Hua Hua Zhu

Hua Hua Zhu

PhD @ Indiana University Bloomington

About Me

I am a PhD Candidate in Economics at Indiana University Bloomington.

My research lies at the intersection of transportation economics and operations research, focusing on how agents make stochastic decisions under uncertainty, especially with variable travel times and imperfect perceptions. In applied work, I study policy interventions such as congestion pricing to mitigate network externalities and enhance efficiency, using data-driven traffic equilibrium models to provide quantitative evidence for policy analysis.

Methodologically, I develop behavior-based traffic equilibrium models that incorporate risk preferences and distributional uncertainty. My theoretical work in spatial economics examines commuting and location choices through general equilibrium frameworks, with practical implications for public transit management, infrastructure investment, and spatial pricing strategies.

In teaching, I employ case-based learning and interactive simulations to break down complex economic concepts, fostering analytical clarity, critical thinking, and confidence in students. I am developing a course titled “Applications of Behavior-Based Stochastic Choice Models in Operations and Supply Chain Management” to integrate theory, case studies, and computational tools while emphasizing inclusive and equitable teaching practices.

Hua Hua Zhu

Hua Hua Zhu

PhD @ Indiana University Bloomington

Research

Research Interesting

My research interests lie in Transportation & Network Economics, Behavioral Economics, and Applied Algorithmic Game Theory. I am particularly interested in studying the network effects of transportation policy interventions through behavioral traffic equilibrium models.

Research Papers

The Transportation Network Efficiency Effects of Congestion Pricing (Job Market Paper)
Abstract: This paper develops a novel policy analysis framework built on Perturbed User and System Optimization, which formalize the Perturbed Traffic Equilibrium and Optimum. The framework establishes the existence and uniqueness of both equilibrium and optimum conditions ... show more Abstract: This paper develops a novel policy analysis framework built on Perturbed User and System Optimization, which formalize the Perturbed Traffic Equilibrium and Optimum. The framework establishes the existence and uniqueness of both equilibrium and optimum conditions, providing a rigorous theoretical foundation for evaluating policy interventions. In the real-world numerical experiment, this paper employs a data-driven inverse optimization approach to estimate structural parameters, yielding mode-specific travel cost functions. Comparative analysis reveals that congestion pricing does not eliminate the primary bottleneck in the network but reshapes the spatial distribution of mode-specific travel demand across origins and induces the mode-transfer dynamics at intermediate nodes. Additionally, counterintuitive substitution and complementarity patterns between vehicles and public transit reduce the network-wide congestion externalities. Vehicle travel times, travel costs, and gasoline consumption decrease by 15.23%, 14.34%, and 30.74%, respectively, compared with larger reductions of 38.65%, 37.90%, and 41.27% observed on the main entry links, resulting in a modest 0.4% improvement in overall network efficiency. Sensitivity analysis further indicates that while economic revenues continue to grow substantially, marginal network efficiency gains diminish as congestion pricing levels increase. These findings provide quantitative evidence to guide the design of urban mobility markets and effective transportation policies. show less
Stochastic Traffic Equilibria under Risk Aversion and Distributionally Robust Optimization
Abstract: This paper develops novel behaviorally grounded models of stochastic traffic equilibrium that capture travelers’ attitudes toward both risk and ambiguity. Moving beyond the standard risk-neutral framework, we first propose a Risk-Averse Stochastic User Equilibrium (RA-SUE) model, where ... show more Abstract: This paper develops novel behaviorally grounded models of stochastic traffic equilibrium that capture travelers’ attitudes toward both risk and ambiguity. Moving beyond the standard risk-neutral framework, we first propose a Risk-Averse Stochastic User Equilibrium (RA-SUE) model, where travelers minimize perceived disutility by considering both mean travel time and its variability. To further account for ambiguity aversion—sensitivity to distributional uncertainty—we introduce a Distributionally Robust RA-SUE (DRO-RA-SUE) model, formulated using a ϕ-divergence-based ambiguity set over perception errors. We establish theoretical properties of existence and uniqueness and develop efficient algorithms to compute equilibria. These models provide a richer behavioral foundation for analyzing travelers’ path choices under uncertainty and offer practical tools for policy evaluation. Numerical experiments reveal key insights: under RA-SUE, both public transit and new link interventions consistently induce a Braess-like paradox, with system costs changing monotonically with risk aversion. Under DRO-RA-SUE, ambiguity aversion alleviates the paradox for public transit but not for the new link, where system performance deteriorates in a non-monotonic manner. These findings highlight how accounting for risk and ambiguity aversion is critical to accurately assessing transportation policies under uncertainty. show less
A Perturbed Quantitative Spatial Equilibrium Model with Optimal Location and Commuting Decisions
Abstract: This paper consider a nonatomic, selfish spatial location and commuting choice game within multi-commodity and multi-class transportation networks, where rational agents balance agglomeration and dispersion forces to make their optimal spatial ... show more Abstract: This paper consider a nonatomic, selfish spatial location and commuting choice game within multi-commodity and multi-class transportation networks, where rational agents balance agglomeration and dispersion forces to make their optimal spatial location and commuting choices. To address the limitations of existing quantitative spatial models and bridge the gap between transportation literature and spatial economics, this paper utilizes a perturbed utility model within a general spatial equilibrium framework to account for unobservable heterogeneity. This paper establishes existence and uniqueness properties of the perturbed spatial equilibrium, and derives its closed-form expression. The results of a numerical experiment suggest that improvements in transportation infrastructure alter the spatial distribution of traffic flows and economic activity. Moreover, the simulated elasticities of spatial equilibrium outcomes highlight the critical role of carefully targeted policy interventions. show less

Work in Progress

Monotone Stochastic Route Choice Models: An Additive Risk-Disutility Framework for Travel under Uncertainty, with Emerson C. Melo
Impacts of Autonomous Vehicle Deployment on Congestion Externalities in Mixed Traffic Networks with Human Learning, with Rodney Parker

Selected Refereed Publications before PhD

Huahua Zhu, Huaqing Wang (2011). Game Analysis on Pollutant Discharge Behavior of Coal Enterprises under Low-carbon Economy. Ecological Economy, Vol.10: pp.304-306.
Huaqing Wang, Longfei Zhao, Huahua Zhu (2011). Game model of vertical cooperative advertising under asymmetric channel power. International Conference on E-Business and E-Government (ICEE).
Hua Hua Zhu

Hua Hua Zhu

PhD @ Indiana University Bloomington

Teaching Experience

Indiana University Bloomington

Associate Instructor (Full teaching responsibilities)

  • FUND OF ECON FOR BUSINESS I and II (B251 & B252)— Spring 2023, Fall 2023, Spring 2024, Summer 2024, Fall 2024
  • FUNDAMENTALS OF ECONOMICS I (E251)— Fall 2025
  • INTRO TO MACROECONOMICS (E202)— Spring 2022

Chongqing Technology and Business University

Counselor and Lecturer (July 2015 – November 2020)

  • Marketing Research and Analysis
  • Consumer Behavior
  • Business Model Design and Innovation
  • Game Theory (partial teaching responsibilities)