The Short-term Traffic Flow Prediction Of Urban Traffic For Industry Logistics Management

Liuzhou is the important regional center city and the largest industrial economiccenter in Guangxi Province，the major industrial enterprises are all over the city,Industry logistics, transportation, post and tourism logistics business volume has grownrapidly, and provide a vast space for the development of logistics industry, At the sametime the city traffic problems are brought.Traffic volume forecasting for industry logistics demand can provides an importantbasis for formulating the policy of the logistics and logistics planning, and industryInternet of Things planning in the city of Liuzhou. Especially at present the problemssuch as traffic congestion, environmental disturbance, traffic accidents in the urbanarea become more, the problems plagues the development of local economy andseriously hinders the people to improve the quality of life and the logistics flow, thathas become one of the main problems in industry logistics management design. so thatthe prediction of short-term traffic flow. The city area is crucial in logistics demandforecasting and has the vital practical significance. Real-time, accurate short-termtraffic flow prediction is helpful to improve the traffic congestion situation, optimizethe use of the road network, provide the basis for management system project, realizethe logistics flow without obstruction, improve the efficiency and safety of logistics,reduce the logistics energy consumption. Therefore， how to accurately predictshort-term traffic volume becomes a hot spot of research.The paper mainly studies the method of combination forecast, and analyses someexamples, including the following: The first of all, research data about the internet ofthings, logistics, intelligent transportation systems and the short-term traffic flowprediction at home and abroad is refered, then the background, practical significanceand the domestic and foreign research situation of the short-time traffic flow predictionare related,with a single crossroads traffic flow forecasting in Liuzhou as an example,the prediction scheme and technical route is formulated. In addition，the text introducesthe basic theoretical knowledge of the logistics and traffic flow, analyzes maincharacteristics of traffic flow, and gives a brief overview about the current predictionmethods commonly used. Finally, combined with the advantage of autoregressivemoving average model (ARIMA) and RBF neural network, the combined model that iscombined with ARIMA and RBF is proposed and used to short—term traffic flowforecasting in the city of Liuzhou. At the same time, the paper set up two kinds ofsingle forecasting model: RBF neural network model and the ARIMA model. Theprediction results is obtained through application example, simulation experiment iscarried out to the measured data using the combination model and single model. Thepredicted results show that, the results of combination forecasting model is moreaccurate than the single model, so the combination forecasting model is suitable forreal-time traffic volume forecast，and the research content and result has practicalguidance for industry logistics forecast research and Internet of Things planningmanagement design.