no code implementations • 8 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.
no code implementations • 5 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.
no code implementations • 16 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.
no code implementations • 3 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.
no code implementations • 13 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.
no code implementations • 24 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.
no code implementations • 2 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.
no code implementations • 11 Apr 2024 • Zhuoqun Xue, Xiaojian Zhang, David O. Prevatt, Jennifer Bridge, Susu Xu, Xilei Zhao
Accurately assessing building damage is critical for disaster response and recovery.