Intelligent Transport System for a Smart City

Telang, Samir; Chel, Arvind; Nemade, Anant; Kaushik, Geetanjali

doi:10.1007/978-3-030-53149-2_9

Cited by 32 publications

(16 citation statements)

References 3 publications

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“…Four research questions were developed to guide the study (1) what are the entities involved in a smart technology ecosystem, (2) what are the impacts of smart technology and applications, (3) what are the challenges of having smart technology implementation, and (4) what are the responsibility or roles of the entities in the smart technology ecosystem to ensure a smooth transition. An analysis of the literature was made to answer the questions.…”

Section: Article Infomentioning

confidence: 99%

“…The concept of smart technologies has been around for several decades, with early developments in AI and ML dating back to the 1950s [3] . However, it was not getting attention until the widespread adoption of personal computers and the Internet in the 1990s that the term "smart technology" began to be widely used [4] . The growth of the Internet, combined with advancements in computer processing power and data storage capabilities, has enabled the development of smart technologies on a large scale [5] .…”

Section: Definition and Ecosystem Of Smart Technologymentioning

confidence: 99%

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Smart technology: Ecosystem, impacts, challenges and the path forward

Katuk,

Wan Abdullah,

Sugiharto

et al. 2023

Infor. Syst. Smart. City

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Smart technologies have become increasingly integral to modern society, bringing many benefits and opportunities. However, they also raise important questions about their impacts on individuals, organisations, and society and how everyone can move forward smoothly in integrating and adopting technology. Therefore, this study analysed and reviewed the literature to address the questions. The study provides an overview of the potential benefits and challenges of smart technologies and applications, including artificial intelligence, machine learning, natural language processing, robotics, and the Internet of Things. The review also explores the key entities involved in a smart technology ecosystem, including governments, businesses, and international communities, and each entity’s unique role in developing and implementing smart technologies. Additionally, the review highlights the importance of ensuring a smooth transition to smart technologies, including the need for effective policies, regulations, and ethical considerations. The key takeaways from this review are the need to balance the benefits and opportunities of smart technologies with the challenges and risks they pose. Individuals, society, businesses and governments must play their respective roles in ensuring that smart technologies are developed, implemented, and used in ways that are responsible, ethical, and sustainable.

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Section: Article Infomentioning

confidence: 99%

Section: Definition and Ecosystem Of Smart Technologymentioning

confidence: 99%

Smart technology: Ecosystem, impacts, challenges and the path forward

Katuk,

Wan Abdullah,

Sugiharto

et al. 2023

Infor. Syst. Smart. City

View full text Add to dashboard Cite

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“…Wireless visual sensor networks (WVSNs) have recently emerged as a new type of sensor-based intelligent system, and their performance exceeds that of existing surveillance sensor networks. Furthermore, due to their small size and the dense spatial coverage that can be achieved, WVSNs can be flexibly deployed for various computer vision tasks, including patient monitoring [2], distributed multimedia-based surveillance systems [3], military, police, airport [4], border [5], urban and transport [6] applications, and other areas of public interest. WVSN surveillance systems have an extensive range of applications worldwide, but their implementation has remained a challenge, due to the need for connectivity between multiple sensors and their complicated setup.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Based Camera Prioritization in Large Scale Surveillance Networks

Hussain¹,

Muhammad²,

Ullah³

et al. 2022

Computers, Materials &Amp; Continua

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Digital surveillance systems are ubiquitous and continuously generate massive amounts of data, and manual monitoring is required in order to recognise human activities in public areas. Intelligent surveillance systems that can automatically identify normal and abnormal activities are highly desirable, as these would allow for efficient monitoring by selecting only those camera feeds in which abnormal activities are occurring. This paper proposes an energy-efficient camera prioritisation framework that intelligently adjusts the priority of cameras in a vast surveillance network using feedback from the activity recognition system. The proposed system addresses the limitations of existing manual monitoring surveillance systems using a three-step framework. In the first step, the salient frames are selected from the online video stream using a frame differencing method. A lightweight 3D convolutional neural network (3DCNN) architecture is applied to extract spatio-temporal features from the salient frames in the second step. Finally, the probabilities predicted by the 3DCNN network and the metadata of the cameras are processed using a linear threshold gate sigmoid mechanism to control the priority of the camera. The proposed system performs well compared to state-of-theart violent activity recognition methods in terms of efficient camera prioritisation in large-scale surveillance networks. Comprehensive experiments and an evaluation of activity recognition and camera prioritisation showed that our approach achieved an accuracy of 98% with an F1-score of 0.97 on the Hockey Fight dataset, and an accuracy of 99% with an F1-score of 0.98 on the Violent Crowd dataset.

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“…The resulting traffic density is estimated at more than 500 per km, which is the highest in the country. In the era of information and communication technology, urban transportation at such a large scale cannot function well without the support of intelligent transportation systems (ITSs) (Telang et al, 2021, Zear et al, 2016, Khatoun and Zeadally, 2016, Mckenney and Frey-Spurlock, 2018. For instance, one plausible measure for coordinating urban road traffic could be monitoring the traffic volume using closed circuit television (CCTV) images at each road junction and implementing the prediction based on the historic traffic volume data.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning–Based Approach for Moving Vehicle Counting and Short-Term Traffic Prediction From Video Images

Zheng

et al. 2022

Front. Environ. Sci.

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The intelligent transportation system (ITS) is one of the effective solutions to the problem of urban traffic congestion, and it is also one of the important topics of smart city construction. One particular application is the traffic monitoring and flow prediction. However, there are still challenges regarding both aspects. On the one hand, the current traffic monitoring relies heavily on the single object detection method that cannot achieve accurate statistics of moving target counting and, meanwhile, has limited speed advantage; on the other hand, the existing traffic flow prediction models rarely consider different weather conditions. Therefore, the present article attempts to propose a packaged solution, which combines a new target tracking and moving vehicle counting method and an improved long short-term memory (LSTM) network for traffic flow forecast with weather conditions. More specifically, the DCN V2 convolution kernel and MultiNetV3 framework are used to replace YOLOv4’s conventional convolution kernel and backbone network to realize multi-target tracking and counting, respectively. Subsequently, combined with the temporal characteristics of historical traffic flow, this article introduces weather conditions into the LSTM network and realizes the short-term prediction of traffic flow at the road junction level. This study carries out a series of experiments using the real traffic video data with a 2-month time span at a popular road junction in the downtown of Shenzhen, China. The results suggest that the proposed algorithms outperform the previous methods in terms of the 10% higher accuracy of target detection tracking and about a half reduction of traffic prediction error, when considering weather conditions.

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Intelligent Transport System for a Smart City

Cited by 32 publications

References 3 publications

Smart technology: Ecosystem, impacts, challenges and the path forward

Smart technology: Ecosystem, impacts, challenges and the path forward

Anomaly Based Camera Prioritization in Large Scale Surveillance Networks

A Deep Learning–Based Approach for Moving Vehicle Counting and Short-Term Traffic Prediction From Video Images

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