Internet of smart-cameras for traffic lights optimization in smart cities

Tchuitcheu, Willy Carlos; Bobda, Christophe; Pantho, Jubaer Hossain

doi:10.1016/j.iot.2020.100207

Cited by 30 publications

(11 citation statements)

References 20 publications

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“…ere are several research works on using cameras to count the number of vehicles for traffic management and optimization. A recent study using the Internet of smart-cameras is presented in [36]. e solution is based on WSN and VANET by deploying a very large number of cameras connected in a dedicated infrastructure.…”

Section: Related Workmentioning

confidence: 99%

“…Although other studies already suggested the use of V2I communication as the basis of the traffic management system, in this paper, we provide a complete study and performance analysis for different traffic conditions. As stated before, this study does not require any extra hardware like counting cables, computer vision, and camera systems [21,36]. ere is no need for expensive and complex systems to extract useful features such as computer vision or deep learning approaches [5,39,40] or parallel processing [42].…”

Section: Related Workmentioning

confidence: 99%

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Intelligent Traffic Management System Based on the Internet of Vehicles (IoV)

Mohamed

Al-Shalfan

2021

Journal of Advanced Transportation

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The present era is marked by rapid improvement and advances in technology. One of the most essential areas that demand improvement is the traffic signal, as it constitutes the core of the traffic system. This demand becomes stringent with the development of Smart Cities. Unfortunately, road traffic is currently controlled by very old traffic signals (tri-color signals) regardless of the relentless effort devoted to developing and improving the traffic flow. These traditional traffic signals have many problems including inefficient time management in road intersections; they are not immune to some environmental conditions, like rain; and they have no means of giving priority to emergency vehicles. New technologies like Vehicular Ad-hoc Networks (VANET) and Internet of Vehicles (IoV) enable vehicles to communicate with those nearby and with a dedicated infrastructure wirelessly. In this paper, we propose a new traffic management system based on the existing VANET and IoV that is suitable for future traffic systems and Smart Cities. In this paper, we present the architecture of our proposed Intelligent Traffic Management System (ITMS) and Smart Traffic Signal (STS) controller. We present local traffic management of an intersection based on the demands of future Smart Cities for fairness, reducing commute time, providing reasonable traffic flow, reducing traffic congestion, and giving priority to emergency vehicles. Simulation results showed that the proposed system outperforms the traditional management system and could be a candidate for the traffic management system in future Smart Cities. Our proposed adaptive algorithm not only significantly reduces the average waiting time (delay) but also increases the number of serviced vehicles. Besides, we present the implemented hardware prototype for STS.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Intelligent Traffic Management System Based on the Internet of Vehicles (IoV)

Mohamed

Al-Shalfan

2021

Journal of Advanced Transportation

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“…Emergency cases inclusive algorithms explicitly discuss emergency cases. For example, Tchuitcheu et al [56] suggested a collision-free distributed algorithm for controlling traffic signals using the information provided by smart cameras. Their algorithm used smart cameras for real-time monitoring and assessment.…”

Section: B Emergency Cases Inclusive Algorithmsmentioning

confidence: 99%

Distributed Mutual Exclusion Algorithms for Intersection Traffic Problems

2020

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Conurbations around the globe are encountering the challenges of traffic congestion. Miscellaneous smart systems have been developed to help control and improve traffic flow in a cost-effective and measurable way. Yet, the existing systems solve the traffic congestion problem with cost-intensive traffic lights and mostly handle emergency cases poorly. In this paper, to minimize the traffic congestion problems for any kind of vehicles without using traffic lights, we propose three deadlock-free algorithms namely: (i) Mutual exclusion algorithm based on single instruction (MEASIR), (ii) Mutual exclusion algorithm based on priority (MEAPRI), and (iii) Mutual exclusion algorithm based on multi-agent systems (MEAMAS). Communication inside a group is accomplished via a queue structure, while an external element (e.g., a router) is used for internal communications. Besides the depiction of experimental and simulation results, a complete statistical analysis has been performed to compare the performance of MEASIR, MEAPRI, and MEAMAS with their alternatives. Our proposed deadlock-free algorithms are not only efficient but also functional with a computational cost of O(n) to enter the critical section, where n represents the number of all vehicles in a particular intersection.

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“…In certain areas, traffic congestion in main roads has been a big issue with the shortage and restricted provision of public transport. If there is no initiative, this dilemma would be unmanageable [5,6].…”

Section: Introductionmentioning

confidence: 99%

IoT-based traffic prediction and traffic signal control system for smart city

Neelakandan¹,

Berlin

Tripathi

et al. 2021

Soft Comput

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Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80286 microprocessor for a smart city.The proposed system consists of '5' phases, namely, IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data is collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80286 microprocessor. The experimental results show that the proposed system outperforms state-of-the-art methods.

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Internet of smart-cameras for traffic lights optimization in smart cities

Cited by 30 publications

References 20 publications

Intelligent Traffic Management System Based on the Internet of Vehicles (IoV)

Intelligent Traffic Management System Based on the Internet of Vehicles (IoV)

Distributed Mutual Exclusion Algorithms for Intersection Traffic Problems

IoT-based traffic prediction and traffic signal control system for smart city

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