Machine learning for next‐generation intelligent transportation systems: A survey

Yuan, Tingting; Rocha, Wilson; Rothenberg, Christian Esteve; Barakat, Chadi; Turletti, Thierry

doi:10.1002/ett.4427

Cited by 67 publications

(34 citation statements)

References 270 publications

(330 reference statements)

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“…Although traditional machine learning methods [310] and reinforcement learning methods [99] already provide acceptable solutions for many ITS problems, due to the graph natural of traffic and road network, GNN-based models have demonstrated superior performance to previous approaches on ITS tasks [112]. We structure this section followed by the type of problem being investigated.…”

Section: 24mentioning

confidence: 99%

Graph Neural Networks in IoT: A Survey

Dong¹,

Tang²,

Wang³

et al. 2022

Preprint

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The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technologies, IoT devices including smart wearables, cameras, smartwatches, and autonomous vehicles can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph Neural Networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source code from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computing methodologies → Neural networks.

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Section: 24mentioning

confidence: 99%

Graph Neural Networks in IoT: A Survey

Dong¹,

Tang²,

Wang³

et al. 2022

Preprint

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“…Several works consider ML-based approaches for crowd management (see, for example, [39,70]). In [40] the authors provide a thorough survey on ML techniques for intelligent transportation systems. Although ML-based approaches can achieve good results in crowd flow prediction, several reasons pushed researchers to adopt DL-based methods, such as, above all, the ability to automatically extract relevant patterns from unstructured and heterogeneous data.…”

Section: A Crowd Predictionmentioning

confidence: 99%

Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review

Darsena¹,

Gelli²,

Iudice³

et al. 2022

Preprint

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<p>Management of crowd information in public transportation (PT) systems is crucial, both to foster sustainable mobility, by increasing the user's comfort and satisfaction during normal operation, as well as to cope with emergency situations, such as pandemic crises, as recently experienced with COVID-19 limitations. This paper presents a taxonomy and review of sensing technologies based on Internet of Things (IoT) for real-time crowd analysis, which can be adopted in the different segments of the PT system (buses/trams/trains, railway/metro stations, and bus stops). To discuss such technologies in a clear systematic perspective, we introduce a reference architecture for crowd management, which employs modern information and communication technologies (ICT) in order to: (i) monitor and predict crowding events; (ii) implement crowd-aware policies for real-time and adaptive operation control in intelligent transportation systems (ITSs); (iii) inform in real-time the users of the crowding status of the PT system, by means of electronic displays installed inside vehicles or at bus stops/stations, and/or by mobile transport applications. It is envisioned that the innovative crowd management functionalities enabled by ICT/IoT sensing technologies can be incrementally implemented as an add-on to state-of-the-art ITS platforms, which are already in use by major PT companies operating in urban areas. Moreover, it is argued that, in this new framework, additional services can be delivered to the passengers, such as, e.g., on-line ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning.</p>

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“…However, it focused on vehicle detection methods and thus did not cover traffic camera calibration, vehicle tracking, and speed estimation, etc. A more recent review by Yuan et al [25] focuses on machine learning techniques for next-generation intelligent transportation systems, but deviated from the area of visual traffic surveillance.…”

Section: Introductionmentioning

confidence: 99%

Monocular Visual Traffic Surveillance: A Review

Zhang

Feng

Angeloudis

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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To facilitate the monitoring and management of modern transportation systems, monocular visual traffic surveillance systems have been widely adopted for speed measurement, accident detection, and accident prediction. Thanks to the recent innovations in computer vision and deep learning research, the performance of visual traffic surveillance systems has been significantly improved. However, despite this success, there is a lack of survey papers that systematically review these new methods. Therefore, we conduct a systematic review of relevant studies to fill this gap and provide guidance to future studies. This paper is structured along the visual information processing pipeline that includes object detection, object tracking, and camera calibration. Moreover, we also include important applications of visual traffic surveillance systems, such as speed measurement, behavior learning, accident detection and prediction. Finally, future research directions of visual traffic surveillance systems are outlined.

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Machine learning for next‐generation intelligent transportation systems: A survey

Cited by 67 publications

References 270 publications

Graph Neural Networks in IoT: A Survey

Graph Neural Networks in IoT: A Survey

Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review

Monocular Visual Traffic Surveillance: A Review

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