22 citations · 28 across the 7 of their papers we have counts for
9 papers
A Bayesian Framework for Evaluating Scenario Compatibility in Generative Population Synthesis
Zhenlin Qin, Leizhen Wang, Yancheng Ling +1
Scenario-based transportation analysis specifies future assumptions through aggregate population targets, whereas generative population synthesis models produce detailed individual…
BoostTaxo: Zero-Shot Taxonomy Induction via Boosting-Style Agentic Reasoning and Constraint-Aware Calibration
Yancheng Ling, Zhenlin Qin, Leizhen Wang +1
Taxonomy induction is crucial for organizing concepts into explicit and interpretable semantic hierarchies. While existing methods have achieved promising results, their generaliza…
SemaPop: Semantic-Persona Conditioned and Controllable Population Synthesis
Zhenlin Qin, Yancheng Ling, Leizhen Wang +2
Population synthesis is essential for individual-level simulation in transport planning and socio-economic analysis, yet remains challenging due to the need to capture both statist…
Group Effect Enhanced Generative Adversarial Imitation Learning for Individual Travel Behavior Modeling under Incentives
Yuanyuan Wu, Zhenlin Qin, Leizhen Wang +2
Understanding and modeling individual travel behavior responses is crucial for urban mobility regulation and policy evaluation. The Markov decision process (MDP) provides a structu…
EvolveSignal: A Large Language Model Powered Coding Agent for Discovering Traffic Signal Control Strategies
Leizhen Wang, Peibo Duan, Hao Wang +4
In traffic engineering, fixed-time traffic signal control remains widely used for its low cost, stability, and interpretability. However, its design relies on hand-crafted formulas…
Scalable and Reliable Multi-agent Reinforcement Learning for Traffic Assignment
Leizhen Wang, Peibo Duan, Cheng Lyu +4
The evolution of metropolitan cities and the increase in travel demands impose stringent requirements on traffic assignment methods. Multi-agent reinforcement learning (MARL) appro…
Reinforcement Learning-based Sequential Route Recommendation for System-Optimal Traffic Assignment
Leizhen Wang, Peibo Duan, Cheng Lyu +1
Modern navigation systems and shared mobility platforms increasingly rely on personalized route recommendations to improve individual travel experience and operational efficiency.…
A Foundational Individual Mobility Prediction Model based on Open-Source Large Language Models
Zhenlin Qin, Leizhen Wang, Yancheng Ling +2
Individual mobility prediction plays a key role in urban transport, enabling personalized service recommendations and effective travel management. It is widely modeled by data-driv…
AI-Driven Day-to-Day Route Choice
Leizhen Wang, Peibo Duan, Zhengbing He +5
Understanding travelers' route choices can help policymakers devise optimal operational and planning strategies for both normal and abnormal circumstances. However, existing choice…