Lijun Sun

Assistant Professor

McGill University

Canada

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Dr. Lijun Sun is an Assistant Professor at McGill University in Canada. His research focuses on advanced forecasting techniques in transportation, including railway booking demand and urban mobility prediction. Dr. Sun has contributed significantly to the field with recent articles on topics such as Bayesian inference for transit services and multivariate time series forecasting.

Articles (20)

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2025

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2022

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2022

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2022

Collaborators (5)

Nicolas Saunier

Full Professor

Polytechnique Montréal

CANADA

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Jiawei Wang

The Univeristy of Tokyo

JAPAN

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Chengyuan Zhang

McGill University

CANADA

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Wu Yuan-Kai

McGill University

CANADA

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Mengying Lei

McGill University

CANADA

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Lijun Sun

Research Interests

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About

Articles (20)

Rethinking Urban Mobility Prediction: A Multivariate Time Series Forecasting Approach

Abnormal metro passenger demand is predictable from alighting and boarding correlation

Railway booking demand forecasting for revenue management: a deep probabilistic approach

Bayesian inference of time-varying origin–destination matrices from boarding and alighting counts for transit services

A gentle introduction and tutorial on Deep Generative Models in transportation research

Nearest Neighbor Multivariate Time Series Forecasting

Probabilistic Traffic Forecasting with Dynamic Regression

Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting

Conditional forecasting of bus travel time and passenger occupancy with Bayesian Markov regime-switching vector autoregression

Contextualizing MLP-Mixers Spatiotemporally for Urban Traffic Data Forecast at Scale

Multi-objective multi-agent deep reinforcement learning to reduce bus bunching for multiline services with a shared corridor

Anti-circulant dynamic mode decomposition with sparsity-promoting for highway traffic dynamics analysis

Routine Pattern Discovery and Anomaly Detection in Individual Travel Behavior

Robust Dynamic Bus Control: a Distributional Multi-Agent Reinforcement Learning Approach

Adversarial Danger Identification on Temporally Dynamic Graphs

Discovering Dynamic Patterns from Spatiotemporal Data with Time-Varying Low-Rank Autoregression

Low-Rank Hankel Tensor Completion for Traffic Speed Estimation

A Universal Framework of Spatiotemporal Bias Block for Long-Term Traffic Forecasting

Bayesian Kernelized Matrix Factorization for Spatiotemporal Traffic Data Imputation and Kriging

Memory-augmented dynamic graph convolution networks for traffic data imputation with diverse missing patterns

Collaborators (5)

Nicolas Saunier

Jiawei Wang

Chengyuan Zhang

Wu Yuan-Kai

Mengying Lei

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