34 citations · 112 across the 20 of their papers we have counts for
7 papers · 1 filter
Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfires
Xiaojian Zhang, Xilei Zhao, Yiming Xu +2
Real-time forecasting of travel demand during wildfire evacuations is crucial for emergency managers and transportation planners to make timely and better-informed decisions. Howev…
Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models
Yuran Sun, Shih-Kai Huang, Xilei Zhao
The aggravating effects of climate change and the growing population in hurricane-prone areas escalate the challenges in large-scale hurricane evacuations. While hurricane prepared…
Travel Demand Forecasting: A Fair AI Approach
Xiaojian Zhang, Qian Ke, Xilei Zhao
Artificial Intelligence (AI) and machine learning have been increasingly adopted for travel demand forecasting. The AI-based travel demand forecasting models, though generate accur…
Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning
Xiaojian Zhang, Xiang Yan, Zhengze Zhou +2
The growing significance of ridesourcing services in recent years suggests a need to examine the key determinants of ridesourcing demand. However, little is known regarding the non…
A Clustering-aided Ensemble Method for Predicting Ridesourcing Demand in Chicago
Xiaojian Zhang, Xilei Zhao
Accurately forecasting ridesourcing demand is important for effective transportation planning and policy-making. With the rise of Artificial Intelligence (AI), researchers have sta…
Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach
Xilei Zhao, Xiang Yan, Pascal Van Hentenryck
Recent years have witnessed an increased focus on interpretability and the use of machine learning to inform policy analysis and decision making. This paper applies machine learnin…
Modeling Stated Preference for Mobility-on-Demand Transit: A Comparison of Machine Learning and Logit Models
Xilei Zhao, Xiang Yan, Alan Yu +1
Logit models are usually applied when studying individual travel behavior, i.e., to predict travel mode choice and to gain behavioral insights on traveler preferences. Recently, so…