Kaidi Yang

Assistant Professor

National University of Singapore (NUS)

Singapore

Research Interests

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Kaidi Yang is an Assistant Professor at the National University of Singapore. His research areas include traffic flow modeling, autonomous vehicle performance, and big data analysis in transportation systems. Recent articles authored by Professor Yang cover topics such as stochastic fundamental diagrams, real-time traffic signal optimization, and safe motion planning in autonomous driving.

Articles (19)

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2021

Collaborators (3)

Zhiyun Deng

University of Texas at Austin

UNITED STATES

View details

Alexander Genser

AUSTRIA

View details

Monica Menendez

New York University

UNITED STATES

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Kaidi Yang

Research Interests

Contact this professor

About

Articles (19)

Stochastic fundamental diagram modeling of mixed traffic flow: A data-driven approach

Investigating relationships between ridesourcing and public transit using big data analysis and nonlinear machine learning: A case study of Shanghai, China

Enhancing High-Speed Cruising Performance of Autonomous Vehicles Through Integrated Deep Reinforcement Learning Framework

Toward real-time operations of modular-vehicle transit services: From rolling horizon control to learning-based approach

A Connected Vehicle-Based Contextual Stochastic Optimization Model for Real-Time Traffic Signal Timing

Bi-Level Control of Weaving Sections in Mixed Traffic Environments With Connected and Automated Vehicles

Connected Vehicle Data-Driven Robust Optimization for Traffic Signal Timing: Modeling Traffic Flow Variability and Errors

Interaction-Aware Trajectory Prediction for Safe Motion Planning in Autonomous Driving: A Transformer-Transfer Learning Approach

Robust Explicit Data-Driven Predictive Control for Mixed Vehicle Platoons

Safe Reinforcement Learning-Based Eco-Driving Control for Mixed Traffic Flows With Disturbances

Time-to-Green Predictions for Fully-Actuated Signal Control Systems With Supervised Learning

Privacy-preserving data fusion for traffic state estimation: A vertical federated learning approach

Privacy-preserving adaptive traffic signal control in a connected vehicle environment

Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning

A traffic signal and loop detector dataset of an urban intersection regulated by a fully actuated signal control system

Cooperative Platoon Formation of Connected and Autonomous Vehicles: Toward Efficient Merging Coordination at Unsignalized Intersections

A traffic signal and loop detector dataset of an urban intersection regulated by a fully actuated signal control system

Trust but Verify: Cryptographic Data Privacy for Mobility Management

On the design of an optimal flexible bus dispatching system with modular bus units: Using the three-dimensional macroscopic fundamental diagram

Collaborators (3)

Zhiyun Deng

Alexander Genser

Monica Menendez

Make your next move.