Research On Forecasting Distribution Of Single Cargo Transportation Demand

With the deepening of the country’s supply-side reforms and the "One Belt One Road" development strategy,the logistics industry has entered a new critical period of transformation and upgrading,requiring more diversified transportation network structures and development methods.Freight demand forecasting can provide decision-making reference for the construction of regional logistics system and is an important basis for building a reasonable freight transportation network.Scientific and effective freight demand forecasting methods are the key to forecasting research.The current research mainly tends to forecast the total freight demand.There is a lack of forecasting of the transportation demand for a single cargo.At the same time,there are insufficient researches on the distribution of freight demand.Based on this,this article relies on the subproject of the Global Environment Facility funded project "China National Freight Analytical model research",constructed a transportation demand distribution forecasting model for a single cargo and verified and applied it with passenger car transportation as an example.The main research contents and conclusions are as follows:(1)Draw lessons from the theoretical framework of the four-stage method of transportation planning,and construct a forecast for a single cargo type to predict the target year’s freight demand through historical data at different scales,and to study its distribution in the regional space.model.Firstly,it discusses the division of freight districts under different research scales;secondly,constructs a combined forecasting method of total freight demand composed of exponential smoothing method,elasticity coefficient method and gray forecasting method;then constructs freight generation demand based on elasticity coefficient method.The forecasting model of the volume and attracting demand is provided,and the forecast result inspection index and inspection method are provided;finally,a forecasting model based on the improved gravity model for the spatial distribution of freight demand for a single cargo is constructed,and a competition coefficient is proposed.And the correction coefficient of the pros and cons coefficients are used to correct the problem of internal competition in freight districts and the uneven distribution of demand for the same kind of goods in freight districts with different purchasing power due to different grades.(2)Empirical analysis was carried out on the passenger vehicle transportation between 278 freight districts nationwide from 2010 to 2019 as an example,which verified the feasibility and reliability of the prediction model proposed in this paper,and further predicted the next five years.Distribution of demand for vehicle transportation.First of all,using historical data from 2010 to2018 and using the combined forecasting model to compare the forecasted value of 2019 with the actual value of 2019,the error range is within 4.72%,and there is a 95.28% confidence that the forecast is reliable.It is further predicted that the total national passenger vehicle transportation demand in 2024 will be 26,578,500.Secondly,through correlation analysis,it is concluded that the biggest influencing factor is the total national production and sales.Using the historical data of total national production and sales from 2010 to 2018,combined with the production and sales data of various freight districts in 2016 and 2018,the elasticity coefficient model is used to predict2019 Compared with the actual value in 2019,the error range is about 10%,which verifies the reliability of the prediction method to a certain extent.At the same time,the demand generated and attracted by various freight districts across the country in 2024 is respectively predicted.Finally,combining the production and attracting volume data of each freight district obtained in the previous step,the distribution prediction model constructed in Chapter 4 is used to calculate the distribution of passenger vehicle transportation demand among various freight districts across the country and visualize it.Display and analysis.