An Intelligent Traffic Light scheduling algorithm through VANETs

Younes, Maram Bani; Boukerche, Azzedine

doi:10.1109/lcnw.2014.6927714

Cited by 26 publications

(22 citation statements)

References 10 publications

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“…Maram Bani Younes and Azzedine Boukerche [5] have offered insights into using VANETs(Vehicular Ad-hoc Networks) technology to develop an intelligent traffic light controlling algorithms by placing wireless transceiver and GPS tools on vehicles. Each vehicle over the road periodically broadcasts the basic traveling data (i.e., location, speed, direction, destination, etc).…”

Section: Literature Surveymentioning

confidence: 99%

Traffic Control System and Technologies: A Survey

Kulkarni¹,

Shah²,

Patil³

et al. 2020

IJERT

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The rapid increase in vehicles and large time delays between traffic lights have been the major reason behind the cost of congestion. To overcome this problem, several techniques and methods have been introduced throughout the years which are trying to make traffic control systems dynamic. The conventional traffic control systems work on fixed timings allocated to each side of the junction which cannot be altered, In smart traffic control systems, these timings are controlled according to traffic density. The purpose of this study is to investigate whether these methods are feasible and if we can find any correlations between them. Methods discussed in this paper include the use of IR sensors, RFID tags, Thermal cameras and Reinforcement learning.

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Section: Literature Surveymentioning

confidence: 99%

Traffic Control System and Technologies: A Survey

Kulkarni¹,

Shah²,

Patil³

et al. 2020

IJERT

View full text Add to dashboard Cite

show abstract

“…Less bandwidth utilization in the core network: In the current landscape of billions of mobile devices that generate data, we observe that captured data often only is of limited spatial and temporal relevance. As an example, we can imagine an intelligent scheduling scheme for traffic lights that is based on reported sensor data from vehicles [51]. Furthermore, individual sensor readings are rarely of interest.…”

Section: A Promises Benefits and Drawbacks Of Edge Computingmentioning

confidence: 99%

“…Service placement in multi-access edge computing (a recent renaming of mobile edge computing by the ETSI-MEC industry specification group 51 to emphasize the inclusion of access technologies other than cellular networks) also raises some open questions. As indicated in Section V, there are already numerous commercial solutions for edge computing, especially for IoT, most of which are advertised for stationary in-house deployment (i.e., hybrid cloud).…”

Section: Multi-access Edge Computingmentioning

confidence: 99%

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

et al. 2019

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Edge computing brings computing and storage resources closer to (mobile) end users and data sources, thus bypassing expensive and slow links to distant cloud computing infrastructures. Often leveraged opportunistically, these heterogeneous resources can be used to offload data and computations, enabling upcoming demanding applications such as augmented reality and autonomous driving. Research in this direction has addressed various challenges, from architectural concerns to runtime optimizations. As of today, however, we lack a widespread availability of edge computing-partly because it remains unclear which of the promised benefits of edge computing are relevant for what types of applications. This article provides a comprehensive snapshot of the current edge computing landscape, with a focus on the application perspective. We outline the characteristics of edge computing and its postulated benefits and drawbacks. To understand the functional composition of applications, we first define common application components that are relevant w.r.t. edge computing. We then present a classification of proposed use cases and analyze them according to their expected benefits from edge computing and which components they use. Furthermore, we illustrate existing products and industry solutions that have recently surfaced and outline future research challenges. INDEX TERMS Edge computing, heterogeneous networks, next generation networking mobile applications, Internet of Things, ubiquitous computing.

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“…In light and medium traffic loads, OAF is the most effective algorithm in reducing the vehicular delay at the intersection compared to pretimed TLS. For cased with low-waiting times and high-throughput, a scheduling algorithm referred to as intelligent traffic light controlling (ITLC), was proposed in [14]. It has outperformed the OAF by 25% and 30% in terms of decreasing the delay time per vehicle and increasing the throughput at the intersection, respectively.…”

Section: A Vehicle-to-traffic Light Signal (V2tls) Communicationmentioning

confidence: 99%

Impact of Two-Way Communication of Traffic Light Signal-to-Vehicle on the Electric Vehicle State of Charge

Alturiman

Alsabaan

2019

IEEE Access

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Given the ongoing concerns on the emissions of greenhouse gases that contribute to global warming, the electric vehicle is considered as a promising technology solution for the reduction of these emissions in the transportation sector. Despite the numerous advantages of electric vehicles, the limited driving range is one of the prominent drawbacks that need to be addressed. Since the driving range is related to the battery's state of charge, which is, in turn, related to power consumption, there is a need to minimize the vehicle power consumption. Increased speeds and accelerations lead to a decrease of the state of charge. In this paper, we utilized vehicular networks that allow traffic light signals to communicate with approaching vehicles to avoid unnecessary high speeds and accelerations at an isolated intersection. This communication can be bidirectional, that is, from the vehicle to the traffic light signal, and vice versa. This enables the traffic light signal to adapt its green duration according to vehicular information and then send back its information to vehicles that in turn adapt their speeds based on the received information. We propose an optimization model that determines the optimum green duration and optimum vehicular speed, which ensures that the vehicle will cross the traffic light signal with maximum battery's state of charge. The analytical results indicate that the proposed approach which is adaptive in both speed and traffic light signal can achieve an improvement of the state of charge compared to other two approaches, which are adaptive in either speed or traffic signal.

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An Intelligent Traffic Light scheduling algorithm through VANETs

Cited by 26 publications

References 10 publications

Traffic Control System and Technologies: A Survey

Traffic Control System and Technologies: A Survey

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

Impact of Two-Way Communication of Traffic Light Signal-to-Vehicle on the Electric Vehicle State of Charge

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