Implementation of Random Forest Algorithm in Order to Use Big Data to Improve Real-Time Traffic Monitoring and Safety
Subject Areas : Clustering and ClassificationNegin Fatholahzade 1 , Gholamreza Akbarizadeh 2 , Morteza Romoozi 3
1 - Computer Department, Faculty of Engineering, Islamic Azad University E-Campus, Tehran, Iran
2 - Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
3 - Department of Computer Engineering, kashan Branch, Islamic Azad University, Kashan, IRAN
Keywords:
Abstract :
[1] A. Baruya, "Speed-accident relationships on Eu-ropean roads." 9th International Conference on Road Safety in Europe. (1998).
[2] B. Leo. "RandomForest: Breiman and Cutler’s ran-dom forests for classification and regression." (2006): 4-5.
[3] B. Leo. "Random forests." Machine learning 45.1 (2001): 5-32.
[4] C. Dias, M. Miska, M. Kuwahara, and H. Warita, "Relationship between congestion and traffic ac-cidents on expressways: an investigation with Bayesian belief networks."Proceedings of 40th Annual Meeting of Infrastructure Planning (JSCE), Japan. (2009).
[5] D. Schrank, B. Eisele, and T. Lomax. "TTI’s 2012 urban mobility report." Texas A&M Transportation Institute. The Texas A&M University System (2012).
[6] L. Andy, and M. Wiener. "Classification and re-gression by randomForest." R news 2.3 (2002): 18-22.
[7] M. Abdel-Aty, and K. Haleem. "Analyzing angle crashes at unsignalized intersections using ma-chine learning techniques." Accident Analysis & Prevention 43.1 (2011): 461-470.
[8] M.A. Beyer, and D. Laney, "The importance of ‘big data’: a definition." Stamford, CT: Gartner (2012): 2014-2018.
[9] M.Ahmed, et al. "Exploring a Bayesian hierarchical approach for developing safety performance func-tions for a mountainous freeway." Accident Analy-sis & Prevention 43.4 (2011): 1581-1589.
[10] 3M. Grant.et al, “Congestion management pro-cess: A guidebook”. No. FHWA-HEP-11-011. 2011.
[11] P.J. Hammond, "The 2012 Congestion Report." WSDOT’s comprehensive annual analysis of state highway system performance, 11th edition, Wash-ington State DOT (2012).
[12] R.Yu, M.Abdel-Aty, M.Ahmed." Bayesian random effect models incorporating realtime weather and traffic data to investigate mountainous freeway hazardous factors." Accident Analysis & Preven-tion 50 (0), 371-376. (2013).
[13] S.Carolin, et al. "Bias in random forest variable importance measures: Illustrations, sources and a solution." BMC bioinformatics 8.1 (2007): 25.
[14] S. Carolin, et al. "Conditional variable importance for random forests." BMC bioinformatics 9.1 (2008): 307.
[15] S.M. Turner, “.et al” Travel time data collection handbook. No. FHWA-PL-98-035. 1998.
[16] W.Logan , P.Mical , A.Steen ,“Automatic learning of mortality in a CPN model of the systemic in-flammatory response syndrome” , ScienceDirect, Novel Models(2017), Analysis and Methods in Medical Systems.
[17] X.Sun, et al. "Research on Traffic State Evaluation Method for Urban Road." Intelligent Transporta-tion, Big Data and Smart City (ICITBS), 2015 Inter-national Conference on. IEEE, (2015).
[18] Y.Kryftis, G.Mastorakis, C.Mavromoustakis, J. Mongay Batalla, E. Pallis and G. Kormentzas ,“Efficient Entertainment Services Provision over a Novel Network Architecture”. To be published in IEEE Wireless Communications Magazine. (2016).
[19] Yu. Rongjie, and M. Abdel-Aty. "Analyzing crash injury severity for a mountainous freeway incorpo-rating real-time traffic and weather data." Safety science 63 (2014): 50-56.