VANET Based Adaptive Traffic Signal Control

Maslekar, Nitin; Boussedjra, M.; Mouzna, Joseph; Labiod, Houda

doi:10.1109/vetecs.2011.5956305

Cited by 61 publications

(20 citation statements)

References 4 publications

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“…Car-to-car communication based adaptive traffic signal control system is discussed in [25]. The approach reduces the queue length to reduce the waiting time at the crossroads for the vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Traffic signals can respond to the communication between vehicles to estimate the density of vehicles around it and consequently adjust the signal timing. In [25] and [26] adaptive traffic light systems based on wireless connectivity between fixed controller nodes and vehicles at crossroads are described. These systems are planned and developed with the objective of improving traffic smoothness, reducing the waiting time at crossroads and helping in the prevention of accidents.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Vehicles themselves are providing this information without the need of extra infra-structure. The authors have developed an adaptive traffic control system similar to the proposal given by [25], [26] in their work. The system is implementing the AODV routing algorithm without considering any clustering method.…”

Section: Literature Reviewmentioning

confidence: 99%

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ATSOT: Adaptive Traffic Signal Using mOTes

Bedi

Jindal

Dhankani

et al. 2015

Databases in Networked Information Systems

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Abstract. This paper presents design and development of Adaptive Traffic Signal using mOTes (ATSOT) system for crossroads to reduce the average waiting time in order to help the commuter drive smoother and faster. Motes are used in the proposed system to collect and store the data. This paper proposes an adaptive algorithm to select green light timings for crossroads in real time environment using clustering algorithm for VANETs. Clustering algorithms are used in VANETs to reduce message transfer, increase the connectivity and provide secure communication among vehicles. Direction and position of vehicles is used in literature for clustering. In this paper, difference in the speed of vehicles is also considered along with direction, node degree, and position to create reasonably stable clusters. A mechanism to check the suitability of cluster initiator is also proposed in the paper. The proposed ATSOT system can be used for hassle free movement of vehicles across the crossroads. Prototype of the system has been designed and developed using open source software tools: MOVE for the Mobility model generation, SUMO for traffic simulation, TraCI for traffic control Interface and Python for client scripting to initiate and control the simulation. Results obtained by simulating ATSOT approach are compared with both OAF algorithm for adaptive traffic signal control and pre-timed approach to show the efficiency in terms of reduced waiting timing at the crossroads. Results are also compared with pre-timed method for single lane and multi-lane environment using Webster's delay function.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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ATSOT: Adaptive Traffic Signal Using mOTes

Bedi

Jindal

Dhankani

et al. 2015

Databases in Networked Information Systems

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“…N.MASLEKAR el al. in [12] developed a dynamic system based on car to car communication. This system reduces the waiting time of the vehicles at the intersection along with the reduction in queue length.…”

Section: Fig 1: Sketch Map Of Urban Arterial Roadmentioning

confidence: 99%

Speed Range Prediction for Traffic Light Control System

Borkar¹,

Jenekar²

2012

IJCA

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This paper discuses a proposed systems for acquiring the next intersection timing and generating the required speed at current intersection to cross next intersection without stopping at it. The system is speed module for next intersection prediction embedded in intelligent traffic light control system at intersection. It can also be designed for GPS based navigation system. For efficiently predicting the time and speed required for crossing next intersection without stopping at it centralized approach is taken into account, the distance between current intersection and next intersection and traffic signal timings of next intersection are considered as input to the system. The traffic signal timings are more on highway than on city road. System then generates the required speed in range to cross next intersection without stopping at it. Speed generated by the system is in specified range like 32Km/Hr to 40 Km/Hr. Also it can't exceed the speed limit of road.

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“…Networks [23,11]. Nonetheless, as observed by [21], this remains an open problem as such systems have thus far been limited to small-scale academic experiments and a timely integration into current road networks seems unlikely.…”

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confidence: 99%

Traffic light status detection using movement patterns of vehicles

Campbell

Amor

Ang

et al. 2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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Traditional methods for detecting the status of traffic lights used in autonomous vehicles may be susceptible to errors, which is troublesome in a safety-critical environment. In the case of vision-based recognition methods, failures may arise due to disturbances in the environment such as occluded views or poor lighting conditions. Some methods also depend on high-precision meta-data which is not always avail-

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VANET Based Adaptive Traffic Signal Control

Cited by 61 publications

References 4 publications

ATSOT: Adaptive Traffic Signal Using mOTes

ATSOT: Adaptive Traffic Signal Using mOTes

Speed Range Prediction for Traffic Light Control System

Traffic light status detection using movement patterns of vehicles

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