Research On Demand Forecast And Development Countermeasures Of Urban Logistics Based On Arima-BP

In recent years,with the improvement of regional coordinated development and industrial division of labor,the modern logistics industry has gradually developed into an important basic service industry in China's national economic system.In the field of meso logistics,urban logistics also plays an important role in serving the needs of the city and promoting the rapid development of the city's economy.However,the formation of urban economy is the foundation for the growth of urban logistics.The lack of urban transportation capacity and the lagged behind transportation infrastructure have seriously affected the efficiency of urban logistics.Only when the transportation capacity matches the urban logistics demand can it truly reflect its economic value and social value.So predicting logistics demand in advance can reduce capacity waste,improve infrastructure in a targeted manner,and improve logistics efficiency.Logistics needs are often related to macroeconomic indicators.Therefore,this paper starts from the macroeconomic indicators,combining relevant literature analysis and logistics demand related factors to obtain the indicator set for forecasting urban logistics demand through grey correlation analysis,and then analyzes the principle and characteristics of Arima time series model and BP neural network.Based on above,the Arima-BP combined forecasting model is formed,taking Tianjin as an example to verify the model and forecast the trend of logistics demand in Tianjin,and then according to the forecast results and the status quo of logistics and supporting industries,the countermeasures and suggestions for urban logistics development are proposed.The details are as follows:Firstly,taking the urban logistics demand as the research object,this paper puts forward the problems to be studied in this paper,expounds the importance of the research,and points out the shortcomings of the existing research,which leads to the research content of this paper and draws the technical roadmap.Based on the above research,the content elaborates the related concepts and existing research results,and provides theoretical basis for the research.Then,according to the prediction principle and model,the modeling and simulation ideas based on Arima-BP are proposed.Through the literature,the influencing factors of analyzing logistics demand are compiled,and the grey relational analysis is used to establish the logistics demand forecasting index system.Combining the advantages of Arima time series model and BP neural network model in processing time series data and learning algorithm,the idea of Arima-BP combined forecasting model are proposed,which can better predict urban logistics demand and provide theory for this study.Finally,use Matlab simulation software to design and implement the forecasting model,the case analysis further validates the effect of the combined forecasting model.On this basis,it predicts the changing trend of urban logistics demand in Tianjin,and combines the current development of logistics industry in Tianjin to find out the gaps and shortcomings in dealing with the rising logistics demand,and give corresponding countermeasures and suggestions.The results show that the prediction results of the combined model have improved significantly in the prediction accuracy,and the feasibility and effectiveness of the model have been verified.On the empirical side,the logistics demand in Tianjin has shown a steady upward trend,which can be considered from highways,railways and so on to improve the city's transportation capacity,and match it with the urban logistics needs,so as to truly realize the value of urban logistics.