Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis
The volume and availability of data in the Intelligent Transportation System (ITS) result in the
need for data-driven approaches. Big Data algorithms are applied to further enhance the …
need for data-driven approaches. Big Data algorithms are applied to further enhance the …
A review on deep learning techniques for IoT data
Continuous growth in software, hardware and internet technology has enabled the growth of
internet-based sensor tools that provide physical world observations and data …
internet-based sensor tools that provide physical world observations and data …
Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction
This paper proposes a convolutional neural network (CNN)-based method that learns traffic
as images and predicts large-scale, network-wide traffic speed with a high accuracy …
as images and predicts large-scale, network-wide traffic speed with a high accuracy …
Urban computing: concepts, methodologies, and applications
Urbanization's rapid progress has modernized many people's lives but also engendered big
issues, such as traffic congestion, energy consumption, and pollution. Urban computing …
issues, such as traffic congestion, energy consumption, and pollution. Urban computing …
Spatio-temporal self-supervised learning for traffic flow prediction
Robust prediction of citywide traffic flows at different time periods plays a crucial role in
intelligent transportation systems. While previous work has made great efforts to model …
intelligent transportation systems. While previous work has made great efforts to model …
Deep architecture for traffic flow prediction: Deep belief networks with multitask learning
Traffic flow prediction is a fundamental problem in transportation modeling and
management. Many existing approaches fail to provide favorable results due to being: 1) …
management. Many existing approaches fail to provide favorable results due to being: 1) …
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 …
LSTM-based traffic flow prediction with missing data
Traffic flow prediction plays a key role in intelligent transportation systems. However, since
traffic sensors are typically manually controlled, traffic flow data with varying length, irregular …
traffic sensors are typically manually controlled, traffic flow data with varying length, irregular …
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 …
A survey on modern deep neural network for traffic prediction: Trends, methods and challenges
In this modern era, traffic congestion has become a major source of severe negative
economic and environmental impact for urban areas worldwide. One of the most efficient …
economic and environmental impact for urban areas worldwide. One of the most efficient …