Vehicles CO2 emission reduction based on real time traffic flow conditions

2015 ◽  
pp. 1065-1070
Author(s):  
Aiping Ni ◽  
Chunxiao Li ◽  
Jie Ding
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Emission Reduction ◽  
Co2 Emission ◽  
Flow Conditions ◽  
Co2 Emission Reduction ◽  
Real Time Traffic

scholarly journals Pulp and Paper Industry: Decarbonisation Technology Assessment to Reach CO2 Neutral Emissions—An Austrian Case Study

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1161
Author(s):  
Maedeh Rahnama Mobarakeh ◽  
Miguel Santos Silva ◽  
Thomas Kienberger
Keyword(s):  
Energy Consumption ◽  
Co2 Emissions ◽  
Emission Reduction ◽  
Co2 Emission ◽  
Manufacturing Industry ◽  
Paper Industry ◽  
Heat Pumps ◽  
Pulp And Paper ◽  
Low Carbon ◽  
Co2 Emission Reduction

The pulp and paper (P&P) sector is a dynamic manufacturing industry and plays an essential role in the Austrian economy. However, the sector, which consumes about 20 TWh of final energy, is responsible for 7% of Austria’s industrial CO2 emissions. This study, intending to assess the potential for improving energy efficiency and reducing emissions in the Austrian context in the P&P sector, uses a bottom-up approach model. The model is applied to analyze the energy consumption (heat and electricity) and CO2 emissions in the main processes, related to the P&P production from virgin or recycled fibers. Afterward, technological options to reduce energy consumption and fossil CO2 emissions for P&P production are investigated, and various low-carbon technologies are applied to the model. For each of the selected technologies, the potential of emission reduction and energy savings up to 2050 is estimated. Finally, a series of low-carbon technology-based scenarios are developed and evaluated. These scenarios’ content is based on the improvement potential associated with the various processes of different paper grades. The results reveal that the investigated technologies applied in the production process (chemical pulping and paper drying) have a minor impact on CO2 emission reduction (maximum 10% due to applying an impulse dryer). In contrast, steam supply electrification, by replacing fossil fuel boilers with direct heat supply (such as commercial electric boilers or heat pumps), enables reducing emissions by up to 75%. This means that the goal of 100% CO2 emission reduction by 2050 cannot be reached with one method alone. Consequently, a combination of technologies, particularly with the electrification of the steam supply, along with the use of carbon-free electricity generated by renewable energy, appears to be essential.

scholarly journals V2V and V2I Communications for Traffic Safety and CO2 Emission Reduction: A Performance Evaluation

2019 ◽  
Vol 151 ◽  
pp. 353-360 ◽  
Author(s):  
Fatma Outay ◽  
Faouzi Kamoun ◽  
Florent Kaisser ◽  
Doaa Alterri ◽  
Ansar Yasar
Keyword(s):  
Performance Evaluation ◽  
Traffic Safety ◽  
Emission Reduction ◽  
Co2 Emission ◽  
Co2 Emission Reduction ◽  
A Performance

Forecasting Model of Short-Term Traffic Flow Based on Cloud Model

2011 ◽  
Vol 94-96 ◽  
pp. 38-42
Author(s):  
Qin Liu ◽  
Jian Min Xu
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Cloud Model ◽  
Prediction Method ◽  
Absolute Error ◽  
Short Term ◽  
Guangzhou City ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Absolute Percent Error

In order to improve the prediction precision of the short-term traffic flow, a prediction method of short-term traffic flow based on cloud model was proposed. The traffic flow was fit by cloud model. The history cloud and the present cloud were built by historical traffic flow and present traffic flow. The forecast cloud is produced by both clouds. Then, combining with the volume of the short-term traffic flow of an intersection in Guangzhou City, the model was calculated and simulated through programming. Max Absolute Error (MAE) and Mean Absolute percent Error (MAPE) were used to estimate the effect of prediction. The simulation results indicate that this prediction method is effective and advanced. The change of the historical and real time traffic flow is taken into account in this method. Because the short-term traffic flow is dealt with as a whole, the error of prediction is avoided. The prediction precision and real-time prediction are satisfied.

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