A Model Tree-Based Vehicle Emission Model at Freeway Toll Plazas

With the increased concern over sustainable development, many efforts have been made to alleviate air quality deterioration. Freeway toll plazas can cause serious pollution, due to the increased emissions caused by stop-and-go operations. Different toll collections and different fuel types obviously influence the vehicle emissions at freeway toll plazas. Therefore, this paper proposes a model tree-based vehicle emission model by considering these factors. On-road emissions data and vehicle operation data were obtained from two different freeway toll plazas. The statistical analysis indicates that different methods of toll collection and fuel types have significant impacts on vehicle emissions at freeway toll plazas. The performance of the proposed model was compared with a polynomial regression method. Based on the results, the mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE) of the proposed model were all smaller, while the R-squared value increased from 0.714 to 0.833. Finally, the variations of vehicle emissions at different locations of freeway toll plazas were calculated and shown in heat maps. The results of this study can help better estimate the vehicle emissions and give advice to the development of electronic toll collection (ETC) lanes and relevant policies at freeway toll plazas.

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Forecasting Brazilian Ethanol Spot Prices Using LSTM

Energies ◽

10.3390/en14237987 ◽

2021 ◽

Vol 14 (23) ◽

pp. 7987

Author(s):

Gustavo Carvalho Santos ◽

Flavio Barboza ◽

Antônio Cláudio Paschoarelli Veiga ◽

Mateus Ferreira Silva

Keyword(s):

Short Term Memory ◽

Mean Squared Error ◽

Percentage Error ◽

Spot Prices ◽

Statistical Measures ◽

Squared Error ◽

Proposed Model ◽

Price Trends ◽

Long Short Term Memory ◽

Crisis Scenarios

Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patterns in crisis scenarios.

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A hybrid model based on mBA-ANFIS for COVID 19 confirmed cases prediction and forecast

10.21203/rs.3.rs-83375/v1 ◽

2020 ◽

Author(s):

Sohail Saif ◽

Priya Das ◽

Suparna Biswas

Keyword(s):

Mean Squared Error ◽

Fuzzy Inference ◽

Absolute Error ◽

Bees Algorithm ◽

Percentage Error ◽

Anfis Model ◽

Inference System ◽

Neuro Fuzzy ◽

Proposed Model ◽

Hybrid Forecasting

Abstract In India, the first confirmed case of novel corona virus (COVID-19) was discovered on 30 January, 2020. The number of confirmed cases is increasing day by day and it crossed 21,53,010 on 09 August, 2020. In this paper a hybrid forecasting model has been proposed to determine the number of confirmed cases for upcoming 10 days based on the earlier confirmed cases found in India. The proposed modelis based on adaptive neuro-fuzzy inference system (ANFIS) and mutation based Bees Algorithm (mBA). ThemetaheuristicBees Algorithm (BA) has been modified applying 4 types of mutation and Mutation based Bees Algorithm (mBA) is applied to enhance the performance of ANFIS by optimizing its parameters. Proposed mBA-ANFIS model has been assessed using COVID-19 outbreak dataset for India and USAand the number of confirmed cases in next 10 days in Indiahas been forecasted. Proposed mBA-ANFIS model has been compared to standard ANFIS model as well as other hybrid models such as GA-ANFIS, DE-ANFIS, HS-ANFIS, TLBO-ANFIS, FF-ANFIS, PSO-ANFIS and BA-ANFIS. All these models have been implemented using Matlab 2015 with 10 iterations each. Experimental results showthat the proposed model has achieved better performance in terms of Root Mean squared error (RMSE), Mean Absolute Percentage Error (MAPE), Mean absolute error (MAE) and Normalized Root Mean Square Error (NRMSE).It has obtained RMSE of 1280.24, MAE of 685.68, MAPE of 6.24 and NRMSE of 0.000673 for India Data.Similarly, for USA the values are 4468.72, 3082.07, 6.1, 0.000952 for RMSE, MAE, MAPE, NRMSE respectively.

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Motor Vehicle Emission Modeling and Software Simulation Computing for Roundabout in Urban City

Mathematical Problems in Engineering ◽

10.1155/2013/312396 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12

Author(s):

Haiwei Wang ◽

Huiying Wen ◽

Feng You ◽

Jianmin Xu ◽

Hailin Kui

Keyword(s):

Vehicle Emissions ◽

Motor Vehicle ◽

Road Traffic ◽

Vehicle Emission ◽

Signal Control ◽

Emission Model ◽

Emission Rates ◽

Integration Model ◽

Urban City ◽

Total Vehicle

In urban road traffic systems, roundabout is considered as one of the core traffic bottlenecks, which are also a core impact of vehicle emission and city environment. In this paper, we proposed a transport control and management method for solving traffic jam and reducing emission in roundabout. The platform of motor vehicle testing system and VSP-based emission model was established firstly. By using the topology chart of the roundabout and microsimulation software, we calculated the instantaneous emission rates of different vehicle and total vehicle emissions. We argued that Integration-Model, combing traffic simulation and vehicle emission, can be performed to calculate the instantaneous emission rates of different vehicle and total vehicle emissions at the roundabout. By contrasting the exhaust emissions result between no signal control and signal control in this area at the rush hour, it draws a conclusion that setting the optimizing signal control can effectively reduce the regional vehicle emission. The proposed approach has been submitted to a simulation and experiment that involved an environmental assessment in Satellite Square, a roundabout in medium city located in China. It has been verified that setting signal control with knowledge engineering and Integration-Model is a practical way for solving the traffic jams and environmental pollution.

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Traffic Congestion and Infant Health: Evidence from E-ZPass

American Economic Journal Applied Economics ◽

10.1257/app.3.1.65 ◽

2011 ◽

Vol 3 (1) ◽

pp. 65-90 ◽

Cited By ~ 152

Author(s):

Janet Currie ◽

Reed Walker

Keyword(s):

Birth Weight ◽

Low Birth Weight ◽

Traffic Congestion ◽

Vehicle Emissions ◽

Infant Health ◽

Housing Prices ◽

Electronic Toll Collection ◽

Toll Plazas ◽

Toll Collection ◽

Toll Plaza

We exploit the introduction of electronic toll collection, (E-ZPass), which greatly reduced both traffic congestion and vehicle emissions near highway toll plazas. We show that the introduction of E-ZPass reduced prematurity and low birth weight among mothers within 2 kilometers (km) of a toll plaza by 10.8 percent and 11.8 percent, respectively, relative to mothers 2–10 km from a toll plaza. There were no immediate changes in the characteristics of mothers or in housing prices near toll plazas that could explain these changes. The results are robust to many changes in specification and suggest that traffic congestion contributes significantly to poor health among infants. (JEL I12, J13, Q51, Q53, R41)

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Short-Term Energy Forecasting Using Machine-Learning-Based Ensemble Voting Regression

Symmetry ◽

10.3390/sym14010160 ◽

2022 ◽

Vol 14 (1) ◽

pp. 160

Author(s):

Pyae-Pyae Phyo ◽

Yung-Cheol Byun ◽

Namje Park

Keyword(s):

Machine Learning ◽

Mean Squared Error ◽

Weather Data ◽

Gradient Boosting ◽

Percentage Error ◽

Short Term ◽

Energy Forecasting ◽

Light Gradient ◽

Proposed Model ◽

Term Energy

Meeting the required amount of energy between supply and demand is indispensable for energy manufacturers. Accordingly, electric industries have paid attention to short-term energy forecasting to assist their management system. This paper firstly compares multiple machine learning (ML) regressors during the training process. Five best ML algorithms, such as extra trees regressor (ETR), random forest regressor (RFR), light gradient boosting machine (LGBM), gradient boosting regressor (GBR), and K neighbors regressor (KNN) are trained to build our proposed voting regressor (VR) model. Final predictions are performed using the proposed ensemble VR and compared with five selected ML benchmark models. Statistical autoregressive moving average (ARIMA) is also compared with the proposed model to reveal results. For the experiments, usage energy and weather data are gathered from four regions of Jeju Island. Error measurements, including mean absolute percentage error (MAPE), mean absolute error (MAE), and mean squared error (MSE) are computed to evaluate the forecasting performance. Our proposed model outperforms six baseline models in terms of the result comparison, giving a minimum MAPE of 0.845% on the whole test set. This improved performance shows that our approach is promising for symmetrical forecasting using time series energy data in the power system sector.

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Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia

Water Science & Technology Water Supply ◽

10.2166/ws.2021.391 ◽

2021 ◽

Author(s):

Demeke Endalie ◽

Getamesay Haile ◽

Wondmagegn Taye

Keyword(s):

Agricultural Sector ◽

Short Term Memory ◽

Mean Squared Error ◽

Daily Rainfall ◽

Weather Conditions ◽

Percentage Error ◽

Support Vector ◽

Rainfall Forecasting ◽

Rainfall Prediction ◽

Proposed Model

Abstract Rainfall prediction is a critical task because many people rely on it, particularly in the agricultural sector. Rainfall forecasting is difficult due to the ever-changing nature of weather conditions. In this study, we carry out a rainfall predictive model for Jimma, a region located in southwestern Oromia, Ethiopia. We proposed a Long Short-Term Memory (LSTM)-based prediction model capable of forecasting Jimma's daily rainfall. Experiments were conducted to evaluate the proposed models using various metrics such as Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) Nash-Sutcliffe model efficiency (NSE), and R2, and the results were 0.01, 0.4786 0.81 and 0.9972, respectively. We also compared the proposed model to existing machine learning regressions like Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT). The RMSE of MLP was the lowest of the four existing learning models i.e., 0.03. The proposed LSTM model outperforms the existing models, with an RMSE of 0.01. The experimental results show that the proposed model has a lower RMSE and a higher R2.

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A NOx Emission Model Incorporating Temperature for Heavy-Duty Diesel Vehicles with Urea-SCR Systems Based on Field Operating Modes

Atmosphere ◽

10.3390/atmos10060337 ◽

2019 ◽

Vol 10 (6) ◽

pp. 337 ◽

Cited By ~ 2

Author(s):

Xin Wang ◽

Guohua Song ◽

Yizheng Wu ◽

Lei Yu ◽

Zhiqiang Zhai

Keyword(s):

Vehicle Emission ◽

Nox Emissions ◽

Emission Model ◽

Heavy Duty ◽

Operating Modes ◽

Diesel Vehicles ◽

Nox Conversion ◽

Proposed Model ◽

Heavy Duty Diesel ◽

Scr System

The selective catalytic reduction (SCR) is the most commonly used technique for decreasing the emissions of nitrogen oxides (NOx) from heavy-duty diesel vehicles (HDDVs). However, the same injection strategy in the SCR system shows significant variations in NOx emissions even at the same operating mode. This kind of heterogeneity poses challenges to the development of emission inventories and to the assessment of emission reductions. Existing studies indicate that these differences are related to the exhaust temperature. In this study, an emission model is established for different source types of HDDVs based on the real-time data of operating modes. Firstly, the initial NOx emission rates (ERs) model is established using the field vehicle emission data. Secondly, a temperature model of the vehicle exhaust based on the vehicle specific power (VSP) and the heat loss coefficient is established by analyzing the influencing factors of the NOx conversion efficiency. Thirdly, the models of NOx emissions and the urea consumption are developed based on the chemical reaction in the SCR system. Finally, the NOx emissions are compared with the real-world emissions and the estimations by the proposed model and the Motor Vehicle Emission Simulator (MOVES). This indicates that the relative error by the proposed method is 12.5% lower than those calculated by MOVES. The characteristics of NOx emissions under different operating modes are analyzed through the proposed model. The results indicate that the NOx conversion rate of heavy-duty diesel trucks (HDDTs) is 39.2% higher than that of urban diesel transit buses (UDTBs).

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Dual Stage Bayesian Network with Dual-Tree Complex Wavelet Transformation for Image Denoising

Journal of Engineering Research ◽

10.36909/jer.v8i1.6043 ◽

2020 ◽

Vol 8 (1) ◽

Author(s):

Venkata Lavanya P. ◽

Venkata Narasimhulu C. ◽

Satya Prasad K.

Keyword(s):

Mean Squared Error ◽

Structural Similarity ◽

Absolute Error ◽

Noise Spectrum ◽

Percentage Error ◽

Wavelet Coefficients ◽

Complex Wavelet Transform ◽

Proposed Model ◽

Complex Wavelet ◽

Image Type

Image de-noising always plays a vital role in various engineering bids. Moreover, in image processing technology, image de-noising statistics is persisted as a substantial dispute. Over the past decades, certain de-noising methods have been exposed incredible accomplishments. This paper intends to develop a de-noising algorithm for multimodal and heterogeneous images, while the conventional de-noising algorithms handle a specific image type. The filtered information is reversed to spatial domain to recover the de-noised image. Dual tree Complex Wavelet Transform (DT-CWT) is exploited for image transformation for which the wavelet coefficients are estimated using Bayesian Regularization (BR). To ensure the de-noising performance for heterogeneous images, the statistical and wavelet features are extracted. Subsequently, the image characteristics are combined with noise spectrum to develop BR model, which estimates the wavelet coefficients for effective de-noising. Hence, the proposed de-noising algorithm exploits two stages of BR. The first stage predicts the image type, whereas the second stage estimates appropriate wavelet coefficients to DT-CWT for de-noising. The performance of the proposed model is analysed in terms of Peak Signal to Noise Ratio (PSNR), Second derivative Measure of Enhancement (SDME), Structural Similarity (SSIM), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson Coefficient (PC), and Symmetric Mean Absolute Percentage Error (SMAPE) respectively. The proposed model is compared to the conventional models, and the significance of the developed model is clearly described.

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