A comprehensive survey on traffic missing data imputation
Intelligent Transportation Systems (ITS) are essential and play a key role in improving road
safety, reducing congestion, optimizing traffic flow and facilitating the development of smart …
safety, reducing congestion, optimizing traffic flow and facilitating the development of smart …
[HTML][HTML] RT-GCN: Gaussian-based spatiotemporal graph convolutional network for robust traffic prediction
Traffic forecasting plays a critical role in intelligent transportation systems (ITS) in smart
cities. Travelers as well as urban managers rely on reliable traffic information to make their …
cities. Travelers as well as urban managers rely on reliable traffic information to make their …
Road traffic can be predicted by machine learning equally effectively as by complex microscopic model
A Sroczyński, A Czyżewski - Scientific reports, 2023 - nature.com
Since high-quality real data acquired from selected road sections are not always available, a
traffic control solution can use data from software traffic simulators working offline. The …
traffic control solution can use data from software traffic simulators working offline. The …
Deep Learning Models for Spectrum Prediction: A Review
L Wang, J Hu, D Jiang, C Zhang, R Jiang… - IEEE Sensors …, 2024 - ieeexplore.ieee.org
Spectrum prediction is a promising technique for improving spectrum exploitation in
cognitive radio networks (CRNs). Accurate spectrum prediction can assist in reducing the …
cognitive radio networks (CRNs). Accurate spectrum prediction can assist in reducing the …
[HTML][HTML] Koopman theory meets graph convolutional network: Learning the complex dynamics of non-stationary highway traffic flow for spatiotemporal prediction
Reliable and accurate traffic flow prediction is crucial for the construction and operation of
smart highways, supporting scientific traffic management and planning. However, accurately …
smart highways, supporting scientific traffic management and planning. However, accurately …
Towards real-world traffic prediction and data imputation: A multi-task pretraining and fine-tuning approach
Y Qu, Z Li, X Zhao, J Ou - Information Sciences, 2024 - Elsevier
Real-world traffic prediction is challenging and requires accuracy, efficiency, and
generalizability for applications. Most studies used two-step data imputation-prediction …
generalizability for applications. Most studies used two-step data imputation-prediction …
[HTML][HTML] A Memory-augmented Conditional Neural Process model for traffic prediction
This paper presents the first neural process-based model for traffic prediction, the Memory-
augmented Conditional Neural Process (MemCNP). Spatio-temporal traffic prediction …
augmented Conditional Neural Process (MemCNP). Spatio-temporal traffic prediction …
Networked Time-series Prediction with Incomplete Data via Generative Adversarial Network
A networked time series (NETS) is a family of time series on a given graph, one for each
node. It has a wide range of applications from intelligent transportation to environment …
node. It has a wide range of applications from intelligent transportation to environment …
Missing data imputation and classification of small sample missing time series data based on gradient penalized adversarial multi-task learning
In practice, time series data obtained is usually small and missing, which poses a great
challenge to data analysis in different domains, such as increasing the bias of model …
challenge to data analysis in different domains, such as increasing the bias of model …
Predicting lane change maneuver and associated collision risks based on multi-task learning
L Yang, J Zhang, N Lyu, Q Zhao - Accident Analysis & Prevention, 2025 - Elsevier
The lane-changing (LC) maneuver of vehicles significantly impacts highway traffic safety.
Therefore, proactively predicting LC maneuver and associated collision risk is of paramount …
Therefore, proactively predicting LC maneuver and associated collision risk is of paramount …