Neural network modeling of street and road network capacity

The problems of increasing the number of personal transportation vehicles in urban agglomeration as well as the number of cargos lead to applying Intelligent transportation systems based on Machine Learning techniques and Artificial Intelligence models to create strategies to reduce congestion and to prevent accidents. An important parameter of transportation system showing the effectiveness of using existing urban infrastructure is the capacity of the planned route. The paper is devoted to the creating model of urban route capacity based on the capacities of its elements, they are namely stretches and intersections. The approach to create such model is Mathematical Remodeling, where feed-forward neural network is chosen as a unified class to substitute models of different heterogeneous classes during modeling. It is proposed to use index of route capacity to form data sets for model fitting. The given numerical examples show how the proposed approach can be applied. The capacities of three planned routes are estimated and the best route is chosen, the efficiency criterion is traffic flow volume to capacity ratio. The prospective issue of the presented study is analyzing sensitivity of the created model to identify the parameters of route elements affecting the most to the capacity and to control them increasing the total efficiency of the system.

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