Based On ANN Parking Demand Forecasting Of Middle City Central District

The parking demand is a complex problem with many influence factories, such as the society development and individual habit, and the unmeasured factories usually are left in the basket in the fact problem. In this paper, the parking policy becomes one of the influence factories, the parking demand of the onside, the society and the annexes are forecasted with the prediction model based on artificial neural network, and the training sample are the parking data of the middle cities. For the aim, this paper studies on four facets.Firstly, find the influence factories of the onside parking, the society parking and the annexes parking with a qualitative analysis. Research the parking demand characteristics, then find out the influence factories in the conventional prediction models, and analyze the relation to the types of parking, and choose the influence factories which be wanted.Secondly, the prediction modeling based on BP artificial neural network for parking demand. The input nerve cells are the influence factories that chosen in Chapter 2, the number of nerve cells for hidden layer are given out through tests, and the onside parking demand, the society parking demand and the annexes parking demand are the output nerve cells. Train the network by the reformative BP arithmetic, training sample are the data in Appendixâ… , Tab.1, Tab.2 and Tab.3, then checkout the network, and forecast the parking demand of the onside, the society and the annexes. The model is made of Matlab computer language.Thirdly, elementarily research the parking policy of the central district for middle cities. Analyze the actuality problems and their reasons, discuss the parking policy contents of the central district for middle cities.Finally, analyze the case. Forecast the parking demand of Jinzhou central district with trained artificial neural network prediction model, and analyze the...