switzerland.
   Google Scholar
• Hazaa MA, Saad RM, Alnaklani MA. 2019. Prediction of traffic accident severity using data mining techniques in ibb province, Yemen. Int J Softw Eng Comp Syst. 5(1):77–92.
   Google Scholar
• Honglei R, Song Y, Wang J, Hu Y, Lei J. 2018. A deep learning approach to the citywide traffic accident risk prediction. 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.
   Google Scholar
• Huang H, Abdel-Aty MA, Darwiche AL. 2010. County-level crash risk analysis in Florida: Bayesian spatial modeling. Transp Res Rec. 2148(1):27–37.
   Web of Science ®Google Scholar
• IHME 2017. What causes the most deaths?. IHME. [accessed 2020 Apr 26]. http://www.healthdata.org/iran.
   Google Scholar
• Jung S, Joo S, Oh C. 2017. Evaluating the effects of supplemental rest areas on freeway crashes caused by drowsy driving. Accid Anal Prev. 99(Pt A):356–363.
   PubMedGoogle Scholar
• Kulkarni VY, Sinha PK. 2012. Pruning of random forest classifiers: a survey and future directions. International Conference on Data Science & Engineering (ICDSE); Jul 18–20; Cochin, Kerala, India. IEEE.
   Google Scholar
• Kumar D, Singh R, Kaur R. 2019. GIS databases: spatial and non-spatial. spatial information technology for sustainable development goals. Singapore: Springer; p. 15–25.
   Google Scholar
• Liaw A, Wiener M. 2002. Classification and regression by random Forest. R News. 2(3):18–22.
   Google Scholar
• Ling W-K. 2007. 2 – Reviews. In: Ling W-K, editor. Nonlinear digital filters. Oxford: Academic Press; p. 8–31.
   Google Scholar
• Mennis J, Guo D. 2009. Spatial data mining and geographic knowledge discovery – an introduction. Comput Environ Urban Syst. 33(6):403–408.
   Web of Science ®Google Scholar
• Mestri RA, Rathod RR, Garg RD. 2020. Identification and removal of accident-prone locations using spatial data mining. applications of geomatics in civil engineering. Singapore: Springer; p. 383–394.
   Google Scholar
• Mohammadi MA, Samaranayake V, Bham GH. 2014. Crash frequency modeling using negative binomial models: an application of generalized estimating equation to longitudinal data. Anal Methods Accid Res. 2:52–69.
   Google Scholar
• Moradi A, Nazari SSH, Rahmani K. 2019. Sleepiness and the risk of road traffic accidents: a systematic review and meta-analysis of previous studies. Transp Res F. 65:620–629.
   Google Scholar
• Naboureh A, Feizizadeh B, Naboureh A, Bian J, Blaschke T, Ghorbanzadeh O, Moharrami M. 2019. Traffic accident spatial simulation modeling for planning of road emergency services. ISPRS Int J Geo-Inform. 8(9):371.
   Web of Science ®Google Scholar
• Narkhede S. 2018. Understanding AUC-ROC curve. Towards Data Sci. 26:220–227.
   Google Scholar
• Nasiri H, Edrissi A. 2006. Modeling truck accident severity on two-lane rural highways. Sci Iran. 13(2):193–200.
   Google Scholar
• Nayak R, Emerson D, Weligamage J, Piyatrapoomi N. 2010. Using data mining on road asset management data in analysing road crashes. 16th Annual TMR Engineering & Technology Forum, Brisbane Australia.
   Google Scholar
• Noble WS. 2006. What is a support vector machine? Nat Biotechnol. 24(12):1565–1567.
   PubMed Web of Science ®Google Scholar
• Oron-Gilad T, Hancock PA. 2005. Road environment and driver fatigue, Rockport, Maine, USA. https://ir.uiowa.edu/cgi/viewcontent.cgi?article=1179&context=drivingassessment
   Google Scholar
• Oron-Gilad T, Ronen A. 2007. Road characteristics and driver fatigue: a simulator study. Traffic Inj Prev. 8(3):281–289.
   PubMedGoogle Scholar
• Pourghasemi HR, Termeh SVR, Kariminejad N, Hong H, Chen W. 2020. An assessment of metaheuristic approaches for flood assessment. J Hydrol. 582:124536.
   Web of Science ®Google Scholar
• Razavi-Termeh SV, Sadeghi-Niaraki A, Choi S-M. 2019. Groundwater potential mapping using an integrated ensemble of three bivariate statistical models with random forest and logistic model tree models. Water. 11(8):1596.
   Web of Science ®Google Scholar
• Razavi-Termeh SV, Sadeghi-Niaraki A, Choi S-M. 2020. Gully erosion susceptibility mapping using artificial intelligence and statistical models. Geomatics Nat Hazards Risk. 11(1):821–845.
   Web of Science ®Google Scholar
• Ren H, Song Y, Wang J, Hu Y, Lei J. 2018. A deep learning approach to the citywide traffic accident risk prediction. 21st International Conference on Intelligent Transportation Systems (ITSC); Maui, HI, USA. IEEE.
   Google Scholar
• Shafabakhsh GA, Famili A, Bahadori MS. 2017. GIS-based spatial analysis of urban traffic accidents: case study in Mashhad, Iran. J Traffic Transp Eng (English Ed.). 4(3):290–299.
   Google Scholar
• Shah SAR, Brijs T, Ahmad N, Pirdavani A, Shen Y, Basheer MA. 2017. Road safety risk evaluation using gis-based data envelopment analysis – artificial neural networks approach. Appl Sci. 7(9):886.
   Google Scholar
• Shaik AB, Srinivasan S. 2019. A brief survey on random forest ensembles in classification model. In: Bhattacharyya S, Hassanien A, Gupta D, Khanna A, Pan I, editors. International conference on innovative computing and communications. Lecture Notes in Networks and Systems, vol 56. Singapore: Springer.
   Google Scholar
• Sharma H, Kumar S. 2016. A survey on decision tree algorithms of classification in data mining. Int J Sci Res. 5(4):2094–2097.
   Google Scholar
• Statnikov A, Aliferis CF, 2007. Are random forests better than support vector machines for microarray-based cancer classification? AMIA Annu Symp Proc. 2007:686–690.
   Google Scholar
• Statnikov A, Wang L, Aliferis CF. 2008. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinform. 9(1):319.
   PubMed Web of Science ®Google Scholar
• Teli S, Kanikar P. 2015. A survey on decision tree based approaches in data mining. Int J Adv Res Comp Sci Softw Eng. 5(4): 613–617.
   Google Scholar
• Thiffault P, Bergeron J. 2003. Monotony of road environment and driver fatigue: a simulator study. Accid Anal Prev. 35(3):381–391.
   PubMed Web of Science ®Google Scholar
• Usman T, Fu L, Miranda-Moreno LF. 2010. Quantifying safety benefit of winter road maintenance: accident frequency modeling. Accid Anal Prev. 42(6):1878–1887.
   PubMed Web of Science ®Google Scholar
• Vemulapalli SS, Ulak MB, Ozguven EE, Sando T, Horner MW, Abdelrazig Y, Moses R. 2017. GIS-based spatial and temporal analysis of aging-involved accidents: a case study of three counties in Florida. Appl Spatial Anal. 10(4):537–563.
   Web of Science ®Google Scholar
• Wang J, Sun S, Fang S, Fu T, Stipancic J. 2017. Predicting drowsy driving in real-time situations: using an advanced driving simulator, accelerated failure time model, and virtual location-based services. Accid Anal Prev. 99(Pt A):321–329.
   PubMedGoogle Scholar
• Wang L, Pei Y. 2014. The impact of continuous driving time and rest time on commercial drivers' driving performance and recovery. J Safety Res. 50:11–15.
   PubMed Web of Science ®Google Scholar
• WHO 2018. Global Status Report on Road Safety 2018. WHO; [accessed 2019 Oct 11]. https://www.who.int/publications-detail/global-status-report-on-road-safety-2018.
   Google Scholar
• Xie K, Wang X, Ozbay K, Yang H. 2014. Crash frequency modeling for signalized intersections in a high-density urban road network. Anal Methods Accid Res. 2:39–51.
   Google Scholar
• Zhao X, Rong J. 2013. The relationship between driver fatigue and monotonous road environment. In Computational intelligence for traffic and mobility. Paris: Atlantis Press; p. 19–36.
   Google Scholar
• Zheng Z, Lu P, Lantz B. 2018. Commercial truck crash injury severity analysis using gradient boosting data mining model. J Safety Res. 65:115–124.
   PubMed Web of Science ®Google Scholar
• Zong F, Xu H, Zhang H. 2013. Prediction for traffic accident severity: comparing the Bayesian network and regression models. Math Prob Eng. 2013:1–9.
   Web of Science ®Google Scholar