activity
20242026
most citedAI-Driven Day-to-Day Route Choice

22 citations · 28 across the 8 of their papers we have counts for

collaborators

11 papers

math.DG2026

On Critical Dimensions for Compactness in the Boundary Yamabe Problem, II

Liuwei Gong, Seunghyeok Kim, Monica Musso +1

We determine the sharp compactness ranges for the scalar-flat and minimal-boundary Yamabe problems on smooth compact manifolds of positive conformal type, excluding the conformal r…

cs.LG2026

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…

cs.CL2026

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…

cs.AI2026

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…

cs.LG2025

A Comprehensive Evaluation Framework for Synthetic Trip Data Generation in Public Transport

Yuanyuan Wu, Zhenlin Qin, Zhenliang Ma

Synthetic data offers a promising solution to the privacy and accessibility challenges of using smart card data in public transport research. Despite rapid progress in generative m…

cs.LG2025

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…

cs.LG2025

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…

cs.AI2025

Chat2SPaT: A Large Language Model Based Tool for Automating Traffic Signal Control Plan Management

Yue Wang, Miao Zhou, Guijing Huang +3

Pre-timed traffic signal control, commonly used for operating signalized intersections and coordinated arterials, requires tedious manual work for signaling plan creating and updat…

cs.LG2025★ 6 cited

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…

cs.AI2025

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.…

cs.LG2024★ 22 cited

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…