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Bibliometric methods in traffic flow prediction based on artificial intelligence
Artificial intelligence (AI) technologies are increasingly applied to traffic flow prediction (TFP)
to enhance prediction accuracy. This study utilizes bibliometric methods and network …
to enhance prediction accuracy. This study utilizes bibliometric methods and network …
Machine learning-based traffic prediction models for intelligent transportation systems
Abstract Intelligent Transportation Systems (ITS) have attracted an increasing amount of
attention in recent years. Thanks to the fast development of vehicular computing hardware …
attention in recent years. Thanks to the fast development of vehicular computing hardware …
Semantic understanding and prompt engineering for large-scale traffic data imputation
K Zhang, F Zhou, L Wu, N **e, 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 …
Using LSTM and GRU neural network methods for traffic flow prediction
R Fu, Z Zhang, L Li - 2016 31st Youth academic annual …, 2016 - ieeexplore.ieee.org
Accurate and real-time traffic flow prediction is important in Intelligent Transportation System
(ITS), especially for traffic control. Existing models such as ARMA, ARIMA are mainly linear …
(ITS), especially for traffic control. Existing models such as ARMA, ARIMA are mainly linear …
A scientometric review of research on traffic forecasting in transportation
Research on traffic forecasting in transportation has received worldwide concern over the
past three decades. While there are comprehensive review studies on traffic forecasting, few …
past three decades. While there are comprehensive review studies on traffic forecasting, few …
Traffic flow prediction with big data: A deep learning approach
Accurate and timely traffic flow information is important for the successful deployment of
intelligent transportation systems. Over the last few years, traffic data have been exploding …
intelligent transportation systems. Over the last few years, traffic data have been exploding …
The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets
The station-free sharing bike is a new sharing traffic mode that has been deployed in a large
scale in China in the early 2017. Without docking stations, this system allows the sharing …
scale in China in the early 2017. Without docking stations, this system allows the sharing …
Short-term traffic forecasting: Where we are and where we're going
Since the early 1980s, short-term traffic forecasting has been an integral part of most
Intelligent Transportation Systems (ITS) research and applications; most effort has gone into …
Intelligent Transportation Systems (ITS) research and applications; most effort has gone into …
A novel learning approach for short-term photovoltaic power forecasting-a review and case studies
Integrating photovoltaic power into the power system can offer significant economic and
environmental benefits. However, the intermittent and random nature of photovoltaic power …
environmental benefits. However, the intermittent and random nature of photovoltaic power …
A new method of data missing estimation with FNN-based tensor heterogeneous ensemble learning for internet of vehicle
T Zhang, D Zhang, H Yan, J Qiu, J Gao - Neurocomputing, 2021 - Elsevier
Abstract The Internet of Vehicles (IoV) can obtain traffic information through a large number
of data collected by sensors. However, the lack of data, abnormal data, and other low-quality …
of data collected by sensors. However, the lack of data, abnormal data, and other low-quality …