Machine Learning for Transportation JTL’s machine learning cluster focuses on using novel machine-learning perspectives to understand travel behavior and solve transportation challenges.

May 6, 2025 · Her research focuses on two topics: 1) environmentally sustainable transportation, and 2) assessing the differential impact of COVID-19 across segments of transit users with …

Currently, he is working on the social justice of transportation policies, integration of urban systems through the machine learning framework, and demand analysis with unstructured data.

An integrated simulation platform, consisting of three modules on short-term operations planning, real-time control, and travel demand prediction, based on the state-of-the-art machine learning …

To examine sequential decision making under uncertainty, we apply dynamic programming and reinforcement learning algorithms. For example, we use these approaches to develop …

Urban Computing and Machine Learning: Developing machine learning algorithms to predict and nudge travel behaviors; developing bias-mitigation methods to enhance fairness and equity in …

We organize our research into four themes, considering the behavioral foundation of urban mobility, the design of mobility systems, the development of policies related to transportation, …

Explores deep learning (DL) methods for urban mobility applications. Covers concepts of algorithmic prediction, interpretability, causality, and fairness in the context of urban mobility …

Predicting drivers’ route trajectories in last-mile delivery using a pair-wise attention-based pointer neural network Mo, Baichuan Wang, Qingyi Guo, Xiaotong Winkenbach, Matthias Zhao, …

Xinyi’s research interests span a wide array of critical areas within the field, including travel behavior analysis, survey method, advanced statistical analysis, machine learning, and issues …