Document Type : Original Research Paper


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


Nowadays the active traffic management is enabled for better performance due to the nature of the real-time large data in transportation system. With the advancement of large data, monitoring and improving the traffic safety transformed into necessity in the form of actively and appropriately. Per-formance efficiency and traffic safety are considered as an im-portant element in measuring the performance of the system. Although the productivity can be evaluated in terms of traffic congestion, safety can be obtained through analysis of incidents. Exposure effects have been done to identify the Factors and solutions of traffic congestion and accidents.
In this study, the goal is reducing traffic congestion and im-proving the safety with reduced risk of accident in freeways to improve the utilization of the system. Suggested method Man-ages and controls traffic with use of prediction the accidents and congestion traffic in freeways. In fact, the design of the real-time monitoring system accomplished using Big Data on the traffic flow and classified using the algorithm of random-ized forest and analysis of Big Data Defined needs. Output category is extracted with attention to the specified characteristics that is considered necessary and then by Alarms and signboards are announced which are located in different parts of the freeways and roads. All of these processes are evaluated by the Colored Petri Nets using the Cpn Tools tool.


Main Subjects

[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. 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.