34 citations · 53 across the 14 of their papers we have counts for
15 papers
Design and Validation of a Grid-based Home Detection via Stay-Time (GHOST) Software for Mobile Location Data
Alessandra Recalde, Mustafa Sameen, Xiaojian Zhang +1
Accurately detecting home locations from GPS data generated by mobile devices is a foundational step in human mobility research, with significant implications for transportation pl…
Evacuation destination choices during Hurricane Ian: A direct demand modeling approach
Alessandra Recalde, Luyu Liu, Xiaojian Zhang +3
Hurricanes are causing unprecedented damage to the natural environment, infrastructure, and communities. Understanding evacuation behavior is essential for improving emergency prep…
Synthesizing Attitudes, Predicting Actions (SAPA): Behavioral Theory-Guided LLMs for Ridesourcing Mode Choice Modeling
Mustafa Sameen, Xiaojian Zhang, Xilei Zhao
Accurate modeling of ridesourcing mode choices is essential for designing and implementing effective traffic management policies for reducing congestion, improving mobility, and al…
Hurricane Evacuation Analysis with Large-scale Mobile Device Location Data during Hurricane Ian
Luyu Liu, Xiaojian Zhang, Shangkun Jiang +1
Hurricane Ian is the deadliest and costliest hurricane in Florida's history, with 2.5 million people ordered to evacuate. As we witness increasingly severe hurricanes in the contex…
Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach
Zhuoqun Xue, Xiaojian Zhang, David O. Prevatt +3
Accurately assessing building damage is critical for disaster response and recovery. However, many existing models for detecting building damage have poor prediction accuracy due t…
Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery
Chenguang Wang, Yepeng Liu, Xiaojian Zhang +6
Timely and accurate assessment of hurricane-induced building damage is crucial for effective post-hurricane response and recovery efforts. Recently, remote sensing technologies pro…
ICN: Interactive Convolutional Network for Forecasting Travel Demand of Shared Micromobility
Yiming Xu, Qian Ke, Xiaojian Zhang +1
Accurate shared micromobility demand predictions are essential for transportation planning and management. Although deep learning models provide powerful tools to deal with demand…
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…
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…
The Short-term Impact of Congestion Taxes on Ridesourcing Demand and Traffic Congestion: Evidence from Chicago
Yuan Liang, Bingjie Yu, Xiaojian Zhang +2
Ridesourcing is popular in many cities. Despite its theoretical benefits, a large body of studies have claimed that ridesourcing also brings (negative) externalities (e.g., inducin…
Real-Time Forecasting of Dockless Scooter-Sharing Demand: A Spatio-Temporal Multi-Graph Transformer Approach
Yiming Xu, Xilei Zhao, Xiaojian Zhang +1
Accurately forecasting the real-time travel demand for dockless scooter-sharing is crucial for the planning and operations of transportation systems. Deep learning models provide r…
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…
Do e-scooters fill mobility gaps and promote equity before and during COVID-19? A spatiotemporal analysis using open big data
Xiang Yan, Wencui Yang, Xiaojian Zhang +3
The growing popularity of e-scooters and their rapid expansion across urban streets has attracted widespread attention. A major policy question is whether e-scooters substitute exi…
Optimizing Demand-Responsive Paratransit Operations: A Mixed Integer Programming Approach
Xiaojian Zhang, Yu Yang, Abigail L. Cochran +2
The traditional demand-responsive paratransit system plays an important role in connecting people to health care, particularly those who are carless, low-income, senior, underinsur…