fuzzy method

These days, the problem of traffic and traffic congestion on crowded streets has become an integral part of life in metropolitan areas. Intelligent traffic control will be crucial as the number of continuous road users increases and the resources provided by the current infrastructure are limited. The purpose of this study is to model traffic flow and control ramp measurement using fuzzy method; To achieve this goal, traffic ramps at the entrances are used to regulate access to the main corridor of the highway or freeway, in order to maintain the flow close to the capacity of the highway. In this research, by presenting a new solution, modeling traffic flow and controlling ramp measurement has been done by fuzzy intelligent method in MATLAB software environment. Data collection was performed using field method and on the highways of Isfahan. This algorithm tries to make the ramp control system intelligent by maintaining simplicity and non-computational complexity. According to the information received from the vehicle, which are located at certain distances from each ramp, the queue length, vehicle entry rate and demand level of each ramp are measured, and then the controller determines the minimum time and phase of the ramp according to this information. According to the obtained results, the average intensity of the factors of total queue length in the ramp, stop time in the ramp, stopping times and traffic congestion in the open loop mode is higher than the closed loop mode based on the fuzzy method algorithm.

With the increase in road transport in Iran, accidental damage has also increased dramatically. If accurate predictions are made using appropriate techniques, the percentage of actual and effective factors to improve the conditions of the accident can be determined. This research has been done using fuzzy logic in MATLAB software, and a detailed analysis of the data has been done and then the results have been obtained. The proposed model consists of three inputs, an inference engine in the fuzzy method and the output. In the first stage, six variables were considered as inputs: longitudinal slope, velocity, shoulder path, subterranean intersection, arc radius, gap gap. These factors are considered with the greatest impact on road accidents. The next stage of the accidental points was considered as the output, and in the third step, for the inference engine, the rules were written descriptively. Finally, the results are compared with the actual statistics. To be This approach will enable prediction of accidents in the coming year and determine the best method for modifying the components to minimize road accidents. The selected method is implemented and evaluated case by case in Birjand axis. . The results suggest that the proposed inference framework will be able to detect accident patterns and predict the severity of road accidents, thereby choosing the best option and priority for repair, repair and maintenance.