Neural Networks Approach to Remodel Capacity on Urban Road and Street Network

The growing number of cars and trucks in cities leads to traffic jams and accidents. To solve this problem, cities have to use smart transportation systems powered by artificial intelligence models and machine learning techniques. An important parameter of transportation systems showing the effectiveness of using existing urban infrastructure is the capacity of the planned route. The paper is devoted to the modeling of urban route capacity based on the capacities of its elements, namely stretches and intersections. The approach to create such a 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 that most capacity and to control them increasing the total efficiency of the system.