implementation of random forest algorithmin order to use big data to improve real-timetraffic monitoring and safety

Nowadays the active traffic management is enabled for better performance due to the nature of the realtime 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. performance efficiency and traffic safety are considered as an important 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 manages and controls traffic with use of prediction the accidents and congestion traffic in freeways. in fact, the design of the realtime monitoring system accomplished using big data on the traffic flow and classified using the algorithm of randomized 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.