Recently,Chinese megacities have suffered serious air pollution.Previous studies have pointed out that transportation systems have become one of the major sources of air pollution and on-road pollutant concentrations ...Recently,Chinese megacities have suffered serious air pollution.Previous studies have pointed out that transportation systems have become one of the major sources of air pollution and on-road pollutant concentrations are significantly higher than off-road.Electric vehicle(EV)introduction is proposed as a method to alleviate the current situation.In order to better understand the benefit of the use of EVs in Beijing,a simulation platform has been developed to evaluate the improvement of air quality with the use of EVs quantitatively within the selected area.Four scenarios with different EV penetration rates are proposed and the results revealed 5%,10%,15%EV penetration rates which will bring about improvement of 0.86%,9.01%and 12.23%for PM2.5,0.92%,9.01%and 13.32%for nitrogen oxides(NO_(x)),0.95%,8.86%and 13.73%for CO,respectively.The results revealed a promising improvement of air quality with the introduction of EVs.展开更多
A heavy 16-day pollution episode occurred in Beijing from December 19, 2015 to January 3,2016. The mean daily AQI and PM2.5 were 240.44 and 203.6 μg/m^3. We analyzed the spatiotemporal characteristics of air pollutan...A heavy 16-day pollution episode occurred in Beijing from December 19, 2015 to January 3,2016. The mean daily AQI and PM2.5 were 240.44 and 203.6 μg/m^3. We analyzed the spatiotemporal characteristics of air pollutants, meteorology and road space speed during this period, then extended to reveal the combined effects of traffic restrictions and meteorology on urban air quality with observational data and a multivariate mutual information model. Results of spatiotemporal analysis showed that five pollution stages were identified with remarkable variation patterns based on evolution of PM2.5 concentration and weather conditions. Southern sites（DX, YDM and DS） experienced heavier pollution than northern ones（DL, CP and WL）. Stage P2 exhibited combined functions of meteorology and traffic restrictions which were delayed peak-clipping effects on PM2.5.Mutual information values of Air quality–Traffic–Meteorology（ATM–MI） revealed that additive functions of traffic restrictions, suitable relative humidity and temperature were more effective on the removal of fine particles and CO than NO2.展开更多
The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to...The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used.The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers＇ information and vehicles＇ parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively.The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.展开更多
基金supported by the project“Research on the Traffic Environment Carrying Capacity and Feedback Gating Based Dynamic Traffic Control in Urban Network”which is funded by the China Postdoctoral Science Foundation with Grant no.2013M540102supported by the project“The research and application of the urban air environment regulation and control technology based on the Internet of Things”,which is under the State High-Tech Development Plan(The 863 program)+1 种基金funded by The Ministry of Science and Technology of the People’s Republic of China(Project No.2012AA063303)support for providing data and technology support.
文摘Recently,Chinese megacities have suffered serious air pollution.Previous studies have pointed out that transportation systems have become one of the major sources of air pollution and on-road pollutant concentrations are significantly higher than off-road.Electric vehicle(EV)introduction is proposed as a method to alleviate the current situation.In order to better understand the benefit of the use of EVs in Beijing,a simulation platform has been developed to evaluate the improvement of air quality with the use of EVs quantitatively within the selected area.Four scenarios with different EV penetration rates are proposed and the results revealed 5%,10%,15%EV penetration rates which will bring about improvement of 0.86%,9.01%and 12.23%for PM2.5,0.92%,9.01%and 13.32%for nitrogen oxides(NO_(x)),0.95%,8.86%and 13.73%for CO,respectively.The results revealed a promising improvement of air quality with the introduction of EVs.
基金conducted as part of the project "Concentration prediction of urban air pollutants based on deep learning" funded by Doctoral scholarship program of Tsinghua Universitypartly financial support is also provided by the National Natural Science Foundation of China (Nos. 61304199 41471333)
文摘A heavy 16-day pollution episode occurred in Beijing from December 19, 2015 to January 3,2016. The mean daily AQI and PM2.5 were 240.44 and 203.6 μg/m^3. We analyzed the spatiotemporal characteristics of air pollutants, meteorology and road space speed during this period, then extended to reveal the combined effects of traffic restrictions and meteorology on urban air quality with observational data and a multivariate mutual information model. Results of spatiotemporal analysis showed that five pollution stages were identified with remarkable variation patterns based on evolution of PM2.5 concentration and weather conditions. Southern sites（DX, YDM and DS） experienced heavier pollution than northern ones（DL, CP and WL）. Stage P2 exhibited combined functions of meteorology and traffic restrictions which were delayed peak-clipping effects on PM2.5.Mutual information values of Air quality–Traffic–Meteorology（ATM–MI） revealed that additive functions of traffic restrictions, suitable relative humidity and temperature were more effective on the removal of fine particles and CO than NO2.
基金supported by the project "Research on the Traffic Environment Carrying Capacity and Feedback Gating Based Dynamic Traffic Control in Urban Network" which is funded by the China Postdoctoral Science Foundation (No. 2013M540102)supported by the Open Foundation of smart-city research center of Hangzhou Dianzi University, smart-city research center of Zhejiang Province
文摘The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used.The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers＇ information and vehicles＇ parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively.The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.
