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Found 51 papers, 26 papers with code
Preference Optimization for Combinatorial Optimization Problems
Reinforcement Learning (RL) has emerged as a powerful tool for neural combinatorial optimization, enabling models to learn heuristics that solve complex problems without requiring expert knowledge.
Likelihood-Free Variational Autoencoders
In this work, we propose EnVAE, a novel likelihood-free generative framework that has a deterministic decoder and employs the energy score--a proper scoring rule--to build the reconstruction loss.
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.
Multi-Agent Coordination across Diverse Applications: A Survey
Multi-agent coordination studies the underlying mechanism enabling the trending spread of diverse multi-agent systems (MAS) and has received increasing attention, driven by the expansion of emerging applications and rapid AI advances.
Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting
However, their robustness and reliability in real-world applications remain under-explored, particularly concerning their susceptibility to adversarial attacks.
Generalized Least Squares Kernelized Tensor Factorization
In this paper, we introduce the Generalized Least Squares Kernelized Tensor Factorization (GLSKF) framework for tensor completion.
SPTTE: A Spatiotemporal Probabilistic Framework for Travel Time Estimation
While existing research employs probabilistic modeling to assess travel time uncertainty and account for correlations between multiple trips, modeling the temporal variability of multi-trip travel time distributions remains a significant challenge.
MVG-CRPS: A Robust Loss Function for Multivariate Probabilistic Forecasting
Multivariate Gaussian (MVG) distributions are central to modeling correlated continuous variables in probabilistic forecasting.
A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Research
Deep Generative Models (DGMs) have rapidly advanced in recent years, becoming essential tools in various fields due to their ability to learn complex data distributions and generate synthetic data.
Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast
Tensor decomposition has emerged as a prominent technique to learn low-dimensional representation under the supervision of reconstruction error, primarily benefiting data inference tasks like completion and imputation, but not classification task.
Communication-Aware Reinforcement Learning for Cooperative Adaptive Cruise Control
Cooperative Adaptive Cruise Control (CACC) plays a pivotal role in enhancing traffic efficiency and safety in Connected and Autonomous Vehicles (CAVs).
Link Representation Learning for Probabilistic Travel Time Estimation
During inference, we estimate the probability distribution of the travel time of a queried trip conditional on the completed trips that are spatiotemporally adjacent.
Learning Car-Following Behaviors Using Bayesian Matrix Normal Mixture Regression
Learning and understanding car-following (CF) behaviors are crucial for microscopic traffic simulation.
Multivariate Probabilistic Time Series Forecasting with Correlated Errors
Accurately modeling the correlation structure of errors is critical for reliable uncertainty quantification in probabilistic time series forecasting.
Computational Efficiency
Probabilistic Time Series Forecasting
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Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach
To address this challenge, we present the Super-Multivariate Urban Mobility Transformer (SUMformer), which utilizes a specially designed attention mechanism to calculate temporal and cross-variable correlations and reduce computational costs stemming from a large number of time series.
A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery
Then, we propose a Multi-view Graph and Complexity Aware Transformer (MGCAT) model to encode these semantics in trajectory pre-training from two aspects: 1) adaptively aggregate the multi-view graph features considering trajectory pattern, and 2) higher attention to critical nodes in a complex trajectory.
Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering
In this paper, we propose a novel tensor Dirichlet Process Multinomial Mixture model with graphs, which can preserve the hierarchical structure of the multi-dimensional trip information and cluster them in a unified one-step manner with the ability to determine the number of clusters automatically.
Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach
To address this gap, we propose an unsupervised method called Floss that automatically regularizes learned representations in the frequency domain.
Contextualizing MLP-Mixers Spatiotemporally for Urban Data Forecast at Scale
Our findings contribute to the exploration of simple-yet-effective models for real-world STTD forecasting.
Tensor Dirichlet Process Multinomial Mixture Model for Passenger Trajectory Clustering
In this paper, we propose a novel Tensor Dirichlet Process Multinomial Mixture model (Tensor-DPMM), which is designed to preserve the multi-mode and hierarchical structure of the multi-dimensional trip information via tensor, and cluster them in a unified one-step manner.
Better Batch for Deep Probabilistic Time Series Forecasting
Deep probabilistic time series forecasting has gained attention for its ability to provide nonlinear approximation and valuable uncertainty quantification for decision-making.
Traffic State Estimation from Vehicle Trajectories with Anisotropic Gaussian Processes
However, the lack of sensors often results in incomplete traffic state data, making it challenging to obtain reliable information for decision-making.
Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization
However, such simple kernel specifications are deficient in learning functions with complex features, such as being nonstationary, nonseparable, and multimodal.
Probabilistic Traffic Forecasting with Dynamic Regression
The newly designed loss function is based on the likelihood of a non-isotropic error term, enabling the model to generate probabilistic forecasts while preserving the original outputs of the base model.
Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting
Deep learning-based multivariate and multistep-ahead traffic forecasting models are typically trained with the mean squared error (MSE) or mean absolute error (MAE) as the loss function in a sequence-to-sequence setting, simply assuming that the errors follow an independent and isotropic Gaussian or Laplacian distributions.
Laplacian Convolutional Representation for Traffic Time Series Imputation
In this study, we first introduce a Laplacian kernel to temporal regularization for characterizing local trends in traffic time series, which can be formulated as a circular convolution.
Discovering Dynamic Patterns from Spatiotemporal Data with Time-Varying Low-Rank Autoregression
In the temporal context, the complex time-varying system behaviors can be revealed by the temporal modes in the proposed model.
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data
Probabilistic modeling of multidimensional spatiotemporal data is critical to many real-world applications.
Toward multi-target self-organizing pursuit in a partially observable Markov game
The proposed distributed algorithm: fuzzy self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the three challenges in multi-target SOP: distributed self-organizing search (SOS), distributed task allocation, and distributed single-target pursuit.
Forecasting Sparse Movement Speed of Urban Road Networks with Nonstationary Temporal Matrix Factorization
Movement speed data from urban road networks, computed from ridesharing vehicles or taxi trajectories, is often high-dimensional, sparse, and nonstationary (e. g., exhibiting seasonality).
Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach
However, the operation of a bus fleet is unstable in nature, and bus bunching has become a common phenomenon that undermines the efficiency and reliability of bus systems.
Hankel-structured Tensor Robust PCA for Multivariate Traffic Time Series Anomaly Detection
We treat the raw data with anomalies as a multivariate time series matrix (location $\times$ time) and assume the denoised matrix has a low-rank structure.
Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging
Specifically, we propose a novel spatial aggregation network (SAN) inspired by Principal Neighborhood Aggregation, which uses multiple aggregation functions to help one node gather diverse information from its neighbors.
Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns
The results show that our proposed model outperforms existing deep learning models in all kinds of missing scenarios and the graph structure estimation technique contributes to the model performance.
Spatially Focused Attack against Spatiotemporal Graph Neural Networks
However, despite that recent studies have demonstrated that deep neural networks (DNNs) are vulnerable to carefully designed perturbations in multiple domains like objection classification and graph representation, current adversarial works cannot be directly applied to spatiotemporal forecasting due to the causal nature and spatiotemporal mechanisms in forecasting models.
Individual Mobility Prediction via Attentive Marked Temporal Point Processes
Individual mobility prediction is an essential task for transportation demand management and traffic system operation.
Scalable Spatiotemporally Varying Coefficient Modelling with Bayesian Kernelized Tensor Regression
To address this challenge, we summarize the spatiotemporally varying coefficients using a third-order tensor structure and propose to reformulate the spatiotemporally varying coefficient model as a special low-rank tensor regression problem.
Low-Rank Hankel Tensor Completion for Traffic Speed Estimation
This paper studies the traffic state estimation (TSE) problem using sparse observations from mobile sensors.
Reducing Bus Bunching with Asynchronous Multi-Agent Reinforcement Learning
However, due to the significant uncertainties in passenger demand and traffic conditions, bus operation is unstable in nature and bus bunching has become a common phenomenon that undermines the reliability and efficiency of bus services.
Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation
In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework by introducing \textit{temporal variation} as a new regularization term into the completion of a third-order (sensor $\times$ time of day $\times$ day) tensor.
One Vertex Attack on Graph Neural Networks-based Spatiotemporal Forecasting
Empirical studies prove that perturbations in one vertex can be diffused into most of the graph when spatiotemporal GNNs are under One Vertex Attack.
Industrial Topics in Urban Labor System
It is therefore important to recognize that classification system are not necessarily static, especially for economic systems, and even more so in urban areas where most innovation takes place and is implemented.
Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation
Recent studies based on tensor nuclear norm have demonstrated the superiority of tensor learning in imputation tasks by effectively characterizing the complex correlations/dependencies in spatiotemporal data.
Efficient Connected and Automated Driving System with Multi-agent Graph Reinforcement Learning
Instead of learning a reliable behavior for ego automated vehicle, we focus on how to improve the outcomes of the total transportation system by allowing each automated vehicle to learn cooperation with each other and regulate human-driven traffic flow.
Incremental Bayesian tensor learning for structural monitoring data imputation and response forecasting
To address this fundamental issue, this paper presents an incremental Bayesian tensor learning method for reconstruction of spatiotemporal missing data in SHM and forecasting of structural response.
Low-Rank Autoregressive Tensor Completion for Multivariate Time Series Forecasting
In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework to model multivariate time series data.
Inductive Graph Neural Networks for Spatiotemporal Kriging
Time series forecasting and spatiotemporal kriging are the two most important tasks in spatiotemporal data analysis.
A Nonconvex Low-Rank Tensor Completion Model for Spatiotemporal Traffic Data Imputation
Sparsity and missing data problems are very common in spatiotemporal traffic data collected from various sensing systems.
Bayesian Temporal Factorization for Multidimensional Time Series Prediction
In this paper, we propose a Bayesian temporal factorization (BTF) framework for modeling multidimensional time series -- in particular spatiotemporal data -- in the presence of missing values.
Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer Optimization
Traditional motion planning methods suffer from several drawbacks in terms of optimality, efficiency and generalization capability.
Cooperative coevolution of real predator robots and virtual robots in the pursuit domain
This paper presents a particle swarm optimization (PSO) based cooperative coevolutionary algorithm for the (predator) robots, called CCPSO-R, where real and virtual robots coexist in an evolutionary algorithm (EA).
Robotics Multiagent Systems
