Forecasting Short-Term Freight Transportation Demand: Poisson STARMA Model

1998 ◽  
Vol 1645 (1) ◽  
pp. 8-16 ◽  
Author(s):  
Rodrigo A. Garrido ◽  
Hani S. Mahmassani
Keyword(s):  
Econometric Model ◽  
Short Term ◽  
Transportation Demand ◽  
Modeling Methodology ◽  
Diagnostic Checking ◽  
Proposed Model ◽  
Stochastic Framework ◽  
Freight Transportation Demand ◽  
Short Term Forecasting ◽  
Spatial And Temporal Characteristics

A framework for analyzing, describing, and forecasting freight flows for operational and tactical purposes is presented. A dynamic econometric model is proposed. This model incorporates the spatial and temporal characteristics of freight demand within a stochastic framework. The model was applied in an actual context and its performance was compared with standard time series models (benchmark) for forecasting ability. The proposed model outperformed the benchmark from the econometric viewpoint. Extensive diagnostic checking and sensitivity analysis confirmed the robustness of the modeling methodology for short-term forecasting applications.

scholarly journals A Hybrid Short-Term Forecasting Model of Passenger Flow on High-Speed Rail considering the Impact of Train Service Frequency

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Qingying Lai ◽  
Jun Liu ◽  
Yongji Luo ◽  
Minshu Ma
Keyword(s):  
Hybrid Model ◽  
Traffic Management ◽  
High Speed ◽  
Short Term ◽  
High Speed Rail ◽  
Passenger Flow ◽  
Proposed Model ◽  
Service Frequency ◽  
Short Term Forecasting ◽  
The Impact

Short-term forecasting of OD (origin to destination) passenger flow on high-speed rail (HSR) is one of the critical tasks in rail traffic management. This paper proposes a hybrid model to explore the impact of the train service frequency (TSF) of the HSR on the passenger flow. The model is composed of two parts. One is the Holt-Winters model, which takes advantage of time series characteristics of passenger flow. The other part considers the changes of TSF for the OD in different time during a day. The two models are integrated by the minimum absolute value method to generate the final hybrid model. The operational data of Beijing-Shanghai high-speed railway from 2012 to 2016 are used to verify the effectiveness of the model. In addition to the forecasting ability, with a definite formation, the proposed model can be further used to forecast the effects of the TSF.

scholarly journals A Deep Neural Network Based on Classification of Traffic Volume for Short-Term Forecasting

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Jing Bai ◽  
Yehua Chen
Keyword(s):  
Traffic Flow ◽  
Urban Traffic ◽  
Fine Tuning ◽  
Traffic Prediction ◽  
Short Term ◽  
The Road ◽  
Deep Architecture ◽  
Proposed Model ◽  
Short Term Forecasting

This paper developed a deep architecture to predict the short-term traffic flow in an urban traffic network. The architecture consists of three main modules: a pretraining module, which generates initialized weights and provides a rough learning of the features firstly with the training set in an unsupervised manner; a classification module, which performs the data classification operation through adding the logistic regression on top of the pretrained architecture to distinguish the traffic state; and a fine-tuning module, which predicts the traffic flow with supervised training based on the initialized weights in the first module. The classification module provides the fine-tuning modules with two classified datasets for more accurate forecasting. Furthermore, both upstream and downstream data are utilized to improve the prediction performance. The effectiveness of the proposed model was verified by the traffic prediction of the road segments of Nanming District of Guiyang. And with the comparison analysis over the existing approaches, the proposed model shows superiority in short-term traffic prediction, especially under incident conditions.

scholarly journals CORRELATION-REGRESSION ANALYSIS OF THE COMPETITIVENESS OF ENTERPRISE PRODUCTS

2020 ◽  
Vol 10 (86) ◽  
Author(s):  
Volodymyr Ulanchuk ◽  
◽  
Olena Zharun ◽  
Serhiy Sokolyuk ◽  
◽  
...  
Keyword(s):  
Mathematical Model ◽  
Regression Analysis ◽  
Correlation Equation ◽  
Regression Equation ◽  
Short Term ◽  
Flexible Tool ◽  
Proposed Model ◽  
Using Data ◽  
Short Term Forecasting ◽  
The Impact

The economic purpose of correlation-regression analysis is to determine the possible options for product competitiveness management, as well as an assessment of possible ways to achieve the desired result. The developed model can be used to improve planning and increase the level of product competitiveness. The forecast of results, though for the short term, gives the chance to learn about the prospects of obtaining the appropriate level of competitiveness of products in accordance with the degree of application of the impact on it. The forecast is dynamic and adapts to changes based on the latest data. The proposed model can be integrated into the existing decision support system to increase the competitiveness of products. In addition, correlation-regression analysis makes it possible to estimate the current situation using a regression equation. The mathematical reflection of the study of product competitiveness is the economic-mathematical model, which determines its functioning and assessment of changes in its effectiveness in the event of possible changes in the characteristics of economic activity. The parameters of economic models are estimated using the methods of mathematical statistics according to real statistical information. The task of correlation-regression analysis is to construct and analysis of the economic-mathematical model of the regression equation (correlation equation, which reflects the dependence of the resultant feature on several factor features and gives an estimate of the degree of connection density. Using data on the magnitude and direction of action of the analyzed factors, you can get the data that can be obtained to assess the relevant impact on the current level of product competitiveness. That is, such an analysis is a powerful and flexible tool for studying the relationships between product competitiveness indicators. The use of this method makes it possible to better understanding of the level of influence of factors on the competitiveness of products, and, accordingly, learn to manage the processes that take place, as well as more accurately predict their further interaction. These studies are important for the formation and implementation of management decisions to increase the competitiveness of products, because it narrows the choice of indicators with the greatest impact on its level. The ability to determine short-term forecasting of such impacts makes it possible to determine regional perspectives under the conditions of implemented measures.

scholarly journals A New Hybrid Ensemble Deep Learning Model for Train Axle Temperature Short Term Forecasting

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 312
Author(s):  
Guangxi Yan ◽  
Chengqing Yu ◽  
Yu Bai
Keyword(s):  
Short Term Memory ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Hybrid Approach ◽  
Operating Conditions ◽  
Short Term ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Temperature Forecasting ◽  
Short Term Forecasting

The axle temperature is an index factor of the train operating conditions. The axle temperature forecasting technology is very meaningful in condition monitoring and fault diagnosis to realize early warning and to prevent accidents. In this study, a data-driven hybrid approach consisting of three steps is utilized for the prediction of locomotive axle temperatures. In stage I, the Complementary empirical mode decomposition (CEEMD) method is applied for preprocessing of datasets. In stage II, the Bi-directional long short-term memory (BILSTM) will be conducted for the prediction of subseries. In stage III, the Particle swarm optimization and gravitational search algorithm (PSOGSA) can optimize and ensemble the weights of the objective function, and combine them to achieve the final forecasting. Each part of the combined structure contributes its functions to achieve better prediction accuracy than single models, the verification processes of which are conducted in the three measured datasets for forecasting experiments. The comparative experiments are chosen to test the performance of the proposed model. A sensitive analysis of the hybrid model is also conducted to test its robustness and stability. The results prove that the proposed model can obtain the best prediction results with fewer errors between the comparative models and effectively represent the changing trend in axle temperature.

scholarly journals Real Time Short-term Forecasting Method of Remaining Parking Space in Urban Parking Guidance Systems

2018 ◽  
Vol 30 (2) ◽  
pp. 173-185 ◽  
Author(s):  
Xiaobo Zhu ◽  
Jianhua Guo ◽  
Wei Huang ◽  
Fengquan Yu ◽  
Byungkyu Brian Park
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Real Time ◽  
Parking Space ◽  
Short Term ◽  
Markov Chain Method ◽  
Proposed Model ◽  
Forecasting Method ◽  
Short Term Forecasting ◽  
Urban Parking

Short-term forecasting of the remaining parking space is important for urban parking guidance systems (PGS). The previous methods like polynomial equations and neural network methods are difficult to be applied in practice because of low accuracy or lengthy initial training time which is unfavourable if real-time training is carried out on adapting to changing traffic conditions. To forecast the remaining parking space in real-time with higher accuracy and improve the performances of PGS, this study develops an online forecasting model based on a time series method. By analysing the characteristics of data collected in Nanjing, China, an autoregressive integrated moving average (ARIMA) model has been established and a real-time forecasting procedure developed. The performance of this proposed model has been further analysed and compared with the performances of a neural network method and the Markov chain method. The results indicate that the mean error of the proposed model is about 2 vehicles per 15 minutes, which can meet the requirements for general PGS. Furthermore, this method outperforms the neural network model and the Markov chain method both in individual and collective error analysis. In summary, the proposed online forecasting method appears to be promising for forecasting the remaining parking space in supporting the PGS.

The causes of poverty in Russia

2020 ◽  
Vol 13 (1) ◽  
pp. 21-36
Author(s):  
I.S. Ivanchenko
Keyword(s):  
Regression Analysis ◽  
Regional Development ◽  
Financial Market ◽  
Russian Federation ◽  
Short Term ◽  
Financial Situation ◽  
Macroeconomic Variables ◽  
Development Plans ◽  
The Russian Federation ◽  
Short Term Forecasting

Subject. This article analyzes the changes in poverty of the population of the Russian Federation. Objectives. The article aims to identify macroeconomic variables that will have the most effective impact on reducing poverty in Russia. Methods. For the study, I used the methods of logical, comparative, and statistical analyses. Results. The article presents a list of macroeconomic variables that, according to Western scholars, can influence the incomes of the poorest stratum of society and the number of unemployed in the country. The regression analysis based on the selected variables reveals those ones that have a statistically significant impact on the financial situation of the Russian poor. Relevance. The results obtained can be used by the financial market mega-regulator to make anti-poverty decisions. In addition, the models built can be useful to the executive authorities at various levels for short-term forecasting of the number of unemployed and their income in drawing up regional development plans for the areas.

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