[HTML][HTML] Machine learning for an explainable cost prediction of medical insurance
Predictive modeling in healthcare continues to be an active actuarial research topic as more
insurance companies aim to maximize the potential of Machine Learning (ML) approaches …
insurance companies aim to maximize the potential of Machine Learning (ML) approaches …
[HTML][HTML] Situational-aware multi-graph convolutional recurrent network (sa-mgcrn) for travel demand forecasting during wildfires
Natural hazards, such as wildfires, pose a significant threat to communities worldwide. Real-
time forecasting of travel demand during wildfire evacuations is crucial for emergency …
time forecasting of travel demand during wildfire evacuations is crucial for emergency …
Modeling protective action decision-making in earthquakes by using explainable machine learning and video data
Earthquakes pose substantial threats to communities worldwide. Understanding how people
respond to the fast-changing environment during earthquakes is crucial for reducing risks …
respond to the fast-changing environment during earthquakes is crucial for reducing risks …
Exploring spatial heterogeneity of e-scooter's relationship with ridesourcing using explainable machine learning
The expansion of e-scooter sharing system has introduced several novel interactions within
the existing transportation system. However, few studies have explored how spatial contexts …
the existing transportation system. However, few studies have explored how spatial contexts …
Unveiling the Spatial Heterogeneity of Factors Influencing Physical and Perceived Recovery Disparities Under Extreme Rainstorms: A Geographically Weighted …
Effectively allocating resources to address both the physical recovery of infrastructure and
subjective needs of residents is crucial to safeguard the well-being of disaster-affected …
subjective needs of residents is crucial to safeguard the well-being of disaster-affected …
[HTML][HTML] Social vulnerabilities and wildfire evacuations: A case study of the 2019 Kincade fire
Vulnerable populations (eg, populations with lower income or disabilities) are
disproportionately impacted by natural hazards like wildfires. It is crucial to develop …
disproportionately impacted by natural hazards like wildfires. It is crucial to develop …
Willingness to use ridesplitting services for home-to-work morning commute in the post-COVID-19 era
This paper explores the influencing factors of commuters' willingness to use ridesplitting
services in the post-COVID-19 era–including promotional strategies–and the possible …
services in the post-COVID-19 era–including promotional strategies–and the possible …
An explainable spatial interpolation method considering spatial stratified heterogeneity
Spatial interpolation is essential for handling sparsity and missing spatial data. Current
machine learning-based spatial interpolation methods are subject to the statistical …
machine learning-based spatial interpolation methods are subject to the statistical …
[HTML][HTML] Nonlinear Influence of the Built Environment on the Attraction of the Third Activity: A Comparative Analysis of Inflow from Home and Work
L Luo, X Yang, X Chen, J Liu, R An, J Li - ISPRS International Journal of …, 2024 - mdpi.com
Gaining an understanding of the intricate mechanisms between human activity and the built
environment can help in promoting sustainable urban development. However, most scholars …
environment can help in promoting sustainable urban development. However, most scholars …
Travel demand forecasting: A fair ai approach
Artificial Intelligence (AI) and machine learning have been increasingly adopted for travel
demand forecasting. The AI-based travel demand forecasting models, though generate …
demand forecasting. The AI-based travel demand forecasting models, though generate …