Adaptive Traffic Signal Flow Control Using Wireless Sensor Networks

Bhuvaneswari, P. T. V.; Raj, G.V. Arun; Balaji, R.; Kanagasabai, S.

doi:10.1109/cicn.2012.38

Cited by 17 publications

(18 citation statements)

References 6 publications

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“…An alternate approach to wireless transceivers is given by Bhuvaneswari et al [33]. Here, the VA is a passive Radio Frequency Identification (RFID) tag and the IA is an RFID reader.…”

Section: Strategies Where Vehicles Are Passive Participantsmentioning

confidence: 99%

“…Specific limitations include the range of the communication, communication can only be one way, and typically only an ID can be stored on a passive RFID tag. Interestingly, Bhuvaneswari et al [33] states vehicle information such as velocity is stored in a remote database. No details are given into how this information is stored into the remote database.…”

Section: Strategies Where Vehicles Are Passive Participantsmentioning

confidence: 99%

“…The algorithms use this information to reduce times waiting at the intersection. In these cases, the level of prediction is determined by the range of the IA's wireless signal [31][32][33][34][35]37].…”

Section: Reactive Versus Predictive Traffic Signal Optimizationmentioning

confidence: 99%

See 2 more Smart Citations

A survey of vehicular communications for traffic signal optimization

Florin

Olariu

2015

Vehicular Communications

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“…An alternate approach to wireless transceivers is given by Bhuvaneswari et al [33]. Here, the VA is a passive Radio Frequency Identification (RFID) tag and the IA is an RFID reader.…”

Section: Strategies Where Vehicles Are Passive Participantsmentioning

confidence: 99%

Section: Strategies Where Vehicles Are Passive Participantsmentioning

confidence: 99%

See 1 more Smart Citation

A survey of vehicular communications for traffic signal optimization

Florin

Olariu

2015

Vehicular Communications

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“…Thus, both the direction and time to be cleared are chosen optimally. They observed that in the fixed time approach, the waiting time increased as the number of vehicles increased irrespective of their speeds and speed factors [16].…”

Section: Wireless Sensor Network (Wsn)mentioning

confidence: 99%

“…Bhuvaneswari et al [16] further support [15] by developing a traffic congestion control system ATSWSN that is adaptive in nature. However it differentiates from Yousef et al [15].…”

Section: Wireless Sensor Network (Wsn)mentioning

confidence: 99%

Techniques for Smart Traffic Control: An In-depth Review

Hawi¹,

Okeyo²,

Kimwele³

2015

IJCATR

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Inadequate space and funds for the construction of new roads and the steady increase in number of vehicles has prompted scholars to investigate other solutions to traffic congestion. One area gaining interest is the use of smart traffic control systems (STCS) to make traffic routing decisions. These systems use real time data and try to mimic human reasoning thus prove promising in vehicle traffic control and management. This paper is a review on the motivations behind the emergence of STCS and the different types of these systems in use today for road traffic management. They include -fuzzy expert systems (FES), artificial neural networks (ANN) and wireless sensor networks (WSN). We give an in depth study on the design, benefits and limitations of each technique. The paper cites and analyses a number of successfully tested and implemented STCS. From these reviews we are able to derive comparisons of the STCS discussed in this paper. For instance, for a learning or adaptive system, ANN is the best approach; for a system that just routes traffic based on real time data and does not need to derive any data patterns afterwards, then FES is the best approach; for a cheaper alternative to the FES, then WSN is the least costly approach. All prove effective in traffic control and management with respect to the context in which each of them is used.

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Active Traffic Signal Decisions Using Vector‐Matrix Multiplication

Jang

Jeon

2023

Advanced Intelligent Systems

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A novel methodology in the manner of vector‐matrix multiplication (VMM) architecture is suggested for intelligently determining traffic signal changes to enhance the flow of urban traffic. Unlike the conventional prediction‐based traffic model, a real‐time decision model considering the traffic density at each transport section is established, which simplifies the traffic signal decision process as a convolutional transformation. Compared with a periodically repetitive signal changing system, the suggested VMM system actively optimizes the signal configuration in an irregular shape according to the traffic density distribution, resulting in reduction in the time cost with highly improved decision efficiency. With this system based on particle dynamics, the travel time is reduced by ≈10% at the same pass ratio for different road structures (one‐way, bidirectional, and intersectional transport). The pass ratio and resulting flow dynamics can be controllable using the different transformation matrix selections according to the traffic conditions. In addition, the analog conductance of the memristor device to the transformation matrix elements is applied, maintaining its reduction rate with a deviation tolerance of the VMM process up to ≈50%. It is believed that VMM‐based signal decision platform can lead to great progress for fast and efficient transport in complex urban traffic networks.

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Adaptive Traffic Signal Flow Control Using Wireless Sensor Networks

Cited by 17 publications

References 6 publications

A survey of vehicular communications for traffic signal optimization

A survey of vehicular communications for traffic signal optimization

Techniques for Smart Traffic Control: An In-depth Review

Active Traffic Signal Decisions Using Vector‐Matrix Multiplication

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