Enhancing transportation systems via deep learning: A survey
Abstract Machine learning (ML) plays the core function to intellectualize the transportation
systems. Recent years have witnessed the advent and prevalence of deep learning which …
systems. Recent years have witnessed the advent and prevalence of deep learning which …
Computer vision applications in intelligent transportation systems: a survey
As technology continues to develop, computer vision (CV) applications are becoming
increasingly widespread in the intelligent transportation systems (ITS) context. These …
increasingly widespread in the intelligent transportation systems (ITS) context. These …
A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation
The missing data problem is inevitable when collecting traffic data from intelligent
transportation systems. Previous studies have shown the advantages of tensor completion …
transportation systems. Previous studies have shown the advantages of tensor completion …
An efficient realization of deep learning for traffic data imputation
Traffic data provide the basis for both research and applications in transportation control,
management, and evaluation, but real-world traffic data collected from loop detectors or …
management, and evaluation, but real-world traffic data collected from loop detectors or …
Semantic understanding and prompt engineering for large-scale traffic data imputation
K Zhang, F Zhou, L Wu, N Xie, Z He - Information Fusion, 2024 - Elsevier
Abstract Intelligent Transportation Systems (ITS) face the formidable challenge of large-
scale missing data, particularly in the imputation of traffic data. Existing studies have mainly …
scale missing data, particularly in the imputation of traffic data. Existing studies have mainly …
Missing value imputation for traffic-related time series data based on a multi-view learning method
In reality, readings of sensors on highways are usually missing at various unexpected
moments due to some sensor or communication errors. These missing values do not only …
moments due to some sensor or communication errors. These missing values do not only …
Short-term traffic prediction based on dynamic tensor completion
Short-term traffic prediction plays a critical role in many important applications of intelligent
transportation systems such as traffic congestion control and smart routing, and numerous …
transportation systems such as traffic congestion control and smart routing, and numerous …
Data-driven transfer learning framework for estimating on-ramp and off-ramp traffic flows
To develop the most appropriate control strategy and monitor, maintain, and evaluate the
traffic performance of the freeway weaving areas, state and local Departments of …
traffic performance of the freeway weaving areas, state and local Departments of …
A variational autoencoder solution for road traffic forecasting systems: Missing data imputation, dimension reduction, model selection and anomaly detection
Efforts devoted to mitigate the effects of road traffic congestion have been conducted since
1970s. Nowadays, there is a need for prominent solutions capable of mining information …
1970s. Nowadays, there is a need for prominent solutions capable of mining information …
Traffic flow imputation using parallel data and generative adversarial networks
Traffic data imputation is critical for both research and applications of intelligent
transportation systems. To develop traffic data imputation models with high accuracy, traffic …
transportation systems. To develop traffic data imputation models with high accuracy, traffic …