Deep learning support for intelligent transportation systems

Ibáñez, Juan Antonio Guerrero; Contreras-Castillo, Juan; Zeadally, Sherali

doi:10.1002/ett.4169

Cited by 33 publications

(17 citation statements)

References 169 publications

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“…Traffic information perception is a multilevel data conversion process including material, data, and feature layers. The material layer contains the sensitive phenomena and processes of the perceived traffic object; the data layer contains the conversion results of different sensors to the corresponding sensitive information; the feature layer is responsible for transmission, feature extraction, and fusion of data collected by the sensor, and finally obtains the perception information and transmits it to the perception subject [34]. Different from traditional perception, urban traffic intelligence can be adjusted to adapt to environmental changes while actively responding to changes in the surrounding environment [35,36].…”

Section: Intelligent Perception and Sensing Network Of Urban Trafficmentioning

confidence: 99%

Sensing fusion in vehicular network digital twins for 6G smart city

Chen¹,

Lv²,

Lv³

2022

ITU J-FET

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The aims demonstrated in this article are to effectively monitor the complex road environment in smart city transportation using the sixth generation mobile communication technology (6G) Digital Twins (DTs), to perceive the complex road environment of smart city traffic. Vehicular Networks (VN) in the smart transportation system have been selected as the research object, and the multi-sensor collaboration and fusion technology in the network is explored, so as to meet the active control requirements of intelligent vehicles. A lidar and camera fusion-based segmentation network C-LNet is proposed. The structure of a C-LNet multi-sensing data fusion segmentation network is double encoder-single decoder. Two encoders are used to extract image features and lidar features respectively. The same heterogeneous data is realized through the synchronization of lidar point cloud data and image data in sensor space. For multimodal information, a multiscale feature fusion-based vehicle collaboration method is designed. In the simulation experiment part, the C-LNet multi-sensing data fusion segmentation network is verified on the KITTI data set. The accuracy, F1 value, and MIoU of C-LNet are 98.4%, 96.7%, and 94.51%, respectively, which are better than those of an RGB network and lidar network. In summary, the smart transportation system supported by DTs in a 6G environment is explored. The proposed VN sensing fusion method can effectively realize the collaborative positioning perception of multiple vehicles, which lays the foundation for the realization of complex collaborative decision-making and control in smart transportation.

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Section: Intelligent Perception and Sensing Network Of Urban Trafficmentioning

confidence: 99%

Sensing fusion in vehicular network digital twins for 6G smart city

Chen¹,

Lv²,

Lv³

2022

ITU J-FET

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“…More importantly, both CNN and RNN are applied to the temporal data forecasting in the field of transportation and logistics inch by inch for the past few years. Guerrero-Ibanez et al [ 42 ] discussed some of the challenges that need to be solved to achieve seamless integration between intelligent transportation systems and deep learning methods. Du et al [ 43 ] proposed a dynamic transition CNN for the purpose of precise traffic demand prediction.…”

Section: An Attention Mechanism Oriented Hybrid Cnn-rnn Deep Learning Architecturementioning

confidence: 99%

An Attention Mechanism Oriented Hybrid CNN-RNN Deep Learning Architecture of Container Terminal Liner Handling Conditions Prediction

2021

Computational Intelligence and Neuroscience

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The booming computational thinking and deep learning make it possible to construct agile, efficient, and robust deep learning-driven decision-making support engine for the operation of container terminal handling systems (CTHSs). Within the conceptual framework of computational logistics, an attention mechanism oriented hybrid convolutional neural network and recurrent neural network deep learning architecture (AMO-HCR-DLA) is proposed technically to predict the container terminal liner handling conditions that mainly include liner handling time (LHT) and total working time of quay crane farm (TWT-QCF) for a calling liner. Consequently, the container terminal oriented logistics generalized computation (CTO-LGC) automation and intelligence are established tentatively by AMO-HCR-DLA. A typical regional container terminal hub of China is selected to design, implement, execute, and evaluate the AMO-HCR-DLA with the actual production data. In the case of severe vibration of LHT and TWT-QCF, while forecasting the handling conditions of 210 ships based on the CTO-LGC running log of four years, the forecasting error of LHT within one hour is more than 97% and that of TWT-QCF within six hours accounts for 89.405%. When predicting the operating conditions of 300 liners by the log of five years, the forecasting deviation of LHT within one hour is more than striking 99% and that of TWT-QCF within six hours reaches up to 94.010% as well. All are far superior to the predicting outcomes by the classical algorithms of machine learning and deep learning. Hence, the AMO-HCR-DLA shows excellent performance for the prediction of CTHS with the low and stable computational consuming. It also demonstrates the feasibility, credibility, and realizability of the computing architecture and design paradigm of AMO-HCR-DLA preliminarily.

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“…The main endeavor, however, has been developing suitable and scalable algorithms and methods that can harness the full potential of such data for analyzing highly complex transportation systems. Machine learning, and its more recent variant, deep learning algorithms, have turned out to be popular and practical solutions for many real-world applications and have proved to perform satisfactorily in the domain of intelligent transportation systems [1], [2], [3]. While deep learning algorithms have shown promising results, there are some shortcomings with these popular methods.…”

Section: Introductionmentioning

confidence: 99%

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Rahmani

Baghbani

Bouguila

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future research directions that covers all transportation areas. Accordingly, in this survey, for the first time, we provide an overview of GNN studies in the general domain of ITS. Unlike previous surveys, which have been limited to traffic forecasting problems, we explore how GNN frameworks have evolved for different ITS applications, including traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. Also, we micro-categorize the studies based on their transportation application to identify domainspecific research directions, opportunities, and challenges, which have been missing in previous surveys. Moreover, we identify unique and undiscussed research opportunities and directions, which is the result of reviewing a wide range of transportation applications. The neglected role of edge and graph learning in ITS applications, developing multi-modal models, and exploiting the power of unsupervised and reinforcement learning methods for developing more powerful GNNs are some examples of such new discussions in this survey. Finally, we have identified popular baseline models and datasets in each transportation domain, which facilitate the development and evaluation of future GNNbased frameworks.

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Deep learning support for intelligent transportation systems

Cited by 33 publications

References 169 publications

Sensing fusion in vehicular network digital twins for 6G smart city

Sensing fusion in vehicular network digital twins for 6G smart city

An Attention Mechanism Oriented Hybrid CNN-RNN Deep Learning Architecture of Container Terminal Liner Handling Conditions Prediction

Graph Neural Networks for Intelligent Transportation Systems: A Survey

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