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Found 11 papers, 1 papers with code
Reimagining Urban Science: Scaling Causal Inference with Large Language Models
Urban causal research is essential for understanding the complex dynamics of cities and informing evidence-based policies.
Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study
Case studies of various community areas in Chicago further illustrated the effectiveness of our approach in addressing spatial and demographic disparities, supporting more balanced and equitable urban planning and policy-making.
Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Composite Spatial Reasoning
Our central hypothesis is that mastering these basic spatial capabilities can significantly enhance a model's performance on composite spatial tasks requiring advanced spatial understanding and combinatorial problem-solving, with generalized improvements in visual-spatial tasks.
GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks
Graph Neural Networks deliver strong classification results but often suffer from poor calibration performance, leading to overconfidence or underconfidence.
Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network
Transportation mode share analysis is important to various real-world transportation tasks as it helps researchers understand the travel behaviors and choices of passengers.
Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System
The results suggest that our proposed method enhances both accuracy and fairness in forecasting ride-hailing demand, ultimately resulting in more equitable vehicle rebalancing in subsequent operations.
Fairness-enhancing deep learning for ride-hailing demand prediction
When coupled with the bias mitigation regularization method, the de-biasing SA-Net effectively bridges the mean percentage prediction error gap between the disadvantaged and privileged groups, and also protects the disadvantaged regions against systematic underestimation of TNC demand.
Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?
The latent space in deep hybrid models can be interpreted, because it reveals meaningful spatial and social patterns.
Simulating the Integration of Urban Air Mobility into Existing Transportation Systems: A Survey
Urban air mobility (UAM) has the potential to revolutionize transportation in metropolitan areas, providing a new mode of transportation that could alleviate congestion and improve accessibility.
Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models
Although researchers increasingly adopt machine learning to model travel behavior, they predominantly focus on prediction accuracy, ignoring the ethical challenges embedded in machine learning algorithms.
Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark
This benchmark study compares two large-scale data sources: a database compiled from literature review summarizing 136 experiments from 35 studies, and our own experiment data, encompassing a total of 6, 970 experiments from 105 models and 12 model families.
