268 citations · 1.7k across the 47 of their papers we have counts for
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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…
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…
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…
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…
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…
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…
Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging
Yuankai Wu, Dingyi Zhuang, Mengying Lei +2
Spatiotemporal kriging is an important application in spatiotemporal data analysis, aiming to recover/interpolate signals for unsampled/unobserved locations based on observed signa…
Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns
Yuebing Liang, Zhan Zhao, Lijun Sun
Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent transportation systems. Despite extensive research regarding traffic data imputation…
Spatially Focused Attack against Spatiotemporal Graph Neural Networks
Fuqiang Liu, Luis Miranda-Moreno, Lijun Sun
Spatiotemporal forecasting plays an essential role in various applications in intelligent transportation systems (ITS), such as route planning, navigation, and traffic control and…
Individual Mobility Prediction via Attentive Marked Temporal Point Processes
Yuankai Wu, Zhanhong Cheng, Lijun Sun
Individual mobility prediction is an essential task for transportation demand management and traffic system operation. There exist a large body of works on modeling location sequen…
Reducing Bus Bunching with Asynchronous Multi-Agent Reinforcement Learning
Jiawei Wang, Lijun Sun
The bus system is a critical component of sustainable urban transportation. However, due to the significant uncertainties in passenger demand and traffic conditions, bus operation…
Low-Rank Hankel Tensor Completion for Traffic Speed Estimation
Xudong Wang, Yuankai Wu, Dingyi Zhuang +1
This paper studies the traffic state estimation (TSE) problem using sparse observations from mobile sensors. Most existing TSE methods either rely on well-defined physical traffic…
Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation
Xinyu Chen, Mengying Lei, Nicolas Saunier +1
Spatiotemporal traffic time series (e.g., traffic volume/speed) collected from sensing systems are often incomplete with considerable corruption and large amounts of missing values…
Incremental Bayesian tensor learning for structural monitoring data imputation and response forecasting
Pu Ren, Xinyu Chen, Lijun Sun +1
There has been increased interest in missing sensor data imputation, which is ubiquitous in the field of structural health monitoring (SHM) due to discontinuous sensing caused by s…
Inductive Graph Neural Networks for Spatiotemporal Kriging
Yuankai Wu, Dingyi Zhuang, Aurelie Labbe +1
Time series forecasting and spatiotemporal kriging are the two most important tasks in spatiotemporal data analysis. Recent research on graph neural networks has made substantial p…