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Found 35 papers, 6 papers with code
Generative AI for Urban Planning: Synthesizing Satellite Imagery via Diffusion Models
This work establishes a benchmark for controlled urban imagery generation and highlights the potential of generative AI as a tool for enhancing planning workflows and public engagement.
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.
The effect of remote work on urban transportation emissions: evidence from 141 cities
The Blinder-Oaxaca decomposition shows that WFH is the main driver in reducing transportation emissions per capita during the pandemic.
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.
Virtual Nodes Improve Long-term Traffic Prediction
Our proposed model incorporates virtual nodes by constructing a semi-adaptive adjacency matrix.
Modular Conversational Agents for Surveys and Interviews
Surveys and interviews are widely used for collecting insights on emerging or hypothetical scenarios.
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.
SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks
Quantifying uncertainty is crucial for robust and reliable predictions.
Correlating Time Series with Interpretable Convolutional Kernels
This study addresses the problem of convolutional kernel learning in univariate, multivariate, and multidimensional time series data, which is crucial for interpreting temporal patterns in time series and supporting downstream machine learning tasks.
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.
Renal function changes in chronic hepatitis B patients
Patients were categorized into stage 1 or stage 2 based on a baseline eGFR of less than 90 ml/min/m^2 Results: 125 CHB patients were matched 1:1 in both the combined treatment and cured groups.
ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning
Citywalk, a recently popular form of urban travel, requires genuine personalization and understanding of fine-grained requests compared to traditional itinerary planning.
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.
Large Language Models for Travel Behavior Prediction
Travel behavior prediction is a fundamental task in transportation demand management.
MetRoBERTa: Leveraging Traditional Customer Relationship Management Data to Develop a Transit-Topic-Aware Language Model
Transit riders' feedback provided in ridership surveys, customer relationship management (CRM) channels, and in more recent times, through social media is key for transit agencies to better gauge the efficacy of their services and initiatives.
ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks
Traffic data serves as a fundamental component in both research and applications within intelligent transportation systems.
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.
Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks
This Prob-GNN framework is substantiated by deterministic and probabilistic assumptions, and empirically applied to the task of predicting the transit and ridesharing demand in Chicago.
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.
Predicting Drivers' Route Trajectories in Last-Mile Delivery Using A Pair-wise Attention-based Pointer Neural Network
To further capture the global efficiency of the route, we propose a new iterative sequence generation algorithm that is used after model training to identify the first stop of a route that yields the lowest operational cost.
Computer Vision for Transit Travel Time Prediction: An End-to-End Framework Using Roadside Urban Imagery
We propose and evaluate an end-to-end framework integrating traditional transit data sources with a roadside camera for automated roadside image data acquisition, labeling, and model training to predict transit travel times across a segment of interest.
Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks
Recently, spatial-temporal deep learning models demonstrate the tremendous potential to enhance prediction accuracy.
The Braess Paradox in Dynamic Traffic
The Braess's Paradox (BP) is the observation that adding one or more roads to the existing road network will counter-intuitively increase traffic congestion and slow down the overall traffic flow.
Preparing urban mobility for the future of work
A gradual growth in flexible work over many decades has been suddenly and dramatically accelerated by the COVID-19 pandemic.
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.
Individual Mobility Prediction: An Interpretable Activity-based Hidden Markov Approach
Individual mobility is driven by demand for activities with diverse spatiotemporal patterns, but existing methods for mobility prediction often overlook the underlying activity patterns.
Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks
However, the two methods are highly complementary because data-driven methods are more predictive but less interpretable and robust, while theory-driven methods are more interpretable and robust but less predictive.
Deep Neural Networks for Choice Analysis: Architectural Design with Alternative-Specific Utility Functions
Overall, this study demonstrates that prior behavioral knowledge could be used to guide the architecture design of DNN, to function as an effective domain-knowledge-based regularization method, and to improve both the interpretability and predictive power of DNN in choice analysis.
Estimating the potential for shared autonomous scooters
Recent technological developments have shown significant potential for transforming urban mobility.
Computers and Society
Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data
This study presents a framework of multitask learning deep neural networks (MTLDNNs) for this question, and demonstrates that MTLDNNs are more generic than the traditional nested logit (NL) method, due to its capacity of automatic feature learning and soft constraints.
Deep Neural Networks for Choice Analysis: Extracting Complete Economic Information for Interpretation
To demonstrate the strength and challenges of DNNs, we estimated the DNNs using a stated preference survey, extracted the full list of economic information from the DNNs, and compared them with those from the DCMs.
A novel method for predicting and mapping the presence of sun glare using Google Street View
GSV images have view sight similar to drivers, which would make GSV images suitable for estimating the visibility of sun glare to drivers.
