Xilei Zhao
Orcid: 0000-0002-7903-4806
According to our database1,
Xilei Zhao
authored at least 23 papers
between 2016 and 2023.
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Bibliography
2023
Real-Time Forecasting of Dockless Scooter-Sharing Demand: A Spatio-Temporal Multi-Graph Transformer Approach.
IEEE Trans. Intell. Transp. Syst., August, 2023
ICN: Interactive Convolutional Network for Forecasting Travel Demand of Shared Micromobility.
CoRR, 2023
Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfires.
CoRR, 2023
Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models.
CoRR, 2023
Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery.
Proceedings of the 8th ACM SIGSPATIAL International Workshop on Security Response using GIS 2023, 2023
2022
Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning.
CoRR, 2022
2021
Real-time Forecasting of Dockless Scooter-Sharing Demand: A Context-Aware Spatio-Temporal Multi-Graph Convolutional Network Approach.
CoRR, 2021
Estimating Wildfire Evacuation Decision and Departure Timing Using Large-Scale GPS Data.
CoRR, 2021
CoRR, 2021
Identifying latent shared mobility preference segments in low-income communities: ride-hailing, fixed-route bus, and mobility-on-demand transit.
CoRR, 2021
Do e-scooters fill mobility gaps and promote equity before and during COVID-19? A spatiotemporal analysis using open big data.
CoRR, 2021
Optimizing Demand-Responsive Paratransit Operations: A Mixed Integer Programming Approach.
Proceedings of the 55th Annual Conference on Information Sciences and Systems, 2021
2020
Micromobility Trip Origin and Destination Inference Using General Bikeshare Feed Specification (GBFS) Data.
CoRR, 2020
2019
Candidate gene prioritization for non-communicable diseases based on functional information: Case studies.
J. Biomed. Informatics, 2019
Optimization Models for Estimating Transit Network Origin-Destination Flows with AVL/APC Data.
CoRR, 2019
CoRR, 2019
Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach.
CoRR, 2019
2018
Modeling Stated Preference for Mobility-on-Demand Transit: A Comparison of Machine Learning and Logit Models.
CoRR, 2018
Proceedings of the 2018 Annual American Control Conference, 2018
2016
Estimating travel time in urban traffic by modeling transportation network systems with binary subsystems.
Proceedings of the 2016 American Control Conference, 2016