教师名录

何红弟

交通运输工程系

电子邮件：hongdihe[at]sjtu.edu.cn
通讯地址：上海市闵行区东川路800号木兰船建大楼A311室
个人主页：https://uav.sjtu.edu.cn/

个人简历研究方向学术兼职科研项目代表性论文及专著教学工作软件版权登记及专利荣誉和奖励

【教育背景】
2006.9-2010.7 香港城市大学建筑学与土木工程系, 博士
2003.9-2006.7 上海大学上海市应用数学和力学研究所，流体力学，硕士
1999.9-2003.7 西北工业大学应用数学系，信息与计算科学，学士

【工作经历】
2019.02-至今上海交通大学船建学院交通运输工程系长聘教规副教授（博导）
2010.10-2019.01 上海海事大学物流研究中心讲师副教授
2017.09-2018.11 美国康乃尔大学访问学者
2016.07-2016.09 香港城市大学 Research Fellow

1 人工智能与智能交通 (Application of AI in Intelligent Transport System)

2 交通、环境与健康研究 (Transportation, Environment and Community Healthy)

3 无人机在交通及其环境中的应用研究(Application of Unmanned Aerial Vehicle (UAV) in Transportation and Environment)

(Students who are interested in joining our group for Master or Ph.D program are all welcome to contact me via hongdihe@sjtu.edu.cn. Preferred background: Transportation, Environment, Civil Engineering, Mathematics, Computer Science and other related major)

2021.05-至今《上海大学学报（自然科学版）》青年编委

2017.08-至今世界交通运输大会交叉学部交通污染技术委员会主席

2016.09-至今上海市力学学会交通流动力学与数据科学专业委员会委员

2014.06-至今交通科学与计算专题研讨会组委会成员

国家自然科学基金面上项目，基于垂直监测的城市高架交通排放物的三维扩散机理研究，2021-2024，项目负责人
2023年上海市人民政府决策咨询研究项目，中国邮轮全产业链生态体系建设研究，2023-2024，项目负责人
上海市2023年度“科技创新行动计划”软科学研究项目, 上海新能源车服务产业的数据基础制度建设路径研究, 2023-2024, 项目负责人
2021年度上海市人民政府决策咨询研究重点课题，科技创新赋能上海碳达峰碳中和目标的路径与对策研究，2021-2022，项目负责人
上海交通大学-康奈尔大学合作项目：Spatiotemporal Distributions of Traffic-related Carbon Emission in Near-road Neighbourhoods，2022-2023，项目负责人
上海交通大学-大阪大学合作项目：Assessment of mobility as a service (MaaS) in sustainable development，2019-2020，项目负责人
国家自然科学基金面上项目，交通拥堵产生的超细颗粒物的动态分布及控制策略研究，2017-2020，项目负责人
国家自然科学基金青年项目，基于颗粒物减排的城市道路交叉口交通流的动力学建模与优化，2014-2016，项目负责人
上海市浦江人才计划项目，上海城市车辆流的动态优化与可吸入细颗粒物的污染控制，2012-2014，项目负责人
上海市科委项目，港口细颗粒物排放特征及对区域空气质量的影响，2014-2016，项目负责人
中国科技部国家重点研发计划子课题，基于无人机和大载荷气艇的大气垂直结构探测技术，2016-2020，项目参与人
国家社会科学基金重大项目，城市交通政策和设施建设对大气环境影响的评价研究，2016-2020，项目参与人
上海市环境保护局，上海市智能环保综合决策与大数据应用研究，2018-2019，项目参与人
中国交通运输部，基于船舶运动轨迹的船舶能耗和碳排放统计算法研究，2014-2016，项目参与人
上海市交通运输与港口管理局，港口综合发展指数，2012，项目参与人

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.

[2]. Huang, HC, He, HD*, Peng, ZR. Urban-scale estimation model of carbon emissions for ride-hailing electric vehicles during operational phase. Energy, 2024, 293: 130665.

[3]. 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.

[4]. 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.

2023

[5]. 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.

[6]. 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.

[7]. 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.

[8]. 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.

[9]. 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.

[10]. 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.

[11]. 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.

[12]. 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.

2022

[13]. 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.

[14]. 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.

[15]. 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.

[16]. 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.

[17]. 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.

[18]. 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, 119084.

[19]. 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.

[20]. 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.

[21]. 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. 03611981221097088.

2021

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

[23]. 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.

[24]. 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.

[25]. 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.

[26]. 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.

[27]. 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.

[28]. 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.

[29]. 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.

[30]. 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.

2020

[31]. 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.

[32]. 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.

[33]. 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, 106678.

[34]. 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, 253:19841.1

[35]. 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.

2019及以前

[36]. 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.

[37]. 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.

[38]. 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.

[39]. 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.

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

[41]. 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.

[42]. 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.

[43]. 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.

[44]. 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.

[45]. 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.

[46]. 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.

[47]. 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.

[48]. 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.

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

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

[51]. 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.

[52]. 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.

[53]. 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.

【承担课程】

本科生课程《运筹学》（校级课程思政示范课程）

研究生课程《交通环境工程》

研究生课程《交通统计分析与建模》

研究生课程《定量分析：模型与方法》

【教学改革项目】

上海交通大学本科课程思政示范课程培育项目，《运筹学》，2022-2023，项目负责人（结题优秀）

上海交通大学教学发展基金：交通强国战略下面向交通运输专业《运筹学》课程的实践教学研究，2021-2022，项目负责人（结题优秀）

上海交通大学教学发展专项基金：互动媒体教学环境中学习主动性的提升策略探究-以《运筹学》为例，2020，项目负责人

【教学论文】

何红弟，王梓烨，吴翠林。线上与线下学习效果的评价对比及对策研究-以《运筹学》为例。中国教育信息化，2022，28（04）：87-92.

【教学奖励】

《校园防疫案例融入《运筹学》思政教学中的设计与探索》，第二届交通运输类专业课程思政教学二等奖，2023

《依托国家物流设计大赛，培养大学生综合实践与科学研究能力》，上海市2013年度教学成果二等奖

《面向交通强国战略构建纵向贯通横向联动的实践育人体系-培养创新型交通人才》，上海交通大学2021年度教学成果奖一等奖

《师德融通-专业贯通-评价联通：交通运输专业课程思政育人模式探索与实践》，上海交通大学2023年度教学成果奖二等奖

【教学创新】

2022年3月，何红弟老师归纳校园防疫中的典型案例，设计出校园防疫背景下运筹学的运输问题、背包问题、组合优化问题等教学内容，并将志愿者的奉献精神、科学家的探究精神、全体师生的人类命运共同体精神等思政元素融入其中，实现了《运筹学》专业知识教学与思政教育的深层次融合。

该创新的教学方式获得学生普遍认可，学生普遍反馈他们不仅学到运筹学的理论知识和方法，也学到了如何利用运筹学知识解决实际问题。同时，也获得了学校教学发展中心的认可，受邀在学校教与学论坛上进行汇报，也被中国运筹学学会进行报道，先后被澎湃新闻网、上海教育电视台等进行视频采访，被人民日报、新华网、解放日报、新民晚报、上海科技网、上海教育新闻网等数十家媒体进行报道。