Academic Employment

2019- now Tenure-track Associate Professor at Shanghai Jiao Tong University, China

2015-2018 Associate Professor at Shanghai Maritime University, China

2010-2014 Assistant Professor at Shanghai Maritime University, China

 

Academic Experience

2017-2018 Visiting Associate Professor at Cornell University

2016.7-9 Research fellow at City University of Hong Kong

 

Academic Qualifications

2006-2010 Ph.D at City University of Hong Kong, Hong Kong, China

2003-2006 M.Sc. in Fluid Dynamics at Shanghai University, Shanghai, China

1999-2003 B.Sc. in Mathematics at University of Northwestern Polytechnic, Xi’an, China/p>

 

Research Interests

1  Application of AI in Intelligent Transport System

2  New energy vehicles and energy consumption

3  Transportation, Environment and Community Healthy

4  Application of Unmanned Aerial Vehicle (UAV) in Transportation and Environment

 

Professional Societies and Activities

2024- now Guest Editor of Special Issue titled Digital Twin Applications for Transportation Decarbonization and Air Quality Improvement in Journal of Transportation Research Part D: Transport and Environ

2024- now Young Editorial Board Member of International Journal of Transportation Science and Technology

2024- now Young Editorial Board Member of Journal of Shanghai University

2017-now Chairman of technical committee on traffic pollution in world transport convention (WTC) and organize parallel session in annuls conference since 2017

2024.7.19 Chairman of parallel session titled “Vertical distribution of air pollution using unmanned aerial vehicle” in 29th China Atmospheric Environment Science and Technology Conference

2023.12.15 Chairman of parallel session titled “AI for environmental science” in 2rd Youth Forum of Chinese Environment Science

2023-now Evaluation Expert of Chinese Undergraduate Graduation Thesis

Referee for 20+ journals including Build Environ, Environ Inc, Transp Res part D

 

Representative research projects

[1]. 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.

[2]. 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.

[3]. 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.

[4]. 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.

[5]. 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.

[6]. 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.

[7]. 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.

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

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

[10]. 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.

[11]. 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.

[13]. 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.

[14]. 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.

[15]. 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.

[17]. Port Comprehensive Development Index, sponsored by the Shanghai Transportation and Port Administration Bureau, Co-PI, 2012.

 

Publications

[1].He, H. D*., Lu, D. N., Zhao, H. M., Peng, Z. R. Characterizing CO2 and NOx emission of vehicles crossing toll stations in highway. Transportation Research Part D: Transport and Environment, 2024, 126: 104024.

[2].Huang, H. C., Li, B. W., Wang, Y. Z., Zhang, Z., He, H. D*. Analysis of factors influencing energy consumption of electric vehicles: Statistical, predictive, and causal perspectives. Applied Energy, 2024, 375: 124110.

[3].Huang, H. C., He, H. D*., Peng, Z. R. Urban-scale estimation model of carbon emissions for ride-hailing electric vehicles during operational phase. Energy, 2024, 293: 130665.

[4].Zhang, Z., Qing, Y., Gao, K., 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.

[5].Zhao, H. M., He, H. D*., Lu, D. N., Zhou, D., Lu, C. X., Fang, X. R., Peng, Z. R. Evaluation of CO2 and NOx emissions from container diesel trucks using a portable emissions measurement system. Building and Environment, 2024, 252: 111266.

[6].Jin, M. Y., Gallagher, J., Li, X. B., Lu, K. F., Peng, Z. R*., He, H. D*. Characterizing the distribution pattern of traffic-related air pollutants in near-road neighborhoods. Environmental Monitoring and Assessment, 2024, 196: 767.

[7].Zhang, Z., Gao, K., He, H. D*., Cui, S. H., Hu, L. Y., Yu, Q., Peng, Z. R. Environmental impacts of ridesplitting considering modal substitution and associations with built environment. Transportation Research Part D: Transport and Environment, 2024, 130: 104160.

[8].Jin, M. Y., Zhang, L. Y., Peng, Z. R., He, H. D., Kumar, P., Gallagher, J*. The impact of dynamic traffic and wind conditions on green infrastructure performance to improve local air quality. Science of the Total Environment, 2024, 917: 170211.

[9].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.

[10].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.

[11].Chen, Z. H., Li, B. W., Li, B., 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.

[12].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.

[13].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.

[14].Zhang, Z., Gao, K., He, H. D*., Yang, J. M., Jia, R., Peng, Z. R. How do travel characteristics of ridesplitting affect its benefits in emission reduction? evidence from Chengdu. Transportation Research Part D: Transport and Environment, 2023, 123: 103912.

[15].Fang, X. R., Zhu, X. H., Li, X. Z., Peng, Z. R*., H, Q. Y*., He, H. D., AJ, Y. C., Cheng, H. Assessing the effects of short-term traffic restriction policies on traffic-related air pollutants. Science of The Total Environment, 2023, 867: 161451.

[16].Cen, B. L., Xue, Y*., Qiao, Y. F., Wang, Y., Pan, W., He, H. D. Global stability and bifurcation of macroscopic traffic flow models for upslope and downslope. Nonlinear Dynamics, 2023, 111: 3725-3742.

[17].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.

[18].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.

[19].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.

[20].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.

[21].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, 231: 116200.

[22].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.

[23].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: Statistical Mechanics and its Applications, 2023, 621: 128798.

[24].何红弟*，卢丹妮，赵红梅，城市高架街谷交通污染物的扩散研究综述。上海大学学报（自然科学版），2022, 28: 569-581

[25].Li, B., Cao, R., He, H. D., Peng, Z. R*., Qin, H., Qin, Q. Three-dimensional diffusion patterns of traffic-related air pollutants on the roadside based on unmanned aerial vehicles monitoring. Building and Environment, 2022, 219: 109159.

[26].Lu, K. F., Wang, H. W., Li, X. B., Peng, Z. R*., He, H. D., Wang, Z. P. Assessing the effects of non-local traffic restriction policy on urban air quality. Transport Policy, 2022, 115: 62-74.

[27].Zhu, X. H., He, H. D*., Lu, K. F., Peng, Z. R*., Gao, H. O. Characterizing carbon emissions from China V and China VI gasoline vehicles based on portable emission measurement systems. Journal of Cleaner Production, 2022, 378: 134458.

[28].Li, C., He, H. D*., Peng, Z. R. Spatial distributions of particulate matter in neighborhoods along the highway using unmanned aerial vehicle in Shanghai. Building and Environment, 2022, 211: 108754.

[29].Wu, C. L., He, H. D*., Song, R. F., Peng, Z. R. Prediction of air pollutants on roadside of the elevated roads with combination of pollutants periodicity and deep learning method. Building Environment, 2022, 207: 108436.

[30].Zhang, Z., He, H. D*., Yang, J. M., Wang, H. W., Peng, Z. R. Spatiotemporal evolution of NO2 diffusion in Beijing in response to COVID-19 lockdown using complex network. Chemosphere, 2022, 293: 133631.

[31].Zhao, H. M., He, H. D*., Lu, K. F., Hang, X. L., Ding, Y*., Peng, Z. R. Measuring the impact of an exogenous factor: An exponential smoothing model of the response of shipping to COVID-19. Transport Policy, 2022, 118: 91-100.

[32].Jiang, Y. H., Li, B., He, H. D*., Li, X. B., Wang, D. S., Peng, Z. R. Identification of the atmospheric boundary layer structure through vertical distribution of PM2.5 obtained by unmanned aerial vehicle measurements. Atmospheric Environment, 2022, 278: 119084.

[33].Zhu, X. H., Lu, K. F., Peng, Z. R*., He, H. D*., Xu, S. Q. Spatiotemporal variations of carbon dioxide (CO2) at Urban neighborhood scale: Characterization of distribution patterns and contributions of emission sources. Sustainable Cities and Society, 2022, 78: 103646.

[34].Liu, X., Shi, X. Q., He, H. D*., Peng, Z. R*. Distribution characteristics of submicron particle influenced by vegetation in residential areas using instrumented unmanned aerial vehicle measurements. Sustainable Cities and Society, 2022, 78: 103616.

[35].Liu, R., Wang, F. T., Wang, Z. P., Wu, C. L., He, H. D*. Identification of Subway Track Irregularities Based on Detection Data of Portable Detector. Transportation Research Record, 2022, 2676: 703-713.

[36].He, H. D*., Gao, H. O. Particulate Matter Exposure at a Densely Populated Urban Traffic Intersection and Crosswalk. Environmental Pollution, 2021, 268: 115931 (ESI).

[37].Wu, C. L., Wang, H. W., Cai, W. J., He, H. D*., Ni, A. N., Peng, Z. R. Impact of the COVID-19 lockdown on roadside traffic-related air pollution in Shanghai, China. Building Environment, 2021, 194: 107718.

[38].Cai, W. J., Wang, H. W., Wu, C. L., Lu, K. F., Peng, Z. R*., He, H. D. Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China. Building and environment, 2021, 205: 108231.

[39].Zheng, T., Jia, Y. P., Zhang, S., Li, X. B., Wu, Y., Wu, C. L., He, H. D., Peng, Z. R*. Impacts of vegetation on particle concentrations in roadside environments. Environmental Pollution, 2021, 282: 117067.

[40].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.

[41].Song, R. F., Wang, D. S., Li, X. B., Li, B., Peng, Z. R., He, H. D*. Characterizing vertical distribution patterns of PM2.5 in low troposphere of Shanghai city, China: Implications from the perspective of unmanned aerial vehicle observations. Atmosphere Environment, 2021, 265: 118724.

[42].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.

[43].Tanvir, M. R. A., He, H. D*., Peng, Z. R. Spatio-temporal variability in black carbon concentrations at highway toll plaza: Comparison between manual and electronic toll lanes. Atmospheric Pollution Research, 2021, 12: 286-294.

[44].Jia, Y. P., Lu, K. F., Zheng, T., Li, X. B., Liu, X., Peng, Z. R., He, H. D*. 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, 2021, 12: 1-12.

[45].Yang, J. M., Peng, Z. R*., Lin, L. Real-time spatiotemporal prediction and imputation of traffic status based on LSTM and Graph Laplacian regularized matrix factorization. Transportation Research Part C: Emerging Technologies, 2021, 129: 103228.

[46].Luo, Z. G., Wang, Z. Y., Wang, H. W., He, H. D*., Peng, Z. R. Characterizing spatiotemporal distributions of black carbon and PM2.5 at a toll station: Observations on manual and electronic toll collection lanes. Building Environment, 2021, 199: 107933.

[47].Zheng, T., Wang, H. W., Li, X. B., Peng, Z. R., He, H. D*. Impacts of traffic on roadside particle variations in varied temporal scales. Atmospheric Environment, 2021, 253: 118354.

[48].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.

[49].Xue, Y*., Wang, X., Cen, B. L., Zhang, P., He, H. D. Study on fuel consumption in the Kerner–Klenov–Wolf three-phase cellular automaton traffic flow model. Nonlinear Dynamics, 2020, 102: 393-402.

[50].Gao, Y., Wang, Z., Li, C. Y., Zheng, T., Peng, Z. R*. Assessing neighborhood variations in ozone and PM2.5 concentrations using decision tree method. Building and Environment, 2021, 188: 107479.

[51].Lu, K. F., He, H. D., Wang, H. W., Li, X. B., Peng, Z. R*. Characterizing temporal and vertical distribution patterns of traffic-emitted pollutants near an elevated expressway in urban residential areas. Building Environment, 2020, 172: 106678.

[52].Wang, H. W., Peng, Z. R., Wang, D., Meng, Y., Wu, T., Sun, W*., Lu, Q. C*. Evaluation and prediction of transportation resilience under extreme weather events: A diffusion graph convolutional approach. Transportation Research Part C: Emerging Technologies, 2020, 115: 102619.

[53].Chen, Q., Li, X. B., Song, R. F., Wang, H. W., Li, B., He, H. D*., Peng, Z. R. Development and utilization of hexacopter unmanned aerial vehicle platform to characterize vertical distribution of boundary layer ozone in wintertime. Atmospheric Pollution Research, 2020, 11: 1073-1083.

[54].Lu, K. F., He, H. D., Wang, H. W., Li, X. B., Peng, Z. R*. Characterizing temporal and vertical distribution patterns of traffic-emitted pollutants near an elevated expressway in urban residential areas. Building Environment, 2020, 172: 106678.

[55].Wang, H. W., Li, X. B., Wang, D. S., Zhao, J., He, H. D*., Peng, Z. R. Regional prediction of ground-level ozone using a hybrid sequence-to-sequence deep learning approach. Journal of Cleaner Production, 2020, 253: 119841.

[56].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, 222: 117134.

[57].Xue, Y*., Zhang, Y., Fan, D., Zhang, P., He, H. D. An extended macroscopic model for traffic flow on curved road and its numerical simulation. Nonlinear Dynamics, 2019, 95: 3295-3307.

[58].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.

[59].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: Transport and Environment, 2018, 60: 76-84.

[60].Wang, Z*., Zhong, S., Peng, Z. R., Cai, M**. Fine-scale variations in PM2.5 and black carbon concentrations and corresponding influential factors at an urban road intersection. Building and Environment, 2018, 141: 215-225.

[61].He, H. D*., Shi, W., Lu, W. Z*. Investigation of exhaust gas dispersion in the near-wake region of a light-duty vehicle. Stochastic Environmental Research and Risk Assessment, 2017, 31: 775-783.

[62].He, H. D*., Qiao, Z. X., Pan, W., Lu, W. Z. Multiscale multifractal properties between ground-level ozone and its precursors in rural area in Hong Kong. Journal of environmental management, 2017, 196: 270-277.

[63].He, H. D*. Multifractal analysis of interactive patterns between meteorological factors and pollutants in urban and rural areas. Atmospheric Environment, 2017, 149: 47-54.

[64].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.

[65].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: Statistical Mechanics and Its Applications, 2016, 443: 518-525.

[66].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.

[67].He, H. D., Lu, W. Z*., Xue, Y. Prediction of particulate matter at street level using artificial neural networks coupling with chaotic particle swarm optimization algorithm. Building and Environment, 2014, 78: 111-117.

[68].He, H. D., Lu, W. Z*. Spectral analysis of vehicle pollutants at traffic intersection in Hong Kong. Stochastic Environmental Research and Risk Assessment, 2012, 26: 1053-1061.

[69].He, H. D., Lu, W. Z*. Decomposition of Pollution Contributors to Urban Ozone Levels Concerning Regional and Local Scales. Building Environment, 2012, 49: 97-103.

[70].He, H. D., Lu, W. Z*. Urban Aerosol Particulates on Hong Kong roadsides: Size Distribution and Concentration Levels with Time. Stochastic Environmental Research and Risk Assessment, 2012, 26: 177-187.

[71].He, H. D., Lu, W. Z*., Dong, L. Y. An Improved Cellular Automaton Model Considering Effect of Traffic Lights and Driving Behavior. Chinese Physics B, 2011, 20: 040514.

[72].He, H. D., Lu, W. Z*., Dong, L. Y. Jam formation of traffic flow in harbor tunnel. Communications in Theoretical Physics, 2011, 56: 1140.

[73].Lu, W. Z*., He, H. D., Leung, A. Y. Leung, Assessing air quality in Hong Kong: A proposed, revised air pollution index (API). Building Environment, 2011, 46: 2562-2569.

[74].Lu, W. Z*., He, H. D., Dong, L. Y. Performance assessment of air quality monitoring networks using principal component analysis and cluster analysis. Building Environment, 2011, 46: 577-583.

[75].He, H. D., Lu, W. Z*., Xue, Y. Prediction of PM10 concentrations at urban traffic intersections using semi-empirical box modelling with instantaneous velocity and acceleration. Atmospheric Environment, 2009, 43: 6336-6342.

[76].He, H. D., Lu, W. Z*., Xue, Y., Dong, L. Y. Dynamic characteristics and simulation of traffic flow with slope. Chinese Physics B, 2009, 18: 2703-2708.