Road Traffic Congestion Detecting by VANETs

Zhang, Enzhan; Zhang, Xia

doi:10.2991/eee-19.2019.39

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…Fuzzy logic is also a popular mechanism used in VANET. Besides been utilized in detecting traffic congestion as in COTEC [19], CARTIM [20], Rui et.al [21], and most recently Wang et.al [22] and TraD-VANET [23] it is also implemented in localization, clustering, beaconing dissemination, aggregation, and other processes in VANET [24][25][26][27]. The fuzzy mechanism detects traffic congestion based on vehicular speed and traffic density.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…The classification is accomplished by a majority vote of the most common of the predominant class known among its k nearest neighbors. It is also characterized as a lazy algorithm, defined as having less ability and is labor intensive when dealing with large datasets [22,23]. The operation of this algorithm is based on comparing a newly received record with the training records and finding training records identical to it.…”

Section: K-nn Classifiermentioning

confidence: 99%

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An overview of traffic congestion detection and classification techniques in VANET

Ramli

Rawi

2020

IJEECS

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<span>Vehicular traffic congestion has been and still is a major problem for many countries and knowledge about the traffic condition is important in order to schedule, plan and avoid traffic congestion. With recent development in technology, various efforts and methods are proposed in mitigating traffic congestion. Vehicular Ad-hoc NETwork (VANET) is very much in the hype in addressing this issue due to its capabilities and adaptation to scalability, highly dynamic topology as well as cooperative communication. A popular focus is in detecting and classisying traffic congestion which presents various techniques and methodologies. This paper presents an overview of traffic congestion detection and classification methods of various related techniques in VANET, organized from the research perspective. Qualitative analysis is presented to classify these strategies in its system architecture, detection and classification methods, as well as its simulated mobility environment and simulation tools used. The analysis is useful in understanding all the techniques and methods applied in resolving this issue in the research domain. </span>

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Section: Fuzzy Logicmentioning

confidence: 99%

Section: K-nn Classifiermentioning

confidence: 99%

An overview of traffic congestion detection and classification techniques in VANET

Ramli

Rawi

2020

IJEECS

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“…Even though they noticed a higher number of drivers reducing their speed, they explained that as a chain reaction where one vehicle braked and forced the following vehicles to brake or change lanes in order to avoid collisions. More recent articles investigate the exploitation of vehicular ad-hoc network (VANET) architecture to overcome the problem of congested metropolitan areas [28][29][30]. Conducted studies showed that such systems can effectively reduce traffic level inside cities, exploiting inter-communication among vehicles and supporting infrastructures.…”

Section: Introductionmentioning

confidence: 99%

Investigations of the Dynamic Travel Time Information Impact on Drivers’ Route Choice in an Urban Area—A Case Study Based on the City of Bialystok

Ziółkowski

Dziejma²

2021

Energies

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Increasing traffic volumes in cities leads to common traffic congestions building up, especially during peak hours. To protect city dwellers from excessive fuel exhaust and traffic noise and to prevent drivers from time loss due to overloaded routes, it is important to inform them about real-time traffic conditions and possible delays in advance. Effectively influencing drivers’ decisions to divert from an original route choice in case of traffic hinderance is essential, and application of dynamic travel information in the form of variable message signs (VMSs) is believed to be effective in these terms. The paper examines drivers’ willingness to divert from an initial route choice due to the information provided on VMS boards. Their behavior was analyzed in terms of their response to everyday and artificially elongated travel times displayed on the VMSs. Maximum simulated elongation reached 200% and 300% of the initial state, depending on the characteristics of the pre-peak conditions. To assess the effectiveness of VMSs, the changes in traffic intensities were statistically analyzed. In general, apart from few significant differences, the results revealed drivers’ ignorance of the travel time information provided on the VMS, regardless of the extension of the original times.

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“…This method alleviated the overload of the communication channel in [12], however, the vehicles that were responsible for collecting and processing traffic information would cause additional channel overhead. A method for the automatic clustering of vehicles was proposed in [14], which used a cluster head node to report the current position and speed information of the vehicle in clusters, to accomplish road traffic congestion detection. This method could reduce the amount of data traffic and avoid the problem of channel overload by aggregating the messages in a cluster, but the detection accuracy was low.…”

Section: Introductionmentioning

confidence: 99%