Transportation Demand Forecast of Bulk Cargo Based on GM(1, 1)-MLP Neural Network Model

In view of the complexity of bulk cargo transportation demand forecast, a forecast method of bulk cargo transportation demand based on the production and transportation coefficient is put forward, and the route of transport structure adjustment is determined according to the development trend of bulk cargo transport demand. Taking Heilongjiang province as an example, taking into account factors such as the amount of chemical fertilizer applied, rural electricity consumption, total power of agricultural machinery and the area sown for grain crops, GM(1, 1) model and GM(1, 1) -MLP neural network model are established to forecast grain yield, and are verified by actual data.Heilongjiang's grain production and transportation coefficient are determined based on statistics on Heilongjiang's permanent population, urbanisation rate of permanent population, food production, per capita food consumption of urban and rural population, and the grain transport volume for the next few years is forecast combining with the forecast of grain output and the production and transportation coefficient to analyze the trend of grain transport demand in Heilongjiang and provide a basis for formulating the structural adjustment plan for bulk cargo transport in Heilongjiang. The result shows that (1) compared with GM(1, 1) model, the precision of GM(1, 1)-MLP neural network model is improved by 1.68%; (2) based on the forecast results, the demand for grain transport in Heilongjiang will continue to increase, and the transport demand will continue to increase, Heilongjiang is still the main part of the bulk cargo transport object, actively adjusting the grain transport structure, promoting the shift of medium and long distance grain transport to railway transport, and road transport as a short-barge distribution at both ends of railway transport, combined rail and public transport plays an important role in optimizing Heilongjiang's bulk cargo transport structure.