Zheyong Bian

Assistant Professor

University of Houston

United States

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About

Zheyong Bian is an Assistant Professor at the University of Houston, USA. His research areas focus on optimization frameworks in transportation systems, statistical analysis of train operations, and the integration of machine learning with spatial statistics. Recent articles include studies on the risks associated with rail transport and the optimization of multimodal transport under various market conditions.

Articles (13)

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2026

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2025

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2025

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2024

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2023

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2023

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2023

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2023

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2023

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2022

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2022

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2022

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2022

Collaborators (1)

Kailai Wang

University of Houston

UNITED STATES

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Zheyong Bian

Research Interests

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Articles (13)

An optimization framework for mechanism design in the digital sharing economy

Real-time re-optimization for generalized ridesharing feeder service with mixed scheduled and on-demand riders

Quantifying the Influence of Tank Car Position and Train Configuration on the Risk of Rail Transport of Class 3 Flammable Liquids

Statistical Analysis of Train Derailment Severity for Unit Trains Versus Manifest Trains

Integrating spatial statistics and machine learning to identify relationships between e-commerce and distribution facilities in Texas, US

Probabilistic risk analysis of unit trains versus manifest trains for transporting hazardous materials

Self-adaptive heuristic algorithms for the dynamic and stochastic orienteering problem in autonomous transportation system

Subsidy policy optimization of multimodal transport on emission reduction considering carrier pricing game and shipping resilience: A case study of Shanghai port

Trajectory-based conflict investigations involving two-wheelers and cars at non-signalized intersections with computer vision

Assessing the role of shared mobility services in reducing travel-related greenhouse gases (GHGs) emissions: Focusing on America’s young adults

A machine learning based methodology for broken rail prediction on freight railroads: A case study in the United States

An Empirical analysis of freight train derailment rates for unit trains and manifest trains

An online hybrid mechanism for dynamic first-mile ridesharing service

Collaborators (1)

Kailai Wang

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