Prediction based traffic management in a metropolitan area

Chavhan, Suresh; Venkataram, Pallapa

doi:10.1016/j.jtte.2018.05.003

Cited by 45 publications

(20 citation statements)

References 37 publications

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“…[1,9,10,[15][16][17][18]21] *; . [13,17,20,44] *; [28,36,39,41,43] **; [59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75]. Note: *discussed under theme 1 & **discussed under theme 2.…”

Section: Methodsmentioning

confidence: 99%

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The State of the Art of Cooperative and Connected Autonomous Vehicles from the Future Mobility Management Perspective: A Systematic Review

Alam

Georgakis

2022

Future Transportation

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Cooperative and connected autonomous vehicles (CCAVs) are considered to be a promising solution for addressing congestion and other operational deficiencies, as part of a holistic future mobility management framework. As a result, a significant number of studies have recently been published on this topic. From the perspective of future mobility management, this review paper discusses three themes, which are traffic management, network performance, and mobility management, including congestion, and incident detection using the PRISMA methodology. Three databases were considered for this study, and peer-reviewed primary studies were selected that were published within the last 10 years in the English language, focusing on CCAV in the context of the future transportation and mobility management perspective. For synthesis and interpretation, like-for-like comparisons were made among studies; it was found that extensive research-supported information is required to ensure a smooth transition from conventional vehicles to the CCAVs regime, to achieve the projected traffic and environmental benefits. Research investigations are ongoing to optimize these benefits and associated goals via the setting of different models and simulations. The tools and technologies for the testing and simulation of CCAV were found to have limited capacity. Following the review of the current state-of-the-art, recommendations for future research have been discussed. The most notable is the need for large-scale simulations to understand the impact of CCAVs beyond corridor-based and small-scale networks, the need for understanding the interactions between the drivers of CCAVs and traffic management centers, and the need to assess the technological transition, as far as infrastructure systems are concerned, that is necessary for the progressive penetration of CCAVs into traffic streams.

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Section: Methodsmentioning

confidence: 99%

“…Most importantly, the synthesis of the results from the studies List of papers [1][2][3]5,6]. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. [1,9,10,[15][16][17][18]21] *; .…”

Section: Methodsmentioning

confidence: 99%

The State of the Art of Cooperative and Connected Autonomous Vehicles from the Future Mobility Management Perspective: A Systematic Review

Alam

Georgakis

2022

Future Transportation

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“…With the advancement of ICTs and their application to transportation is known as Intelligent Transport System (ITS). ITS-based freight transportation majorly concentrates on providing safety, mobility management, avoiding accidents/crashing, etc., on the roadways [16][17][18][19][20]. As there is a reduction in accidents and eicient mobility management, which result into smooth and steady movements of vehicles on the roadway.…”

Section: Origin Of Problem Statementmentioning

confidence: 99%

“…Hence, it has become a major function to monitor and reduce greenhouse gas emissions from road transport [24][25][26][27]. The combination of AI, MAS and IoT is denoted as A-MIT, is capable of collecting, processing, predicting and sharing huge data generated at various devices and infrastructures in the urban areas [4,5,7,9,11,19,20]. As the development, advancement and maturity of ICTs and A-MIT, it is essential to analyze the possibility and eiciency of AI-empowered [12,[21][22][23] IoT [14,17,21] based intelligent transportation system (ITS) [7,9,11,13,14,16].…”

Section: Introductionmentioning

confidence: 99%

Edge Computing AI-IoT Integrated Energy-efficient Intelligent Transportation System for Smart Cities

Chavhan

Gupta

Gochhayat

et al. 2022

ACM Trans. Internet Technol.

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Advancement of information and communication technologies (ICTs) there is high-scale utilization of IoT and adoption of AI in the transportation system to improve the utilization of energy, reduce greenhouse gas (GHG) emissions, increase quality of services and many extensive benefits to the commuters’ and transportation authorities. In this paper, we propose a novel edge-based AI-IoT integrated energy efficient intelligent transport system for smart cities by using distributed multi-agent system. An urban area is divided into multiple number of regions and each region is sub-divided into finite number of zones. At each zone optimal number of RSUs are installed along with the edge computing devices. The MAS deployed at each RSU collects the huge volume of data from the various sensors, devices and infrastructures. The edge computing device uses the collected raw data from the MAS to process, analyze and predict. The predicted information will be shared with the neighborhood RSUs, vehicles, and cloud by using MAS with the help of IoT. The predicted information can be used by freight vehicles to maintain the smooth and steady movement which result into reduction in the GHG emissions and energy consumptions, and finally improves the freight vehicles mileage by reducing the traffic congestion in the urban areas. We have exhaustively carried out the simulation results and demonstrated the effectiveness of the proposed system.

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“…The traffic engineering with traffic predictions is a promising approach to settle that traffic that changes in time [4]. In addition, traffic prediction can reduce problems such as traffic congestions, accidents, environmental pollution, economic losses, and unnecessary waste of fuel, in metropolitan areas [5].…”

Section: Introductionmentioning

confidence: 99%

Study of Delays to Evaluate the Sustainability over Time of Solutions for Urban Intersections

Medina¹,

Burga²,

Bravo³

2020

Proceedings of the 4th International Conference on Civil, Structural and Transportation Engineering (ICCSTE'19)

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Prediction based traffic management in a metropolitan area

Cited by 45 publications

References 37 publications

The State of the Art of Cooperative and Connected Autonomous Vehicles from the Future Mobility Management Perspective: A Systematic Review

The State of the Art of Cooperative and Connected Autonomous Vehicles from the Future Mobility Management Perspective: A Systematic Review

Edge Computing AI-IoT Integrated Energy-efficient Intelligent Transportation System for Smart Cities

Study of Delays to Evaluate the Sustainability over Time of Solutions for Urban Intersections

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