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Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach
Accurately assessing building damage is critical for disaster response and recovery.
Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery
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
This paper proposes a deep learning model named Interactive Convolutional Network (ICN) to forecast spatiotemporal travel demand for shared micromobility.
Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfires
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.
Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models
The aggravating effects of climate change and the growing population in hurricane-prone areas escalate the challenges in large-scale hurricane evacuations.
Travel Demand Forecasting: A Fair AI Approach
This study can provide transportation professionals with a new tool to achieve fair and accurate travel demand forecasting.
Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning
The growing significance of ridesourcing services in recent years suggests a need to examine the key determinants of ridesourcing demand.
A Clustering-aided Ensemble Method for Predicting Ridesourcing Demand in Chicago
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.
Distilling Black-Box Travel Mode Choice Model for Behavioral Interpretation
Furthermore, the paper provides a comprehensive comparison of student models with the benchmark model (decision tree) and the teacher model (gradient boosting trees) to quantify the fidelity and accuracy of the students' interpretations.
Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach
The results on the case study show that the machine-learning classifier, together with model-agnostic interpretation tools, provides valuable insights on travel mode switching behavior for different individuals and population segments.
Modeling Stated Preference for Mobility-on-Demand Transit: A Comparison of Machine Learning and Logit Models
In other words, how to draw behavioral insights from the high-performance "black-box" machine-learning models remains largely unsolved in the field of travel behavior modeling.
