Adaptive Traffic Light Control of Multiple Intersections in WSN-Based ITS

Zhou, Binbin; Cao, Jiannong; Wu, Hejun

doi:10.1109/vetecs.2011.5956434

Cited by 62 publications

(22 citation statements)

References 7 publications

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“…Moreover, B. Zhou et al studied adaptive traffic light control for smoothing vehicles' travel and maximizing the traffic throughout for both single and multiple lanes [21], [22]. In addition, the work [23] proposed a three-tier structure for traffic light control.…”

Section: B Similar Workmentioning

confidence: 99%

The Fog Computing Paradigm: Scenarios and Security Issues

Stojmenović¹,

Wen²

2014

Annals of Computer Science and Information Systems

771

437

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Abstract-Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. In this article, we elaborate the motivation and advantages of Fog computing, and analyse its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks. We discuss the state-of-the-art of Fog computing and similar work under the same umbrella. Security and privacy issues are further disclosed according to current Fog computing paradigm. As an example, we study a typical attack, man-in-the-middle attack, for the discussion of security in Fog computing. We investigate the stealthy features of this attack by examining its CPU and memory consumption on Fog device.

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Section: B Similar Workmentioning

confidence: 99%

The Fog Computing Paradigm: Scenarios and Security Issues

Stojmenović¹,

Wen²

2014

Annals of Computer Science and Information Systems

771

437

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“…Their goal is to minimize the average waiting time in order to reduce the queues' length. In [22] and [23] the authors proposed an adaptive traffic light control algorithm that adjusts both the sequence and length of traffic lights in accordance with the real time traffic detected. The proposed algorithms consider several traffic factors (traffic volume, waiting time, vehicle density) in order to determine the optimal green light duration.…”

Section: Traffic Lights Management Techniques Using Wireless Sensor Nmentioning

confidence: 99%

A dynamic traffic light management system based on wireless sensor networks for the reduction of the red-light running phenomenon

Collotta

Pau

Scatà

et al. 2014

Transport and Telecommunication Journal

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The real-time knowledge of information concerning traffic light junctions represents a valid solution to congestion problems with the main aim to reduce, as much as possible, accidents. The Red Light Running (RLR) is a behavioural phenomenon that occurs when the driver must to choose to cross (or not) the road when the traffic light changes from green to yellow. Most of the time the drivers cross even during transitions from yellow to red and, as a consequence, the possibility of accidents increases. This often occurs because the drivers wait too much in the traffic light queue as a consequence of the fact that the traffic light is not well balanced. In this paper we propose a technique that, based on information gathered through a wireless sensor network, dynamically processes green times in a traffic light of an isolated intersection. The main aim is to optimise the waiting time in the queue and, as a consequence, reduce the RLR phenomenon occurrence.

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“…In this approach discontinuous traffic flow and special traffic circumstances are used for green lights length and sequence scheduling in order to reduce the average waiting time and increase the throughput. An extension of this approach for using at multiple intersections is proposed by (Zhou et al (2011)) where real-time traffic data such as vehicle density, waiting time and number of stops, are used for adjusting the green lights sequences in multiple intersections. TAPIOCA (distribuTed and AdaPtive IntersectiOns Control Algorithm) (Faye et al (2012)) is another adaptive TLS system for multiple intersections that uses real-time data for adjusting the green lights sequences in multiple intersections.…”

Section: Traffic Light Signalsmentioning

confidence: 99%