Analysis and Forecast of Railway Freight Volume based on Prophet-Deep AR Model

The research on railway freight volume forecast is of great significance to the allocation of railway freight transport resources, the formulation of transport plans and the organization of railway freight transport. This study, by fully mining the railway freight ticket data information, put forward the precise forecast model of railway freight volume under different types of freight. Firstly, the railway freight ticket data are cleaned, regulated and integrated, and the time series of the daily number of railway freight trains are constructed, get the trend, periodicity and holiday data of railway traffic data, and set the parameters of Chinese holidays and rest days according to the demand characteristics of different categories. Secondly, the forecasting result of the Prophet is taken as a cooperative parameter. DeepAR is used to forecast, and a combined model of the Prophet-DeepAR is constructed. Finally, the combined model was validated with Shanghai Railway Bureau data from January 1, 2015 to December 31, 2018 for the food and tobacco category, and with Prophet-DeepAR, LSTM, Wavelet, BILSTM, and Prophet-LSTM, prophet-wavelet and Prophet-Bilstm are used to compare the prediction results. The results show that the Prophet-DeepAR model can extract the multi-dimensional periodicity of freight traffic and mine the trend information of freight traffic, and get the prediction result with high precision. It has better accuracy than other models

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Traffic Volume Forecasting Model of Freeway Toll Stations During Holidays – An SVM Model

Support vector machine (SVM) models have good performance in predicting daily traffic volume at toll stations, however, they cannot accurately predict holiday traffic volume. Therefore, an improved SVM model is proposed in this paper. The paper takes a toll station in Heilongjiang, China as an example, and uses the daily traffic volume as the learning set. The current and previous 7-day traffic volumes are used as the dependent and independent variables for model learning, respectively. This paper found that the basic SVM model is not accurate enough to forecast the traffic volume during holidays. To improve the model accuracy, this paper first used the SVM model to forecast non-holiday traffic volumes, and proposed a prediction method using quarterly conversion coefficients combined with the SVM model to construct an improved SVM model. The result of the prediction showed that the improved SVM model in this paper was able to effectively improve accuracy, making it better than in the basic SVM and GBDT model, thus proving the feasibility of the improved SVM model