Logistics Park’s Logistics Capacity Forecast Based On Forecasting Model And Its Empirical Study

Under the background of the economic globalization, the proportion of the tertiaryindustry occupies more and more, the logistics industry as one of the tertiary industry, makesgreat contribution for each link in the social economic activities smoothly. To protect thedevelopment of logistics industry, state and local governments have issued various positivemeasures, rationally for logistics park planning and construction has become an important partof the regional economic development. The logistics demand forecast is an integral part of thelogistics park planning and construction. The prediction of logistics capacity can not onlyprovide the decision-making basis for the operation of logistics park and logisticsinfrastructure, the prediction results is reasonable and will affect the healthy development ofthe logistics business and logistics infrastructure’s return, to ensure the coordinateddevelopment of regional economy, rational integration of logistics resources, balance thelogistics supply capacity has the important guiding role.This paper mainly studies the logistics park traffic of forecasting model and itsapplication. Firstly, logistics park, logistics capacity and other related theories are described,and it introduces the research status of the theory at home and abroad.On this basis, this paperpoints out that the diversity of the prediction method, and according to the characteristics ofmaterial flow prediction and the existence of the problem, selects the appropriate model toforecast. Because of the traditional GM (1,1) model limitations, this paper is based on thetraditional grey model, to improve and take full advantage of new information, improve theprediction accuracy. In the empirical research of the Zongfa logistics park, according to theforecast method of the improved grey model, it makes full use of the original data, to definethe process flow as the standard, so get the Zongfa logistics park logistics capacity in the nextfew years. At the same time, the prediction results were compared with the traditional model,the new GM (1,1) model has a better prediction precision.