Search Results for author: Xiaojian Zhang

Found 7 papers, 0 papers with code

Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery

no code implementations2 Oct 2023 Chenguang Wang, Yepeng Liu, Xiaojian Zhang, Xuechun Li, Vladimir Paramygin, Arthriya Subgranon, Peter Sheng, Xilei Zhao, Susu Xu

We gathered and annotated building damage ground truth data in Lee County, Florida, and compared the introduced method's estimation results with the ground truth and benchmarked it against state-of-the-art models to assess the effectiveness of our proposed method.

ICN: Interactive Convolutional Network for Forecasting Travel Demand of Shared Micromobility

no code implementations24 Jun 2023 Yiming Xu, Qian Ke, Xiaojian Zhang, Xilei Zhao

This paper proposes a deep learning model named Interactive Convolutional Network (ICN) to forecast spatiotemporal travel demand for shared micromobility.

Management

Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfires

no code implementations13 Apr 2023 Xiaojian Zhang, Xilei Zhao, Yiming Xu, Ruggiero Lovreglio, Daniel Nilsson

Based on the travel demand inferred from the GPS data, we develop a new deep learning model, i. e., Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN), along with a model updating scheme to achieve real-time forecasting of travel demand during wildfire evacuations.

Travel Demand Forecasting: A Fair AI Approach

no code implementations3 Mar 2023 Xiaojian Zhang, Qian Ke, Xilei Zhao

This study can provide transportation professionals with a new tool to achieve fair and accurate travel demand forecasting.

Decision Making Fairness

Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning

no code implementations16 Sep 2022 Xiaojian Zhang, Xiang Yan, Zhengze Zhou, Yiming Xu, Xilei Zhao

The growing significance of ridesourcing services in recent years suggests a need to examine the key determinants of ridesourcing demand.

The Short-term Impact of Congestion Taxes on Ridesourcing Demand and Traffic Congestion: Evidence from Chicago

no code implementations5 Jul 2022 Yuan Liang, Bingjie Yu, Xiaojian Zhang, Yi Lu, Linchuan Yang

To this end, this study applies difference-in-differences (i. e., a regression-based causal inference approach) to empirically evaluate the effects of the congestion tax policy on ridesourcing demand and traffic congestion in Chicago.

Causal Inference regression

A Clustering-aided Ensemble Method for Predicting Ridesourcing Demand in Chicago

no code implementations8 Sep 2021 Xiaojian Zhang, Xilei Zhao

To account for spatial heterogeneity, this study proposes a Clustering-aided Ensemble Method (CEM) to forecast the zone-to-zone (census-tract-to-census-tract) travel demand for ridesourcing services.

BIG-bench Machine Learning Clustering

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