activity
20242026
most citedUnraveling stochastic fundamental diagrams considering empirical knowledge: modeling, limitation and further discussion

6 citations · 8 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI2026

Customized Generative AI Agent for Transportation Engineering Practice: A Development and Continued Pre-training Guideline

Dianwei Chen, Yuan-Zheng Lei, Zifan Zhang +2

Recent advancements in generative artificial intelligence (AI) and large language models (LLMs) have shown significant promise in automating complex reasoning, summarization, and q…

cs.ET2026

Improving Feasibility in Quantum Approximate Optimization Algorithm for Vehicle Routing via Constraint-Aware Initialization and Hybrid XY-X Mixing

Yuan-Zheng Lei, Yaobang Gong, Xianfeng Terry Yang +1

The Quantum Approximate Optimization Algorithm (QAOA) is a leading framework for quantum combinatorial optimization. The Vehicle Routing Problem (VRP), a core problem in logistics…

cs.LG2025★ 2 cited

Reconstructing Physics-Informed Machine Learning for Traffic Flow Modeling: a Multi-Gradient Descent and Pareto Learning Approach

Yuan-Zheng Lei, Yaobang Gong, Dianwei Chen +2

Physics-informed machine learning (PIML) is crucial in modern traffic flow modeling because it combines the benefits of both physics-based and data-driven approaches. In convention…

cs.LG2025

Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis

Yuan-Zheng Lei, Yaobang Gong, Dianwei Chen +2

This study investigates why physics-informed machine learning (PIML) can fail in macroscopic traffic flow modeling. We define failure as cases where a PIML model underperforms both…

math.OC2025

A Scalable Min-Max Multi-Gradient Descent Method for Multi-Objective Transportation Problems

Yuan-Zheng Lei, Yaobang Gong, Xianfeng Terry Yang

This paper develops a scalable min-max multi-gradient descent framework for multi-objective transportation problems with competing objectives and high-dimensional constraints. Unli…

stat.AP2024★ 6 cited

Unraveling stochastic fundamental diagrams considering empirical knowledge: modeling, limitation and further discussion

Yuan-Zheng Lei, Yaobang Gong, Xianfeng Terry Yang

Traffic flow modeling relies heavily on fundamental diagrams. However, deterministic fundamental diagrams, such as single or multi-regime models, cannot capture the uncertainty pat…