Journal ArticleDOI
Data-Driven Intelligent Transportation Systems: A Survey
TLDR
A survey on the development of D2ITS is provided, discussing the functionality of its key components and some deployment issues associated with D2 ITS Future research directions for the developed system are presented.Abstract:
For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.read more
Citations
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Journal ArticleDOI
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
TL;DR: This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies currently adopt to deal with the Big Data problems.
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Traffic Flow Prediction With Big Data: A Deep Learning Approach
TL;DR: A novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently and is applied for the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction.
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Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting
TL;DR: Experiments on two real-world datasets from the Caltrans Performance Measurement System demonstrate that the proposed ASTGCN model outperforms the state-of-the-art baselines.
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Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning
TL;DR: It is presented that MTL can improve the generalization performance of shared tasks and a grouping method based on the weights in the top layer to make MTL more effective is proposed to take full advantage of weight sharing in the deep architecture.
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Short-term traffic forecasting: Where we are and where we’re going
TL;DR: In this article, the authors present a review of the existing literature on short-term traffic forecasting and offer suggestions for future work, focusing on 10 challenging, yet relatively under researched, directions.
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