報告人：邵虎 教授
報告題目：Transportation Network Modeling: Traffic Flow Estimation and Prediction
報告時間：2026年5月12日（周二）下午4：00
報告地點：云龍校區6號樓318會議室
主辦單位：數學與統計學院、數學研究院、科學技術研究院
報告人簡介：
邵虎，中國礦業大學數學學院，教授，博士，博士生導師，中國礦業大學校學術委員會常委、江蘇省應用數學(中國礦業大學)中心副主任、中國礦業大學數學學科建設與指導委員會主任、數學學院教授委員會主任。全國煤炭行業教學名師、全國大學生數學建模競賽優秀指導教師江蘇省高校優秀共產黨員，江蘇省“青藍工程”優秀教學團隊帶頭人，江蘇省運籌學會副理事長。作為主持人，連續主持5項國家自然科學基金項目(面上4項，青年1項)，主持全國教育規劃重點項目1項，省教改項目3項（含重點項目2項），發表科研論文70余篇，出版第一作者專著1部，主編、參編教材4部，獲得江蘇省教學成果一等獎、教育部自然科學獎二等獎、江蘇省教學創新大賽特等獎、中國礦業大學教學貢獻獎、教學模范等100余項獎勵。主要從事問題驅動型“應用數學”研究，研究方向涉及最優化理論應用、交通網絡建模與算法設計、數據驅動下的網絡建模與算法、機器學習的應用等。
報告摘要：
Traffic demand flow estimation (TDFE) is a critical task in urban transportation planning and management, as it provides a scientific foundation for decision-making in infrastructure construction, public transit optimization, and congestion mitigation. To address this problem, this study comprises three systematic and interconnected research components: (1) the establishment of an observability theory based on graph isomorphism to guide cost-effective data acquisition, (2) the development of a deep learning-based model for accurate and interpretable dynamic TDFE, and (3) the construction of a bilevel origin-destination (OD) demand model along with the design of a corresponding efficient solution algorithm. To overcome the economic constraints of limited sensing resources, this research first establishes a graph theory-based analytical framework for traffic network flow observability. It derives the analytical relationship for the minimum number of observable links and subsequently proposes a resource-constrained sensor deployment optimization model. By quantifying the information loss imposed by budget limitations, the model aims to maximize network flow observability at minimal cost, thereby laying a high-quality data foundation for subsequent analysis. For accurate and interpretable estimation of dynamic traffic demand, a Multi-feature Recurrent Learning Network (MRLN) that integrates physical traffic mechanisms is developed. This model structures key system processes such as trip distribution, route choice, and traffic assignment into interpretable computational units. Through a temporal-recursive and feature-fusion architecture, it achieves high-fidelity inversion of dynamic origin-destination (OD) demand, significantly enhancing both model interpretability and estimation accuracy. Addressing the practical challenge of solving complex models with real-world, multi-source heterogeneous data, a bilevel OD estimation model is constructed. A tailored, efficient heuristic solving algorithm based on the proximal linearized Alternating Direction Method of Multipliers (ADMM) is designed for this model. This algorithm substantially improves computational efficiency and numerical stability in data-sparse and heterogeneous scenarios, ensuring the practical utility of advanced estimation models. The three research components follow a progressive logic: the economical deployment establishes the essential data foundation for precise modeling, which in turn drives the development of robust solving algorithms. This complete technical chain ensures the practical applicability of advanced models, ultimately delivering an implementable framework for TDFE that provides tangible support for decision-making in intelligent transportation systems.