Review on Deep Learning in Intelligent Transportation Systems
Intelligent transportation system (ITS) contributes to allocate transportation resources, from citywide ones to nationwide, more efficiently with the help of algorithms. Due to the fact that the ITS is a quite comprehensive field, it is necessary for rese
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Abstract. Intelligent transportation system (ITS) contributes to allocate transportation resources, from citywide ones to nationwide, more efficiently with the help of algorithms. Due to the fact that the ITS is a quite comprehensive field, it is necessary for researchers to have a better understanding of dominant methods and which one is proper for the targeted subjects of ITS. More importantly, with a knowledge of the remained challenges in developing ITS and relevant techniques, researchers may have a clearer direction to work on. To provide researchers with dedicated information on specific machine learning (ML) techniques used in object recognition and traffic prediction, two essential study subjects in ITS, this paper mainly focuses on deep learning and neural network (NN), one of widely-used ML algorithms, and aims to conduct a brief review on its recent applications in ITS, as well as to mine its potential usage. As a result, this review introduces some popular NN, convolutional neural network (CNN), long short-term memory (LSTM) network, gated recurrent unit (GRU) network, and their hybrid mechanism, first. Then their applications and performance in ITS are described. Finally, this paper discusses constraints on some of them and suggests some promising research directions. Keywords: Deep learning Convolutional neural network Long Short-Term memory Gated recurrent unit Intelligent transportation system
1 Introduction As the traffic demand boosts with the increasing urbanization level, constructing an intelligent transportation system (ITS) is a necessity, not only would it bring more convenience to people, but also it makes the management of citywide or nationwide transportation more efficient. Machine learning (ML), progressed rapidly on the basis of artificial intelligence (AI) in the last decades, has achieved a lot in classification tasks and regression problems. Since there is also a strong need of recognizing objects and estimating traffic in ITS, such as vehicle detection in autonomous driving system or travel time prediction in routing planning, many ML techniques have been adopted in various subjects of ITS consequently, for example, k-means in speed prediction [1] and Bayes Network in car detection [2]. However, most of these traditional ML techniques failed to learn the complex features in a large-scaled transportation system. Hence, recent mainstream researches pay attention to the usage of neural networks (NNs) in ITS, since NN performs well in capturing features and obtains higher accuracy © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Q. Liu et al. (Eds.): CENet 2020, AISC 1274, pp. 399–408, 2021. https://doi.org/10.1007/978-981-15-8462-6_45
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compared to those ML algorithms. Considered these facts, this study will introduce three popular NN structures, which are convolutional neural network (CNN), long short-term memory (LSTM) network, and gated recurrent unit (GRU) network. The ITS applications based o
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