Effectiveness of Traffic Plans Using Neural Network in GIS Environment (Case Study: Sanandaj)

Cities need to take advantage of traffic control policies to address the increasing demand for private car traffic, commercial traffic, public transportation and access to surrounding land uses as well as parking of urban traffic management systems, providing policies and policies such as one-way passages, coupled and individual plans, including Policies are applied to manage urban travel demand by urban management. The city of Sanandaj has been developing and implementing various traffic policies due to its high traffic density in its central part. The present study also evaluates the impact of applying the above policies on various dimensions such as reducing traffic, reducing traffic accidents, traveling time and time, and energy consumption is. The present research is based on the type and nature of Drew in terms of methodology in the field of descriptive-analytic research. The research community estimates traffic accidents and volume traffic congestion. Part of the statistics and information was obtained from the traffic police of Sanandaj and the municipality of this city. Another part was also taken through field observations. Geographic information system (GIS) and neural network analysis model were used for analyzing information. The results indicate that traffic policies have increased the 6250-meter-long route along with a 0.5-liter increase in petrol consumption in the central part of the city of Sanandaj. The results also show that, despite the impact of traffic policies on traffic volumes, this effect is negligible due to the increase in the coefficient of personal car.