A survey of vehicular-based cooperative traffic information systems

Seredynski, Marcin; Bouvry, Pascal

doi:10.1109/itsc.2011.6083055

Cited by 40 publications

(23 citation statements)

References 22 publications

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“…In such systems updates are far from real-time-lag time is typically in the range of 2 to 30 minutes [12] (although this is system designers' choice rather than technical restriction). However, bandwidth limitation, dissemination delays, and communication costs are the main drawbacks of such systems [23,24]. These drawbacks can be addressed …”

Section: Centralised Vs Decentralised Vs Distributed Traffic Data Mmentioning

confidence: 99%

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Mitigating flash crowd effect using connected vehicle technology

Grzybek

Danoy

Bouvry

et al. 2015

Vehicular Communications

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A Flash Crowd Effect (FCE) occurs when in the case of non-recurring congestion a large portion of drivers follows similar re-routing advice. Consequently, congestion is transferred from one road to another. Coping with the FCE is challenging, especially if the congestion results from a temporary loss of capacity (e.g. due to a traffic incident). The existing route guidance systems do not address FCE, as they either do not consider the effects of guidance on the rest of the road network, or predict link travel times based on the number of vehicles traveling on the link, which in the case of the loss of capacity is unreliable. We demonstrate that the FCE can be addressed in a distributed way route guidance, connected vehicle technology, flash crowd effect.

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Section: Centralised Vs Decentralised Vs Distributed Traffic Data Mmentioning

confidence: 99%

“…based on V2V communication only) allow for efficient traffic information crowd-sourcing even with low penetration rates of the system [8] (for details please refer to [24] In this article we introduce an alternative approach explicitly designed to cope with the FCE (the above-mentioned systems were not evaluated in the …”

mentioning

confidence: 99%

Mitigating flash crowd effect using connected vehicle technology

Grzybek

Danoy

Bouvry

et al. 2015

Vehicular Communications

Self Cite

View full text Add to dashboard Cite

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“…[3]- [5] are examples of these systems, using cellular networks [6]. At the other cooperative systems, central management and control part has been replaced by a multilayer architecture where the analysis of traffic data is done inside each moving node.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Two-Purpose Peer-to-Peer VANET to Disseminate Traffic Information and Reduce Travel Time

Valibak¹,

Balouchzahi²,

Raahemifar³

et al. 2015

IJFCC

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Abstract-In this paper, we propose a two purpose structure that can do vehicle navigating and packet routing simultaneously in an efficient way. The algorithm suggests the shortest travel time path to the drivers and finds reliable routes for traffic information messages. In order to reduce traveling time, vehicles are directed to the paths with lower car congestion. Because such paths have a higher risk of radio coverage partitioning, it is more desired to disseminate traffic information packets from routes that have more probability of radio connectivity, to increase successful ratio on delivering messages. For this purposes, it is necessary to assess vehicles distribution on city roads periodically to evaluate real-time car congestion and radio connectivity of roads. The peer-to-peer layer is constructed in a fast and simple way, without any overhead or extra message exchanging between nodes. The system can optimally save the limited bandwidth of vehicular network as well as reducing the travel time of vehicles. Due to completely cooperative characteristic of this system, there is no need to install any infrastructure at road network.

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“…In addition, by the development of cities, the need for public services and facilities has been increased. This issue draws attentions to the subjects such as urban transportation (Seredynski and Bouvry, 2011). Due to the increasing volume of urban traffic and its undesirable effects on economy, environment, and community health, effective management of urban traffic has become a remarkable subject.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Energy Consumption in Vehicular Sensor Networks by Clustering Using Fuzzy C-Means and Fuzzy Subtractive Algorithms

Ebrahimi

Pahlavani

Masoumi

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Traffic monitoring and managing in urban intelligent transportation systems (ITS) can be carried out based on vehicular sensor networks. In a vehicular sensor network, vehicles equipped with sensors such as GPS, can act as mobile sensors for sensing the urban traffic and sending the reports to a traffic monitoring center (TMC) for traffic estimation. The energy consumption by the sensor nodes is a main problem in the wireless sensor networks (WSNs); moreover, it is the most important feature in designing these networks. Clustering the sensor nodes is considered as an effective solution to reduce the energy consumption of WSNs. Each cluster should have a Cluster Head (CH), and a number of nodes located within its supervision area. The cluster heads are responsible for gathering and aggregating the information of clusters. Then, it transmits the information to the data collection center. Hence, the use of clustering decreases the volume of transmitting information, and, consequently, reduces the energy consumption of network. In this paper, Fuzzy C-Means (FCM) and Fuzzy Subtractive algorithms are employed to cluster sensors and investigate their performance on the energy consumption of sensors. It can be seen that the FCM algorithm and Fuzzy Subtractive have been reduced energy consumption of vehicle sensors up to 90.68% and 92.18%, respectively. Comparing the performance of the algorithms implies the 1.5 percent improvement in Fuzzy Subtractive algorithm in comparison.

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A survey of vehicular-based cooperative traffic information systems

Cited by 40 publications

References 22 publications

Mitigating flash crowd effect using connected vehicle technology

Mitigating flash crowd effect using connected vehicle technology

An Optimized Two-Purpose Peer-to-Peer VANET to Disseminate Traffic Information and Reduce Travel Time

Optimizing Energy Consumption in Vehicular Sensor Networks by Clustering Using Fuzzy C-Means and Fuzzy Subtractive Algorithms

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