一、基本情况

姓名：李洪涛                                       职称：教授

学历：研究生                                              学位：博士

通信地址：兰州交通大学                         邮编：730070

电子邮箱：liht@mail.lzjtu.cn

二、教育背景

2010年12月获兰州大学理学博士学位

三、工作经历

2021年12月兰州交通大学 教授、博士生导师

2019年12月兰州交通大学 教授

四、研究方向

综合交通运输理论与技术、运输组织理论与技术、机器学习与大数据分析、智能决策、预测理论与方法

五、招生专业

硕士研究生：交通运输规划与管理，管理科学与工程，交通运输

博士研究生：管理科学与工程

六、科研项目

[1]国家自然科学基金项目：基于多源信息数据驱动的综合集成高速铁路客流需求预测方法研究(72161022)，2022.1-2025.12，主持

[2]国家自然科学基金项目：二阶宏观交通流模型的动力学行为(11361031)，2014.1-2017.12，主持

[3]甘肃省自然科学基金项目：基于人工智能的碳排放交易价格预测模型研究(20JR5RA394)，2020.11-2022.10，主持

[4]甘肃省高等学校科学研究项目：二阶交通流模型全局吸引子的存在性(2013A-045)，2013.1-2016.6，主持

[5]甘肃省财政厅基本科研业务费项目子课题项目：不确定环境下铁路集装箱空箱多阶段动态调运优化研究(213060-2)，主持

[6]兰州交通大学-天津大学创新基金项目：基于成网条件下的中西部地区铁路货运预测研究(2018064)，2018.6-2021.6，主持

[7]兰州交通大学校青年基金项目：道路交通流的吸引性研究(2013034)，2014.1-2016.12，主持

[8]兰州交通大学基础研究拔尖人才培养计划项目：基于机器学习和大数据驱动的交通出行需求预测与分析研究(2022JC12)，2023.01-2026.12，主持

[9]国家自然科学基金项目：网络条件下高速铁路动车组运用优化理论与方法研究(71761023)，2018.1-2021.12，参加

[10]教育部人文社会科学研究项目：“一带一路”发展战略下的西部地区多式联运网络协同优化机理研究，2015.9-2018.9，参加

七、教学工作

1. 本科生《管理运筹学》，

2. 研究生《最优化理论与方法》

     兰州交通大学青年教师教学/科研导师

八、期刊论文

[1] Li, Hongtao; Fu, Wenjie∗; Zhang, Haina; Liu, Wenzheng; Sun, Shaolong; Zhang, Tao. Spatiotemporal graph hierarchical learning framework for metro passenger flow prediction across stations and lines. Knowledge-based Systems, 2025, 311(113132): 113-132. (SCI)

[2] Wenzheng Liu, Hongtao Li∗, Haina Zhang, Jiang Xue, Shaolong Sun. Dynamic Spatio-Temporal Graph Fusion Network modeling for urban metro ridership prediction. Information Fusion 117 (2025) 102845. (SCI)

[3] Ruihang Xie, Haina Zhang, Hongtao Li∗, Wenzheng Liu, Shaolong Sun, Tao Zhang. High-speed rail passenger flow prediction based on crossformer and quantile regression: A deep learning approach assisted by internet search indices. Measurement 242 (2025) 116189. (SCI)

[4] Gao, Xinyu; Li, Hongtao∗; Zhang, Haina; Xue, Jiang; Sun, Shaolong; Liu, Wenzheng. Trafficprediction based on spatial-temporal disentangled generative models. Information Sciences, 2024,680: 121-142. (SCI)

[5] Hongtao Li, Xiaoxuan Li∗, Shaolong Sun, Zhipeng Huang, Xiaoyan Jia. Multivariable forecasting approach of high-speed railway passenger demand based on residual term of Baidu search index and error correction. Journal of Forecasting. 2024;1–33. (SCI)

[6] Duo Chen, Hongtao Li∗, Shaolong Sun, Juncheng Bai, Zhipeng Huang. Point and interval forecasting approach for short-term urban subway passenger flow based on residual term decomposition and fuzzy information granulation. Applied Soft Computing, 2024, 166: 112187. (SCI)

[7] Yifan Cheng, Hongtao Li∗, Shaolong Sun, Wenzheng Liu, Xiaoyan Jia, Yang Yu. Short-term subway passenger flow forecasting approach based on multi-source data fusion. Information Sciences, 2024, 679: 121109. (SCI)

[8] Hongtao Li, Yang Yu∗, Zhipeng Huang, Shaolong Sun, Xiaoyan Jia. A multi-step ahead point-interval forecasting system for hourly PM2.5 concentrations based on multivariate decomposition and kernel density estimation. Expert Systems with Applications 226 (2023) 120140. (SCI)

[9] Shaolong Sun*, Zongjuan Du, Kun Jin, Hongtao Li, Shouyang Wang. Spatiotemporal wind power forecasting approach based on multi-factor extraction method and an indirect strategy. Applied Energy 350 (2023) 121749. (SCI)

[10] Hongtao Li, Kun Jin*, Shaolong Sun, Xiaoyan Jia, Yongwu Li. Metro passenger flow forecasting though multi-source time-series fusion: An ensemble deep learning approach. Applied Soft Computing, 120 (2022) 108644. (SCI)

[11] Yang Yu, Hongtao Li*, Shaolong Sun, Yongwu Li. PM2.5 concentration forecasting through a novel multi-scale ensemble learning approach considering intercity synergy. Sustainable Cities and Society 85 (2022) 104049. (SCI)

[12] Bingzhen Sun, Juncheng Bai, Xiaoli Chu, Shaolong Sun, Yongwu Li, Hongtao Li*. Interval prediction approach to crude oil price based on three-way clustering and decomposition ensemble learning. Applied Soft Computing 123 (2022) 108933. (SCI)

[13] Kun Jin, Shaolong Sun, Hongtao Li*, Fengting Zhang. A novel multi-modal analysis model with Baidu Search Index for subway passenger flow forecasting. Engineering Applications of Artificial Intelligence 107 (2022) 104518. (SCI)

[14] Juncheng Bai, Bingzhen Sun∗, Xiaoli Chu, Ting Wang, Hongtao Li, Qingchun Huang. Neighborhood rough set-based multi-attribute prediction approach and its application of gout patients. Applied Soft Computing, 2022, 114: 108127. (SCI)

[15] Wenxiu Cao, Shaolong Sun, Hongtao Li*. A new forecasting system for high-speed railway passenger demand based on residual component disposing. Measurement 183 (2021) 109762. (SCI)

[16] Hongtao Li, Feng Jin, Shaolong Sun*, Yongwu Li. A new secondary decomposition ensemble learning approach for carbon price forecasting. Knowledge-Based Systems 214 (2021) 106686. (SCI)

[17] Shaolong Sun, Feng Jin, Hongtao Li*, Yongwu Li. A new hybrid optimization ensemble learning approach for carbon price forecasting. Applied Mathematical Modelling 97 (2021) 182-205. (SCI)

[18] Hongtao Li, Juncheng Bai, Xiang Cui, Yongwu Li, Shaolong Sun*. A new secondary decomposition-ensemble approach with cuckoo search optimization for air cargo forecasting. Applied Soft Computing Journal 90 (2020) 106161. (SCI)

[19] Feng Jin, Yongwu Li, Shaolong Sun, Hongtao Li*. Forecasting air passenger demand with a new hybrid ensemble approach. Journal of Air Transport Management 83 (2020) 101744. (SSCI)

[20] Sibao Fu, Yongwu Li*, Shaolong Sun, Hongtao Li. Evolutionary support vector machine for RMB exchange rate forecasting. Physica A: Statistical Mechanics and its Applications, 2019, 521: 692-704. (SCI)

[21] Hongtao Li, Juncheng Bai, Yongwu Li*. A novel secondary decomposition learning paradigm with kernel extreme learning machine for multi-step forecasting of container throughput. Physica A 534 (2019) 122025. (SCI)

[22] Hongtao Li, Xiaojuan Chai*. A two-obstacle problem with variable exponent and measure data. Turkish Journal of Mathematics (2017) 41:717-724. (SCI)

[23] Hongtao Li, Shan Ma* and Chengkui Zhong. Long-time behavior for a class of degenerate parabolic equations. Discrete and Continuous Dynamical Systems, Volume 34, Number 7, July 2014 pp. 2873-2892. (SCI)

[24] Hongtao Li and Shan Ma*. Asymptotic behavior of a class of degenerate parabolic equations. Abstract and Applied Analysis, Volume 2012, Article ID 673605, 15 pages. (SCI)

[25] Shan Ma, Hongtao Li. The long-time behavior of weighted p-Laplacian equations, Topological Methods in Nonlinear Analysis. (2019). DOI: 10.12775/TMNA.2019.064. (SCI)

[26] Shan Ma, Hongtao Li*. Global attractors for weighted P-Laplacian equations with boundary degeneracy. Journal of Mathematical Physics 53, 012701 (2012). (SCI)

[27] Weisheng Niu and Hongtao Li. Asymptotic behavior of approximated solutions to parabolic equations with irregular data. Abstract and Applied Analysis. Hindawi Publishing Corporation, 2012, 2012(1): 312536. (SCI)

[28] Shan Ma*, Xiyou Cheng, Hongtao Li. Attractors for non-autonomous wave equations with a new class of external forces. Journal of Mathematical Analysis and Applications. 337 (2008) 808–820. (SCI)

九、获奖荣誉

2023年获得兰州交通大学“科研先进个人”

2022年获得“来华留学生教学先进个人”

2022年获得兰州交通大学“优秀研究生指导教师”

2018年获得兰州交通大学“优秀研究生任课教师”

2018年获得兰州交通大学课程教学质量评价：优秀

2013年获得兰州交通大学 “教学优秀奖”

十、社会兼职

兰州交通大学学报编委

系统工程学会交通运输分委员会委员

教育部学位委员会评审专家

国家自然科学基金委评审专家