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Found 11 papers, 3 papers with code
Astra: Toward General-Purpose Mobile Robots via Hierarchical Multimodal Learning
The planning head utilizes flow matching and a novel masked ESDF loss to minimize collision risks for generating local trajectories, and the odometry head integrates multi-sensor inputs via a transformer encoder to predict the relative pose of the robot.
Generative AI for Urban Design: A Stepwise Approach Integrating Human Expertise with Multimodal Diffusion Models
This study underscores the benefits of multimodal diffusion models and stepwise generation in preserving human control and facilitating iterative refinements, laying the groundwork for human-AI interaction in urban design solutions.
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.
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 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.
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.
Estimating air quality co-benefits of energy transition using machine learning
Applications of the framework with Chinese data reveal highly heterogeneous health benefits of reducing fossil fuel use in different sectors and regions in China with a mean of \$34/tCO2 and a standard deviation of \$84/tCO2.
Detection of electric network frequency in audio recordings-from theory to practical detectors
Further, we propose a time-frequency (TF) domain detector, termed as TF detector, which exploits the a priori knowledge of the ENF.
ENF (Electric Network Frequency) Detection
ENF (Electric Network Frequency) Extraction
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.
