A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation

Directing against the shortcoming of low accuracy in short-term traffic flow prediction caused by strong traffic flow fluctuation, a novel method for short-term traffic forecasting based on the combination of improved grey Verhulst prediction algorithm and first-order difference exponential smoothing is proposed. Firstly, we constructed an improved grey Verhulst prediction model by introducing the Markov chain to its traditional version. Then, based on an introduced dynamic weighting factor, the improved grey Verhulst prediction method, and the first-order difference exponential smoothing technique, the new method for short-term traffic forecasting is completed in an efficient way. Finally, experiment and analysis are carried out in the light of actual data gathered from strong fluctuation environment to verify the effectiveness and rationality of our proposed scheme

The new keynesian approach to dynamic general equilibrium modeling: models, methods, and macroeconomic policy evaluation

This chapter aims to provide a hands-on approach to New Keynesian models and their uses for macroeconomic policy analysis. It starts by reviewing the origins of the New Keynesian approach, the key model ingredients and representative models. Building blocks of current-generation dynamic stochastic general equilibrium (DSGE) models are discussed in detail. These models address the famous Lucas critique by deriving behavioral equations systematically from the optimizing and forward-looking decision-making of households and firms subject to well-defined constraints. State-of-the-art methods for solving and estimating such models are reviewed and presented in examples. The chapter goes beyond the mere presentation of the most popular benchmark model by providing a framework for model comparison along with a database that includes a wide variety of macroeconomic models. Thus, it offers a convenient approach for comparing new models to available benchmarks and for investigating whether particular policy recommendations are robust to model uncertainty. Such robustness analysis is illustrated by evaluating the performance of simple monetary policy rules across a range of recently-estimated models including some with financial market imperfections and by reviewing recent comparative findings regarding the magnitude of government spending multipliers. The chapter concludes with a discussion of important objectives for on-going and future research using the New Keynesian framework

Development of a Comprehensive 0-1D Powertrain and Vehicle Model for the Analysis of an Innovative 48 V Mild-Hybrid Diesel Passenger Car

L'abstract è presente nell'allegato / the abstract is in the attachmen

Forecasting Petroleum Products Consumption in the Chadian Road Transport Sector using Optimised Grey Models

This study aims to estimate the demand for petroleum products (PP) in the Chadian road sector by 2030 and to determine which of the two models used is the most efficient. The methodology is based on two optimised Grey models, namely: the Sequential-GM(1,N)-GA and NeuralODE-GM(1,1) models.  These models reduce forecasting errors compared with the conventional Grey model. The forecasts confirm that both models are robust, with MAPEs of 1.16% and 2.5% respectively for gasoline and diesel obtained with the Sequential-GM(1,N)-GA, and 3.3% and 4.8% respectively for gasoline and diesel obtained with the NeuralODE-GM(1,1). We note that the Sequential-GM(1,N)-GA is more robust than NeuralODE-GM(1,1) with regard to MAPEs. The estimated consumption needs for gasoline and diesel in the road transport sector by 2030 are 294376818.5 and 381570061.5 litres respectively for the Sequential-GM(1,N)-GA and 264376818.5 and 375570061.5 litres for the NeuralODE-GM(1,1). Based on these results, securing the supply of PP in the road transport sector requires the development of the downstream petroleum sector. The development of alternative energies and the acquisition of hybrid vehicles. A policy encouraging mass transport in urban areas can considerably reduce energy consumption in this sector. This study adds to the literature through the simultaneous use of two new optimised grey models and their comparison in terms of predicting demand for PP in the Chadian road transport sector

Roving vehicle motion control Quarterly report, 1 Mar. - 31 May 1967

System and subsystem requirements for remote control of roving space vehicle motio

Forecasting smog in Beijing using a novel time-lag GM (1, N) model based on interval grey number sequences

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Purpose Smog seriously affects the ecological environment and poses a threat to public health. Therefore, smog control has become a key task in China, which requires reliable prediction. Design/methodology/approach This paper establishes a novel time-lag GM(1,N) model based on interval grey number sequences. Firstly, calculating kernel and degree of greyness of the interval grey number sequence respectively. Then, establishing the time-lag GM(1,N) model of kernel and degree of greyness sequences respectively to obtain their values after determining the time-lag parameters of two models. Finally, the upper and lower bounds of interval grey number sequences are obtained by restoring the values of kernel and degree of greyness. Findings In order to verify the validity and practicability of the model, the monthly concentrations of PM2.5, SO2 and NO2 in Beijing during August 2017 to September 2018 are selected to establish the time-lag GM(1,3) model for kernel and degree of greyness sequences respectively. Compared with three existing models, the proposed model in this paper has better simulation accuracy. Therefore, the novel model is applied to forecast monthly PM2.5 concentration for October to December 2018 in Beijing and provides a reference basis for the government to formulate smog control policies. Practical implications The proposed model can simulate and forecast system characteristic data with the time-lag effect more accurately, which shows that the time-lag GM(1,N) model proposed in this paper is practical and effective. Originality/value Based on interval grey number sequences, the traditional GM(1,N) model neglects the time-lag effect of driving terms, hence this paper introduces the time-lag parameters into driving terms of the traditional GM(1,N) model and proposes a novel time-lag GM(1,N) model

Data Challenges and Data Analytics Solutions for Power Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The electricity consumption forecasting problem is especially important for policy making in developing region. To properly formulate policies, it is necessary to have reliable forecasts. Electricity consumption forecasting is influenced by some factors, such as economic, population and so on. Considering all factors is a difficult task since it requires much detailed study in which many factors significantly influence on electricity forecasting whereas too many data are unavailable. Grey convex relational analysis is used to describe the relationship between the electricity consumption and its related factors. A novel multi-variable grey forecasting model which considered the total population is developed to forecast the electricity consumption in Shandong Province. The GMC(1,N) model with fractional order accumulation is optimized by changing the order number and the effectiveness of the first pair of original data by the model is proven. The results of practical numerical examples demonstrate that the model provides remarkable prediction performances compared with the traditional grey forecasting model. The forecasted results showed that the increase of electricity consumption will speed up in Shandong Province

The Ecology of Fecal Indicators

Animal and human wastes introduce pathogens into rivers and streams, creating human health and economic burdens. While direct monitoring for pathogens is possible, it is impractical due to the sporadic distribution of pathogens, cost to identify, and health risks to laboratory workers. To overcome these issues, fecal indicator organisms are used to estimate the presence of pathogens. Although fecal indicators generally protect public health, they fall short in their utility because of difficulties in public health risk characterization, inconsistent correlations with pathogens, weak source identification, and their potential to persist in environments with no point sources of fecal pollution. This research focuses on characterizing the ecology of fecal indicators using both modeling and metabolic indicators to better understand the processes that drive fecal pollution. Fecal indicator impairment was modeled in Sinking Creek, a 303 (d) listed stream in Northeast Tennessee, using the ecological niche model, Maxent, for two different fecal indicators. While the use of Maxent has been well demonstrated at the macroscale, this study introduces its application to ecological niches at the microscale. Stream impairment seasonality was exhibited in two different indicators over multiple years and different resolutions (quarterly versus monthly sampling programs). This stresses the need for multiple year and month sampling to capture heterogeneity in fecal indicator concentrations. Although discharge is strongly associated with dissolved solutes, fecal indicator impairment was governed by other ecological factors such as populations of heterotrophic bacteria, enzyme activity, nutrient conditions, and other metabolic indicators. This research also incorporated metabolic indicators to characterize spatiotemporal variability in microbial community function, making connections to fecal and other pollution gradients. Communities differed in their ability to use a wide variety of substrates, and metabolic inhibition in sediments captured most of the interaction of aquatic and benthic communities. Sediment substrate activity was also indicative of degrees of pollution, suggesting that sediment is a potential reservoir for Escherichia coli in this stream, and there is possibility for resuspension, extended residence times, and increased duration for exposure. This research highlights the benefit of using models and other microbial indicators to better understand how environment shapes the niche of fecal indicators

Maxent Estimation of Aquatic Escherichia Coli Stream Impairment

Background.The leading cause of surface water impairment in United States’ rivers and streams is pathogen contamination. Although use of fecal indicators has reduced human health risk, current approaches to identify and reduce exposure can be improved. One important knowledge gap within exposure assessment is characterization of complex fate and transport processes of fecal pollution. Novel modeling processes can inform watershed decision-making to improve exposure assessment