A comprehensive review on intelligent traffic management using machine learning algorithms

Modi, Yash; Teli, Ridham; Mehta, Akshat; Shah, Konark; Shah, Manan

doi:10.1007/s41062-021-00718-3

Cited by 28 publications

(7 citation statements)

References 68 publications

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“…Intelligent traffic management systems powered by AI offer promising solutions to tackle traffic congestion and enhance overall transportation efficiency [21]. These AI systems can optimize traffic flow, identify congestion hotspots, and dynamically alter traffic signal timings to cut wait times by collecting data from numerous sources such as traffic cameras, GPS devices, and weather conditions.…”

Section: Transportationmentioning

confidence: 99%

AI: the future of humanity

Rawas

2024

Discov Artif Intell

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Artificial intelligence (AI) is reshaping humanity's future, and this manuscript provides a comprehensive exploration of its implications, applications, challenges, and opportunities. The revolutionary potential of AI is investigated across numerous sectors, with a focus on addressing global concerns. The influence of AI on areas such as healthcare, transportation, banking, and education is revealed through historical insights and conversations on different AI systems. Ethical considerations and the significance of responsible AI development are addressed. Furthermore, this study investigates AI's involvement in addressing global issues such as climate change, public health, and social justice. This paper serves as a resource for policymakers, researchers, and practitioners understanding the complex link between AI and humans.

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Section: Transportationmentioning

confidence: 99%

AI: the future of humanity

Rawas

2024

Discov Artif Intell

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“…The difference between the two curves represents, for any volume of traffic (q), the increase in the journey times of the other vehicles which are in circulation due to the introduction of an additional vehicle. Modi et al 30) showed the use of various algorithms for optimising various aspects of the traffic management system, including smart traffic signal management, traffic flow prediction, traffic congestion detection and its management, and automatic detection of traffic signals, is covered in detail in this article, along with its methodology, review, challenges, and potential future applications.…”

Section: Literature Reviewmentioning

confidence: 99%

A Real Time Dynamic Approach for Management of Vehicle Generated Traffic

Chaudhary¹,

Saraswat²,

Yadav³

et al. 2023

Evergreen

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India, with a population of 1.3 billion and almost 300 million vehicles, is one of the biggest contributors to traffic jams, vehicle-specific pollution, and chronic lung diseases. To manage the footfall of these gigantic vehicles, continuous effort and research is taking place in the direction of both active and passive traffic management. This research paper aims towards showcasing a dynamic, fully autonomous deep learning model that uses real-time feeds from existing traffic junctions/ intersection cameras, process them and provide an intensity score based on the density of traffic in each adjoining lane.The proposed CNN model which is based on YOLO framework uses 10 seconds wait analysis time. The proposed system manages traffic, based on the intensity scores which assign traffic a Go time to each lane using an optimal traffic time in the range of 10-50 seconds.The model also scans for emergency vehicles in each lane, to provide a priority pass to such vehicles. Evaluation of model performance Mean Average Precision (mAP) is used.

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“…Whether that be through defining our interests through our browser history, [14], determining which music we should listen to [15], and where to go for dinner [16]. En mass, algorithms are used to extract information from so called "big data" and assist decision making processes in different areas such as surveillance [17], traffic management [18], and financial markets [12].…”

Section: The Place Of Algorithms In Societymentioning

confidence: 99%

Trust in algorithms. An experimental approach

Marmolejo-Ramos

2023

Preprint

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Algorithms have become ubiquitous in our day-to-day activities. Their presence ranges from low-stakes decisions such as what music we should listen to, to high-stakes decisions regarding promotion decisions. Despite the frequency and range with which we interact with algorithms, academic research, as well as public outcries, suggest that humans often express distrust of algorithms and their decisions. Previous research suggests that there may be ways to mitigate this distrust through educational efforts that help explain how algorithms operate. With a sample of 1,841 participants across 19 countries, the present study investigates these notion by examining variations in algorithmic trust across low and high-stakes decisions, and the role of explainability and statistical literacy in shaping reactions. Broadly, results suggest a relationship between how much one understands about algorithms and their propensity to trust. Implicationsof these findings are discussed.

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A comprehensive review on intelligent traffic management using machine learning algorithms

Cited by 28 publications

References 68 publications

AI: the future of humanity

AI: the future of humanity

A Real Time Dynamic Approach for Management of Vehicle Generated Traffic

Trust in algorithms. An experimental approach

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