Academic Achievements

Research on the collaborative optimization of fuel consumption and battery energy consumption for hybrid vehicles in the intelligent connected environment, sponsored by the National Natural Science Foundation of China, PI, 2025-2028.

Evaluation and early warning of electric vehicle battery status based on general pre-trained algorithms, sponsored by the Shanghai New Energy Vehicle Public Data Collection and Monitoring Research Center, PI, 2024-2025.

Dispersion mechanics of traffic-related air pollution in street canyon with viaduct based on the vertical monitoring of unmanned aerial vehicles, sponsored by National Natural Science Foundation of China, PI, 2021-2024.

Research on the construction of the full industrial chain ecosystem for China's cruise industry, sponsored by the Shanghai Municipal People's Government (Decision-Making Consultation Program), PI, 2023-2024.

Research on the data infrastructure development path for the new energy vehicle service industry in Shanghai, sponsored by the 2023 "Science and Technology Innovation Action Plan" Soft Science Research Program of Shanghai, PI, 2023-2024.

Investigation of hybrid-diesel electrical technology for the carbon dioxide peaking and carbon neutrality in Shanghai. sponsored by Shanghai Municipal People's Government (Key Program on Decision-making Consultation), PI, 2021-2022.

Spatiotemporal distributions of traffic-related carbon emissions in near-road neighborhoods, a collaborative project between Shanghai Jiao Tong University and Cornell University, PI, 2022-2023.

Assessment of mobility as a service (MaaS) in sustainable development, a collaborative project between Shanghai Jiao Tong University and Osaka University, PI, 2019-2020.

Dynamics dispersion and control of traffic-generated ultrafine particles in traffic congestion area, sponsored by National Natural Science Foundation of China, PI, 2017-2020.

Dynamic modeling and optimization of urban road intersection traffic flow based on particulate matter reduction, sponsored by the National Natural Science Foundation of China (Youth Program), PI, 2014-2016.

Dynamic optimization of urban vehicle flows and control of inhalable particulate matter pollution in Shanghai, sponsored by the Pujiang Talent Program of Shanghai, PI, 2012-2014. [12].Study of particulate matter form port emission and its influence on surrounding area, sponsored by Science & Technology Commission of Shanghai, PI, 2014-2016.

PM emission characteristics of ports and their impact on regional air quality, sponsored by the Shanghai Science and Technology Commission, 2014-2016, PI.

Atmospheric vertical structure detection technology based on UAVs and large-load aerostats, a subproject of the National Key R&D Program of China, Co-PI, 2016-2020.

Evaluation of the impact of urban transportation policies and infrastructure on atmospheric environment, sponsored by the Major Program of the National Social Science Foundation of China, Co-PI, 2016-2020.

Integrated decision-making and big data applications for intelligent environmental protection in Shanghai, sponsored by the Shanghai Environmental Protection Bureau, Co-PI, 2018-2019. [16].Dynamic calculation of ship exhaust emissions and energy consumption based on real-time AIS data, sponsored by Ministry of Transport of the PRC, Co-PI, 2014-2016.

Statistical algorithms for ship energy consumption and carbon emissions based on vessel trajectories, sponsored by the Ministry of Transport, 2014-2016, participant.

Port Comprehensive Development Index, sponsored by the Shanghai Transportation and Port Administration Bureau, Co-PI, 2012. 2024 1. He, HD*, LU, DN., Zhao, HM, Peng, ZR. Characterizing CO2 and NOx emission of vehicles crossing toll stations in highway. Trans. Res. Part D, 2024, 126: 104024. https://doi.org/10.1016/j.trd.2023.104024 2. Huang, H.C., Li, BW., Wang, YZ., Zhang, Z. He, H.D*. Analysis of factors influencing energy consumption of electric vehicles: Statistical, predictive, and causal perspectives. Applied Energy, 2024, 375: 124110. https://doi.org/10.1016/j.apenergy.2024.124110 3. Huang, H.C., He, HD*., Peng, Z.R. Urban-scale estimation model of carbon emissions for ride-hailing electric vehicles during operational phase. Energy, 2024, 293: 130665. https://doi.org/10.1016/j.energy.2024.130665 4. Zhang, Zhe., Qing, Y., Gao, Kun., He, H.D*. Liu, Y., Huang, H.C. Carbon emission reduction benefits of ride-hailing vehicle electrification considering energy structure. Applied Energy, 2024, 375: 4905243. https://doi.org/10.1016/j.apenergy.2024.124548 5. Zhao, HM, He, HD*, Lu, DN, Zhou, D, Lu, CX, Fang, XR, Peng, ZR. Evaluation of CO2 and NOx emissions from container diesel trucks using a portable emissions measurement system, Build. Environ. 2024, 252: 111266. https://doi.org/10.1016/j.buildenv.2024.111266 6. Zhang, Z., Gao, K., He, HD*., Cui, SH., Hu, LY., Yu, Q., Peng, ZR. Environmental impacts of ridesplitting considering modal substitution and associations with built environment. Trans. Res. Part D, 2024, 130: 104160. https://doi.org/10.1016/j.trd.2024.104160 7. Zhao, H.M., He, H.D*., Wu, C.L., Zhu, X.H., Zhou, D., Peng, Z.R. Multifractal property change of NOx and O3 variations in port area in responding to COVID-19 lockdown. Stochastic Environmental Research and Risk Assessment, 2024,38: 1145-1161. https://doi.org/10.1007/s00477-023-02620-z 8. Huang, H.C., Chen, Z.H., Li, B.W., Ma, Q.H., He, H.D*. FeSTGCN: A frequency-enhanced spatio-temporal graph convolutional network for traffic flow prediction under adaptive signal timing. Applied Intelligence, 2024, 54: 4848–4864. https://doi.org/10.1007/s10489-024-05401-5 9. Chen, Z.H., Li, B. W., Li, B.W., Peng, Z.R., Huang, H. C., Wu, J.Q., He, H.D*. Identification of particle distribution pattern in vertical profile via unmanned aerial vehicles observation. Environmental Pollution, 2024, 348: 123893. https://doi.org/10.1016/j.envpol.2024.123893 10. Huang, H.C., He, H.D*., Fu, Q.Y., Pan, J., Peng, Z.R. A deep learning model incorporating frequency domain information for ultra multi-step air pollutant forecasting: A case study of Shanghai. Atmospheric Pollution Research, 2024, 15: 102247. https://doi.org/10.1016/j.apr.2024.102247 2023 1. He, H.D*., Wang, Z.Y., Zhao, H.M., Pan, W., Lu, W.Z. Spatial-temporal distribution and pedestrian exposure assessment of size-fractionated particles on crosswalk of urban intersection. Environmental Science and Pollution Research, 2023, 30: 83917-83928. https://doi.org/10.1007/s11356-023-28150-3 2. Zhang, Z., Gao, K., He, HD*., Yang, JM., Jia R., Peng, ZR. How do travel characteristics of ridesplitting affect its benefits in emission reduction? evidence from Chengdu. Trans. Res. Part D, 2023, 123: 103912. https://doi.org/10.1016/j.trd.2023.103912 3. Liu, R., He, H.D*., Zhang, Z., Wu, C.L., Yang, J.M., Zhu, X.H., Peng, Z.R. Integrated MOVES model and machine learning method for prediction of CO2 and NO from light-duty gasoline vehicle. Journal of Cleaner Production, 2023, 422, 138612. https://doi.org/10.1016/j.jclepro.2023.138612 4. Wu, C.L., He, H.D*., Song, R.F., Zhu, X.H., Peng, Z.R., Fu, Q.Y., Pan, J. A hybrid deep learning model for regional O3 and NO2 concentrations prediction based on spatiotemporal dependencies in air quality monitoring network. Environmental Pollution, 2023, 320, 121075. https://doi.org/10.1016/j.envpol.2023.121075 5. Liu, X., Shi, X.Q., Peng, Z.R*., He, H.D*. Quantifying the effects of urban fabric and vegetation combination pattern to mitigate particle pollution in near-road areas using machine learning. Sustainable Cities and Society, 2023, 93: 104524. https://doi.org/10.1016/j.scs.2023.104524 6. Xu, S.Q., He, H.D*., Yang, M.K., Wu, C.L., Zhu, X.H., Peng, Z.R., Sasaki, Y., Doi, K., Shimojo, S. To what extent the traffic restriction policies can improve its air quality? An inspiration from COVID-19. Stochastic Environmental Research and Risk Assessment, 2023,37: 1479-1495. https://doi.org/10.1007/s00477-022-02351-7 7. Lu, D.N., He, H.D*., Zhao, H.M., Lu, K.F., Peng, Z.R., Li, J*. Quantification of traffic-related carbon emission on elevated roads through on-road measurement. Environmental Research, 2023, 116200. https://doi.org/10.1016/j.envres.2023.116200 8. Lu, D.N., He, H.D*., Wang, Z., Zhao, H.M., Peng, Z.R. Impact of urban viaducts on the vertical distribution of fine particles in street canyons. Atmospheric Pollution Research, 2023, 14, 101726. https://doi.org/10.1016/j.apr.2023.101726 9. Huang, H.C., Cheng, J., Shi, B.C., He, H.D*. Multi-step forecasting of short-term traffic flow based on Intrinsic Pattern Transform. Physica A. 2023,621, 128798. https://doi.org/10.1016/j.physa.2023.128798 2022 1. Kai-Fa Lu, Hong-Wei Wang, Xiao-Bing Li, Zhong-Ren Peng*, Hong-Di He, Zhi-Peng Wang, 2022, Assessing the effects of non-local traffic restriction policy on urban air quality, Transport Policy, Volume 115, Pages 62-74, https://doi.org/10.1016/j.tranpol.2021.11.005 2. Li, B., Cao, R., He, H. D., Peng, Z. R., Qin, H., & Qin, Q. (2022). Three-dimensional diffusion patterns of traffic-related air pollutants on the roadside based on unmanned aerial vehicles monitoring. Building and Environment, 109159. https://doi.org/10.1016/j.buildenv.2022.109159 3. Jiang, Y. H., Li, B., Li, X. B., Wang, D. S., & Peng, Z. R. (2022). Identification of the atmospheric boundary layer structure through vertical distribution of PM2. 5 obtained by unmanned aerial vehicle measurements. Atmospheric Environment, 119084. https://doi.org/10.1016/j.atmosenv.2022.119084 4. Zhu, X. H., Lu, K. F., Peng, Z. R., He, H. D., & Xu, S. Q. (2022). Spatiotemporal variations of carbon dioxide (CO2) at Urban neighborhood scale: Characterization of distribution patterns and contributions of emission sources. Sustainable Cities and Society, 78, 103646. https://doi.org/10.1016/j.scs.2021.103646 5. Liu, X., Shi, X. Q., He, H. D., & Peng, Z. R. (2022). Distribution characteristics of submicron particle influenced by vegetation in residential areas using instrumented unmanned aerial vehicle measurements. Sustainable Cities and Society, 78, 103616. https://doi.org/10.1016/j.scs.2021.103616 6. Li, C., H.D. He, and Z.R. Peng, Spatial distributions of particulate matter in neighborhoods along the highway using unmanned aerial vehicle in Shanghai. Building and Environment, 2022. 211. https://doi.org/10.1016/j.buildenv.2022.108754 7. Zhang, Z., et al., Spatiotemporal evolution of NO2 diffusion in Beijing in response to COVID-19 lockdown using complex network. Chemosphere, 2022. 293. https://doi.org/10.1016/j.chemosphere.2022.133631 8. Hong-Mei Zhao, Hong-Di He, Kai-Fa Lu, Xiao-Long Han, Zhong-Ren Peng. Characterizing the distribution pattern of submicron and coarse particles on high-density container truck roads through mobile monitoring, Atmospheric Pollution Research, 2022. https://doi.org/10.1016/j.apr.2022.101561 9. Xing-hang Zhu, Hong-di He, Kai-fa Lu, Zhong-ren Peng, H. Oliver Gao. Characterizing carbon emissions from China V and China VI gasoline vehicles based on portable emission measurement systems,Journal of Cleaner Production, 2022. https://doi.org/10.1016/j.jclepro.2022.134458 10. Tie Zheng, Zhong-Ren Peng, Hong-Di He, Shaojun Zhang, Ye Wu. Horizontal profiles of size-segregated particle number concentration and black carbon beside a major roadway,Atmospheric Environment: X,Volume 16, 2022. https://doi.org/10.1016/j.aeaoa.2022.100187 11. Hong-Mei Zhao, Hong-Di He, Kai-Fa Lu, Xiao-Long Han, Yi Ding, Zhong-Ren Peng. Measuring the impact of an exogenous factor: An exponential smoothing model of the response of shipping to COVID-19,Transport Policy, Volume 118, 2022, Pages 91-100. https://doi.org/10.1016/j.tranpol.2022.01.015 2021 1. Kai-Fa Lu, Hong-Wei Wang, Xiao-Bing Li, Zhong-Ren Peng*, Hong-Di He, Zhi-Peng Wang, 2022, Assessing the effects of non-local traffic restriction policy on urban air quality, Transport Policy, Volume 115, Pages 62-74, https://doi.org/10.1016/j.tranpol.2021.11.005 2. He, H.D*., Gao, H. Oliver. Particulate Matter Exposure at a Densely Populated Urban Traffic Intersection and Crosswalk. Environmental Pollution. 2021, 268:115931(ESI). https://doi.org/10.1016/j.envpol.2020.115931 3. Zhao, H.M., He, H.D*., Zhao, J.Q., Ding, Y., Peng, Z.R., Wang, H.W. Characterizing the Particle Variations and Human Exposure in Port and Urban Areas. Transportation Research Record 2021, 2675: 669-684. https://doi.org/10.1177/03611981211019041 4. Wang, Z.Y., He, H.D*., Zhao, H.M., Peng, Z.R. Spatiotemporal analysis of pedestrian exposure to submicron and coarse particulate matter on crosswalk at urban intersection. Building Environment 2021, 204:108149 https://doi.org/10.1016/j.buildenv.2021.108149 5. Xin Liu, Xue-Qing Shi, Hong-Di He, Zhong-Ren Peng*, 2021, Distribution characteristics of submicron particle influenced by vegetation in residential areas using instrumented unmanned aerial vehicle measurements, Sustainable Cities and Society, https://doi.org/10.1016/j.scs.2021.103616 6. Wu, Cui-lin, Hong-wei Wang, Wan-jin Cai, Hong-di He, An-ning Ni, Zhong-Ren Peng (2021), Impact of the COVID-19 lockdown on roadside traffic-related air pollution in Shanghai, China, Building and Environment, Volume 194, May 2021, 107718, https://doi.org/10.1016/j.buildenv.2021.107718 7. Wu, Cui-lin, Hong-di He, Rui-feng Song, Zhong-ren Peng, (2021), Prediction of air pollutants on roadside of the elevated roads with combination of pollutants periodicity and deep learning method, Building and Environment, 108436, https://doi.org/10.1016/j.buildenv.2021.108436 8. X. Liu, X. Shi, H. He, X. Li, Z. Peng (2021), Vertical distribution characteristics of particulate matter beside an elevated expressway by unmanned aerial vehicle measurements, Build. Environ. 206 (2021) 108330. https://doi.org/10.1016/j.buildenv.2021.108330. 9. Rui-feng Song, Dong-sheng Wang, Xiao-bing Li, Bai Li, Zhong-Ren Peng, Hong-di He, (2021), Characterizing Vertical Distribution Patterns of PM2.5 in Low Troposphere of Shanghai city, China: implications from the perspective of unmanned aerial vehicle observations, Atmospheric Environment, Available online 14 September 2021, 118724, https://doi.org/10.1016/j.atmosenv.2021.118724 10. Cai, Wan-Jin, Hong-Wei Wang, Cui-Lin Wu, Kai-Fa Lu, Zhong-Ren Peng, Hong-Di He (2021), Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China, Building and Environment, Volume 205, 108231, https://doi.org/10.1016/j.buildenv.2021.108231 11. Yang, J., Peng, Z.R.*, Lin, L. (2021). Real-time Spatiotemporal Prediction and Imputation of Traffic Status Based on LSTM and Graph Laplacian Regularized Matrix Factorization, Transportation Research Part C, Vol. 129, 203228, https://doi.org/10.1016/j.trc.2021.103228 12. Luo, Z., Wang, Z., Wang, H., He, H., & Peng, Z.R. (2021). Characterizing spatiotemporal distributions of black carbon and PM2.5 at a toll station: Observations on manual and electronic toll collection lanes. Building and Environment, 199, 107933. https://doi.org/10.1016/j.buildenv.2021.107933 13. Zhai, W., Liu, M., Fu, X., Peng, Z.R*. (2021). American Inequality Meets COVID-19: Uneven Spread of the Disease across Communities, Annals of the American Association of Geographers. https://doi.org/10.1080/24694452.2020.1866489 14. Zheng, Tie, Yue-Ping Jia, Shaojun Zhang, Xiao-Bing Li, Ye Wu, Cui-Lin Wu, Hong-Di He, Zhong-Ren Peng, (2021). Impacts of vegetation on particle concentrations in roadside environments, Environmental Pollution, Volume 282, 117067, https://doi.org/10.1016/j.envpol.2021.117067 15. Tanvir, M. R. A., He, H. di, & Peng, Z. R. (2021). Spatio-temporal variability in black carbon concentrations at highway toll plaza: Comparison between manual and electronic toll lanes. Atmospheric Pollution Research, 12(1), 286–294. https://doi.org/10.1016/j.apr.2020.09.010 16. Zheng, T., Li, B., Li, X. B., Wang, Z., Li, S. Y., & Peng, Z. R. (2021). Vertical and horizontal distributions of traffic-related pollutants beside an urban arterial road based on unmanned aerial vehicle observations. Building and Environment, 187, 107401. https://doi.org/10.1016/j.buildenv.2020.107401 17. Zheng, T., Wang, H. W., Li, X. B., Peng, Z. R., & He, H. Di. (2021). Impacts of traffic on roadside particle variations in varied temporal scales. Atmospheric Environment, 253, 118354. https://doi.org/10.1016/j.atmosenv.2021.118354 18. Luo, Z., Wang, Z., Wang, H., He, H., & Peng, Z.R. (2021). Characterizing spatiotemporal distributions of black carbon and PM2.5 at a toll station: Observations on manual and electronic toll collection lanes. Building and Environment, 199, 107933. https://doi.org/10.1016/j.buildenv.2021.107933 19. Jia, Y. P., Lu, K. F., Zheng, T., Li, X. B., Liu, X., Peng, Z. R., & He, H. Di. (2021). Effects of roadside green infrastructure on particle exposure: A focus on cyclists and pedestrians on pathways between urban roads and vegetative barriers. Atmospheric Pollution Research, 12(3), 1–12. https://doi.org/10.1016/j.apr.2021.01.017 2020 1. Gao, Ya, Zhanyong Wang, Chao-yang Li, Tie Zheng, Zhong-Ren Peng, (2020), Assessing neighborhood variations in ozone and PM2.5 concentrations using decision tree method, Building and Environment, https://doi.org/10.1016/j.buildenv.2020.107479 2. He, H.D*., Lu, W.Z. Comparison of three prediction strategies within PM2.5 and PM10 monitoring networks. Atmospheric Pollution Research. 2020, 11: 590-597. https://doi.org/10.1016/j.apr.2019.12.010 3. Li, X.B., Peng, Z.R., Lu, Q.C., Wang, D.F., Hu, X.M., Wang, D.S., Li, B., Fu, Q.Y., Xiu, G.L. He, H.D*. Evaluation of unmanned aerial system in measuring lower tropospheric ozone and fine aerosol particles using portable monitors. Atmospheric Environment 2020, 117134. https://doi.org/10.1016/j.atmosenv.2019.117134 4. Wang, Dongsheng, Hong-Wei Wang, Chao Li, Kai-Fa Lu, Zhong-Ren Peng, Juanhao Zhao, Qingyan Fu, and Jun Pan. (2020). "Roadside Air Quality Forecasting in Shanghai with a Novel Sequence-to-Sequence Model" International Journal of Environmental Research and Public Health 17, no. 24: 9471. https://doi.org/10.3390/ijerph17249471 5. Cao, R., Li, B., Wang, H. W., Tao, S., Peng, Z. R., & He, H. D. (2020). Vertical and Horizontal Profiles of Particulate Matter and Black Carbon Near Elevated Highways Based on Unmanned Aerial Vehicle Monitoring. Sustainability, 12(3), 1204. https://www.mdpi.com/2071-1050/12/3/1204 6. Wang, H. W., Peng, Z. R., Wang, D., Meng, Y., Wu, T., Sun, W. *, Lu, Q. C. * (2020). “Evaluation and prediction of transportation resilience under extreme weather events: A diffusion graph convolutional approach,” Transportation Research Part C: Emerging Technologies, 115, 102619. https://doi.org/10.1016/j.trc.2020.102619 7. Chen, Q., Li, X., Song, R., Wang, H. W., Li, B., He, H. D.* and Peng, Z. R.* (2020), “Development and utilization of hexacopter unmanned aerial vehicle platform to characterize vertical distribution of boundary layer ozone in wintertime,” Atmospheric Pollution Research, https://doi.org/10.1016/j.apr.2020.04.002 8. Cao, Rong, Bai Li, Zhanyong Wang, Zhong-Ren Peng, Shikang Tao, Shengrong Lou, 2020, “Using a distributed air sensor network to investigate the spatiotemporal patterns of PM2.5 concentrations,” Environmental Pollution, https://doi.org/10.1016/j.envpol.2020.114549 9. Lu, Kai-Fa, Hong-Di He, Hong-Wei Wang, Xiao-Bing Li, Zhong-Ren Peng*, 2020, “Characterizing temporal and vertical distribution patterns of traffic-emitted pollutants near an elevated expressway in urban residential areas,” Building and Environment, https://doi.org/10.1016/j.buildenv.2020.106678 10. Wang, Hong-Wei, Xiao-Bing Li, Dongsheng Wang, Juanhao Zhao, Hong-Di He, Zhong-Ren Peng*, 2020, “Regional prediction of ground-level ozone using a hybrid sequence-to-sequence deep learning approach,” Journal of Cleaner Production, Volume 253, 20 April 2020, 119841, https://doi.org/10.1016/j.jclepro.2019.119841 2019 1. Chen, Qian, Dongsheng Wang, Xiaobing Li, Bai Li, Ruifeng Song, Hongdi He*, Zhong-Ren Peng* (2019), “Vertical Characteristics of Winter Ozone Distribution within the Boundary Layer in Shanghai Based on Hexacopter Unmanned Aerial Vehicle Platform,” Sustainability 2019, 11(24), 7026; https://doi.org/10.3390/su11247026 2. Li, Xiao-Bing, Zhong-Ren Peng*, Qing-Chang Lu, Dongfang Wang, Xiao-Ming Hu, Dongsheng Wang, BaiLi, Qingyan Fu, Guangli, Xiu, Hongdi He (2019), Evaluation of unmanned aerial system in measuring lower tropospheric ozone and fine aerosol particles using portable monitors, Atmospheric Environment, https://doi.org/10.1016/j.atmosenv.2019.117134 3. Li, B., Li, X. B., Li, C., Zhu, Y., Peng, Z. R*., Wang, Z., & Lu, S. J. (2019). Impacts of wind fields on the distribution patterns of traffic emitted particles in urban residential areas. Transportation Research Part D: Transport and Environment, 68, 122-136. https://doi.org/10.1016/j.trd.2018.01.030 4. Li, Bai, Rong Cao, Zhanyong Wang, Rui-Feng Song, Zhong-Ren Peng*, Guangli Xiu, Qingyan Fu (2019), “Use of multi-rotor unmanned aerial vehicles for fine-grained road-side air quality monitoring,” Transportation Research Record: Journal of the Transportation Research Board, https://doi.org/10.1177/0361198119847991 2018 1. Chao Li, Zhanyong Wang*, Bai Li, Zhong-Ren Peng*, Qingyan Fu (2018), Investigating the relationship between air pollution variation and urban form. Building and Environment, https://doi.org/10.1016/j.buildenv.2018.06.038 2. He, H.D*., Li, M., Wang, W.L., Wang, Z.Y., Xue, Y. Prediction of PM2.5 Concentration based on the Similarity in Air Quality Monitoring Network. Building Environment 2018, 137:11-17. https://doi.org/10.1016/j.buildenv.2018.03.058 3. He, H.D*., Zhang, C.Y., Wang, W.L., Hao, Y.Y., Ding, Y. Feedback control scheme for traffic jam and energy consumption based on two-lane traffic flow model. Transportation Research Part D 2018, 60:76-84. https://doi.org/10.1016/j.trd.2015.11.005 4. Bai Li, Yi Zhu, Zhanyong Wang*, Chao Li, Zhong-Ren Peng,* and Lixin Ge, 2018, “Use of Multi-Rotor Unmanned Aerial Vehicles for Radioactive Source Search,” Remote Sensing, 2018, 10(5), 728; https://doi.org/10.3390/rs10050728 5. Li, Xiao-Bing, Dongfang Wang, Qing-Chang Lu, Zhong-Ren Peng*, Qingyan Fu, Xiao-Ming Hu, Juntao Huo, Guangli Xiu, Bai Li, Chao Li, Dong-Sheng Wang, Hanyu Wang, (2018), Three-dimensional analysis of ozone and PM2.5 distributions obtained by observations of tethered balloon and unmanned aerial vehicle in Shanghai, China, Stochastic Environmental Research and Risk Assessment, 32(5), pp. 1189-1203, DOI:10.1007/s00477-018-1524-2 6. Jiang, Ruoyun, Qing-Chang Lu, Zhong-Ren Peng*, (2018) “A station-based rail transit network vulnerability measure considering land use dependency,” Journal of Transport Geography, Volume 66, Pages 10-18, https://doi.org/10.1016/j.jtrangeo.2017.09.009 2017 1. Chao Li, Zhanyong Wang*, Bai Li, Zhong-Ren Peng*, Qingyan Fu (2018), Investigating the relationship between air pollution variation and urban form. Building and Environment, https://doi.org/10.1016/j.buildenv.2018.06.038 2. He, H.D*., Li, M., Wang, W.L., Wang, Z.Y., Xue, Y. Prediction of PM2.5 Concentration based on the Similarity in Air Quality Monitoring Network. Building Environment 2018, 137:11-17. https://doi.org/10.1016/j.buildenv.2018.03.058 3. He, H.D*., Zhang, C.Y., Wang, W.L., Hao, Y.Y., Ding, Y. Feedback control scheme for traffic jam and energy consumption based on two-lane traffic flow model. Transportation Research Part D 2018, 60:76-84. https://doi.org/10.1016/j.trd.2015.11.005 4. Bai Li, Yi Zhu, Zhanyong Wang*, Chao Li, Zhong-Ren Peng,* and Lixin Ge, 2018, “Use of Multi-Rotor Unmanned Aerial Vehicles for Radioactive Source Search,” Remote Sensing, 2018, 10(5), 728; https://doi.org/10.3390/rs10050728 5. Li, Xiao-Bing, Dongfang Wang, Qing-Chang Lu, Zhong-Ren Peng*, Qingyan Fu, Xiao-Ming Hu, Juntao Huo, Guangli Xiu, Bai Li, Chao Li, Dong-Sheng Wang, Hanyu Wang, (2018), Three-dimensional analysis of ozone and PM2.5 distributions obtained by observations of tethered balloon and unmanned aerial vehicle in Shanghai, China, Stochastic Environmental Research and Risk Assessment, 32(5), pp. 1189-1203, DOI:10.1007/s00477-018-1524-2 6. Jiang, Ruoyun, Qing-Chang Lu, Zhong-Ren Peng*, (2018) “A station-based rail transit network vulnerability measure considering land use dependency,” Journal of Transport Geography, Volume 66, Pages 10-18, https://doi.org/10.1016/j.jtrangeo.2017.09.009 2016 1. He, H.D., Pan, W., Lu, W. Z., Xue, Y. Multifractal property and long-range cross-correlation behavior of particulate matters at urban traffic intersection in Shanghai. Stochastic Environmental Research and Risk Assessment 2016, 30:1515-1525. 2. He, H.D*., Wang, J.L., Wei, H.R., Ye, C., Ding, Y. Fractal behavior of traffic volume on urban expressway through adaptive fractal analysis. Physica A 2016, 443:518–525. 3. Wang H.-W., Cheng K., Lu Q.-C., Peng Z.-R*(2016). Improved model of start-wave velocity at intersections based on unmanned aerial vehicle data. Journal of Donghua University (English Edition) 33(1) 13-19 4. Li, Xiao-Bing, Qing-Chang Lu, Si-Jia Lu, Hong-Di He, Zhong-Ren Peng*, Ya Gao, Zhan-Yong Wang (2016), The impacts of roadside vegetation barriers on the dispersion of gaseous traffic pollution in urban street canyons, Urban Forestry & Urban Greening, Volume 17, Pages 80–91, doi:10.1016/j.ufug.2016.03.006 5. Gao, Ya, Zhanyong Wang, Qing-Chang Lu, Chao Liu, Zhong-Ren Peng*, Yue Yu (2016), “Prediction of vertical PM 2.5, concentrations alongside an elevated expressway by using the neural network hybrid model and generalized additive model.” Frontiers of Earth Science, 2016:1-14 2015 1. Wang Q., Liu Z., Peng Z* (2015), A PSO-SVM model for short-term travel time prediction based on bluetooth technology, Journal of Harbin Institute of Technology (New Series) 22（3） 7-14 2. Sun D.J., Xu Y., Peng Z.-R.（2015）Timetable optimization for single bus line based on hybrid vehicle size model. Journal of Traffic and Transportation Engineering (English Edition).2(3)179-186 3. Peng, Zhong-Ren*, Dongsheng Wang, Zhanyong Wang, Ya Gao, Sijia Lu, (2015), A study of vertical distribution patterns of PM2.5 concentrations based on ambient monitoring with unmanned aerial vehicles: A case in Hangzhou, China, Atmospheric Environment, doi:10.1016/j.atmosenv.2015.10.074 4. Ni, Xun-You; Sun, Daniel Jian*; & Peng, Zhong-Ren (2015). An improved incremental assignment model for parking variable message sign location problem. Journal of advanced transportation, 49(4). doi:10.1002/atr.1305 5. Bai, Cong, Zhong-Ren Peng, Qing-Chang Lu, and Jian Sun, (2015) Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes, Computational Intelligence and Neuroscience Volume 2015, Article ID 432389, http://dx.doi.org/10.1155/2015/432389 6. He, H.D., Lu, W. Z., Xue, Y. Prediction of Particulate Matter at Urban Intersection by using Multilayer Perceptron Model based on Principal Components. Stochastic Environmental Research and Risk Assessment 2015, 29: 2107-2114. 2014 1. Zhang, Dao-Zheng & Peng, Zhong-Ren* (2014). Near-road fine particulate matter concentration estimation using artificial neural network approach. International Journal of Environmental Science and Technology, 11(8), 2403-2412. doi: 10.1007/s13762-014-0565-4 2. Zhang, Dao-Zheng; Peng, Zhong-Ren; & Sun, Jian* (2014). A Comprehensive Taxi Assessment Index Using Floating Car Data. Journal of Harbin Institute of Technology, 21(1), 7-16. 3. Sun, Daniel, C Zhang, L Zhang, F Chen, ZR Peng (2014), Urban travel behavior analyses and route prediction based on floating car data, Transportation Letters, Pages 118-125. https://doi.org/10.1179/1942787514Y.0000000017 4. He, H.D., Lu, W. Z., Xue, Y. Prediction of Particulate Matter at Urban Intersection by using Artificial Neural Networks combined with Chaotic Particle Swarm Optimization Algorithm. Building Environment 2014, 78:111-117. 2013 1. Chang, Jing-Jing; Peng, Zhong-Ren; & Sun, Jian* (2013). Freight vehicle routing optimization for sporadic orders using floating car data. Journal of Donghua University (English Edition), 30(2) ,96-102. 2. Zhang Dao-Zheng; Sun, Jian*; & Peng, Zhong-Ren (2013). Urban Taxi Goodness Index and System Implementation of GIS. Journal of Transportation Systems Engineering and Information Technology, 13(1), 87-96. (In Chinese) 2012 1. Peng, Zhong-Ren; Sun, Jian; & Lu, Qing-Chang (2012). 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Performance assessment of air quality monitoring networks using principal component analysis and cluster analysis. Building Environment 2011, 46:577-583. CO₂ Emissions Calculator for Container Truck Version 1 Container trucks are a major contributor to transportation-related carbon emissions in port cities. Quantifying the carbon emission intensity of container trucks and establishing emission factors are fundamental to accurately evaluating the carbon emissions of port logistics systems. These efforts are also critical for achieving carbon peaking and neutrality goals within the urban transportation sector. In recent years, the Center for ITS and UAV Applications Research at Shanghai Jiao Tong University has conducted extensive on-road carbon emission monitoring of container trucks in Shanghai using a Portable Emissions Measurement System (PEMS). This work has resulted in the accumulation of a substantial dataset of raw emissions data. Based on these empirical observations, the research team has developed a carbon emission model for container trucks operating under real-world conditions and created a container truck carbon emission calculator. Vehicle Fuel Type: ? Please Select Fuel-Powered Container Truck Natural Gas-Powered Container Truck Emission Standard: ? Please Select China VI Standard Calculation Method: ? Please Select Micro-level Calculation Macro-level Calculation Driving Characteristics: ? Placeholder Placeholder Calculate Note: This result is derived from empirical data fitting and is provided solely for reference. The website assumes no legal liability. For further information, please contact the research team. 上海交通大学 Array 更多教师 Copyright © 2018 All Rights Reserved. 设计 支持 supporter --> Copyright © 2018 All Rights Reserved. 站长统计