268 citations · 1.7k across the 47 of their papers we have counts for
48 papers
Likelihood-Free Variational Autoencoders
Chen Xu, Qiang Wang, Lijun Sun
Variational Autoencoders (VAEs) typically rely on a probabilistic decoder with a predefined likelihood, most commonly an isotropic Gaussian, to model the data conditional on latent…
SPTTE: A Spatiotemporal Probabilistic Framework for Travel Time Estimation
Chen Xu, Qiang Wang, Lijun Sun
Accurate travel time estimation is essential for navigation and itinerary planning. While existing research employs probabilistic modeling to assess travel time uncertainty and acc…
Link Representation Learning for Probabilistic Travel Time Estimation
Chen Xu, Qiang Wang, Lijun Sun
Travel time estimation is a key task in navigation apps and web mapping services. Existing deterministic and probabilistic methods, based on the assumption of trip independence, pr…
Learning Car-Following Behaviors Using Bayesian Matrix Normal Mixture Regression
Chengyuan Zhang, Kehua Chen, Meixin Zhu +2
Learning and understanding car-following (CF) behaviors are crucial for microscopic traffic simulation. Traditional CF models, though simple, often lack generalization capabilities…
Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering
Ziyue Li, Hao Yan, Chen Zhang +3
Passenger clustering based on trajectory records is essential for transportation operators. However, existing methods cannot easily cluster the passengers due to the hierarchical s…
Shareable Driving Style Learning and Analysis with a Hierarchical Latent Model
Chaopeng Zhang, Wenshuo Wang, Zhaokun Chen +3
Driving style is usually used to characterize driving behavior for a driver or a group of drivers. However, it remains unclear how one individual's driving style shares certain com…
Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach
Chunwei Yang, Xiaoxu Chen, Lijun Sun +2
Time series analysis is a fundamental task in various application domains, and deep learning approaches have demonstrated remarkable performance in this area. However, many real-wo…
Calibrating Car-Following Models via Bayesian Dynamic Regression
Chengyuan Zhang, Wenshuo Wang, Lijun Sun
Car-following behavior modeling is critical for understanding traffic flow dynamics and developing high-fidelity microscopic simulation models. Most existing impulse-response car-f…
Tensor Dirichlet Process Multinomial Mixture Model for Passenger Trajectory Clustering
Ziyue Li, Hao Yan, Chen Zhang +4
Passenger clustering based on travel records is essential for transportation operators. However, existing methods cannot easily cluster the passengers due to the hierarchical struc…
Better Batch for Deep Probabilistic Time Series Forecasting
Vincent Zhihao Zheng, Seongjin Choi, Lijun Sun
Deep probabilistic time series forecasting has gained attention for its ability to provide nonlinear approximation and valuable uncertainty quantification for decision-making. Howe…
Traffic State Estimation from Vehicle Trajectories with Anisotropic Gaussian Processes
Fan Wu, Zhanhong Cheng, Huiyu Chen +2
Accurately monitoring road traffic state is crucial for various applications, including travel time prediction, traffic control, and traffic safety. However, the lack of sensors of…
Anti-circulant dynamic mode decomposition with sparsity-promoting for highway traffic dynamics analysis
Xudong Wang, Lijun Sun
Highway traffic states data collected from a network of sensors can be considered a high-dimensional nonlinear dynamical system. In this paper, we develop a novel data-driven metho…
Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization
Mengying Lei, Lijun Sun
Bayesian optimization (BO) primarily uses Gaussian processes (GP) as the key surrogate model, mostly with a simple stationary and separable kernel function such as the squared-expo…
Probabilistic Traffic Forecasting with Dynamic Regression
Vincent Zhihao Zheng, Seongjin Choi, Lijun Sun
This paper proposes a dynamic regression (DR) framework that enhances existing deep spatiotemporal models by incorporating structured learning for the error process in traffic fore…
Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting
Seongjin Choi, Nicolas Saunier, Vincent Zhihao Zheng +2
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 fun…
Laplacian Convolutional Representation for Traffic Time Series Imputation
Xinyu Chen, Zhanhong Cheng, HanQin Cai +2
Spatiotemporal traffic data imputation is of great significance in intelligent transportation systems and data-driven decision-making processes. To perform efficient learning and a…
Discovering Dynamic Patterns from Spatiotemporal Data with Time-Varying Low-Rank Autoregression
Xinyu Chen, Chengyuan Zhang, Xiaoxu Chen +2
The problem of broad practical interest in spatiotemporal data analysis, i.e., discovering interpretable dynamic patterns from spatiotemporal data, is studied in this paper. Toward…
Bayesian calibration of traffic flow fundamental diagrams using Gaussian processes
Zhanhong Cheng, Xudong Wang, Xinyuan Chen +2
Modeling the relationship between vehicle speed and density on the road is a fundamental problem in traffic flow theory. Recent research found that using the least-squares (LS) met…
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data
Mengying Lei, Aurelie Labbe, Lijun Sun
Probabilistic modeling of multidimensional spatiotemporal data is critical to many real-world applications. As real-world spatiotemporal data often exhibits complex dependencies th…
Probabilistic forecasting of bus travel time with a Bayesian Gaussian mixture model
Xiaoxu Chen, Zhanhong Cheng, Jian Gang Jin +2
Accurate forecasting of bus travel time and its uncertainty is critical to service quality and operation of transit systems; for example, it can help passengers make better decisio…
Forecasting Sparse Movement Speed of Urban Road Networks with Nonstationary Temporal Matrix Factorization
Xinyu Chen, Chengyuan Zhang, Xi-Le Zhao +2
Movement speed data from urban road networks, computed from ridesharing vehicles or taxi trajectories, is often high-dimensional, sparse, and nonstationary (e.g., exhibiting season…
Bayesian inference for link travel time correlation of a bus route
Xiaoxu Chen, Zhanhong Cheng, Lijun Sun
Estimation of link travel time correlation of a bus route is essential to many bus operation applications, such as timetable scheduling, travel time forecasting and transit service…
Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach
Jiawei Wang, Lijun Sun
Bus system is a critical component of sustainable urban transportation. However, the operation of a bus fleet is unstable in nature, and bus bunching has become a common phenomenon…
Hankel-structured Tensor Robust PCA for Multivariate Traffic Time Series Anomaly Detection
Xudong Wang, Luis Miranda-Moreno, Lijun Sun
Spatiotemporal traffic data (e.g., link speed/flow) collected from sensor networks can be organized as multivariate time series with additional spatial attributes. A crucial task i…