Jointly contrastive representation learning on road network and trajectory
Road network and trajectory representation learning are essential for traffic systems since
the learned representation can be directly used in various downstream tasks (eg, traffic …
the learned representation can be directly used in various downstream tasks (eg, traffic …
Forecasting the subway passenger flow under event occurrences with multivariate disturbances
Subway passenger flow prediction is of great significance in transportation planning and
operation. Special events, as for vocal concerts and sports games, lead large-scaled …
operation. Special events, as for vocal concerts and sports games, lead large-scaled …
Dynamic causal graph convolutional network for traffic prediction
Modeling complex spatiotemporal dependencies in correlated traffic series is essential for
traffic prediction. While recent works have shown improved prediction performance by using …
traffic prediction. While recent works have shown improved prediction performance by using …
Spatiotemporal prediction of microstructure evolution with predictive recurrent neural network
AAK Farizhandi, M Mamivand - Computational Materials Science, 2023 - Elsevier
Prediction of microstructure evolution during material processing is essential to control the
material properties. Simulation tools for microstructure evolution prediction based on …
material properties. Simulation tools for microstructure evolution prediction based on …
Correlated time series self-supervised representation learning via spatiotemporal bootstrapping
Correlated time series analysis plays an important role in many real-world industries.
Learning an efficient representation of this large-scale data for further downstream tasks is …
Learning an efficient representation of this large-scale data for further downstream tasks is …
Mm-dag: Multi-task dag learning for multi-modal data-with application for traffic congestion analysis
This paper proposes to learn Multi-task, Multi-modal Direct Acyclic Graphs (MM-DAGs),
which are commonly observed in complex systems, eg, traffic, manufacturing, and weather …
which are commonly observed in complex systems, eg, traffic, manufacturing, and weather …
Adaptive hierarchical spatiotemporal network for traffic forecasting
Accurate traffic forecasting is vital to intelligent transportation systems, which are widely
adopted to solve urban traffic issues. Existing traffic forecasting studies focus on modeling …
adopted to solve urban traffic issues. Existing traffic forecasting studies focus on modeling …
A unified probabilistic framework for spatiotemporal passenger crowdedness inference within urban rail transit network
This paper proposes the Spatio-Temporal Crowdedness Inference Model (STCIM), a
framework to infer the passenger distribution inside the whole urban rail transit (URT) …
framework to infer the passenger distribution inside the whole urban rail transit (URT) …
Bus Single‐Trip Time Prediction Based on Ensemble Learning
H Huang, L Huang, R Song, F Jiao… - Computational …, 2022 - Wiley Online Library
The prediction of bus single‐trip time is essential for passenger travel decision‐making and
bus scheduling. Since many factors could influence bus operations, the accurate prediction …
bus scheduling. Since many factors could influence bus operations, the accurate prediction …
VisionTraj: A Noise-Robust Trajectory Recovery Framework based on Large-scale Camera Network
Trajectory recovery based on the snapshots from the city-wide multi-camera network
facilitates urban mobility sensing and driveway optimization. The state-of-the-art solutions …
facilitates urban mobility sensing and driveway optimization. The state-of-the-art solutions …