Managing DSRC and WAVE Standards Operations in a V2V Scenario

Morgan, Yasser

doi:10.1155/2010/797405

Cited by 58 publications

(23 citation statements)

References 17 publications

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“…In (9), the probability function is binomial distribution due to the third assumption of probability of connected vehicle being Bernoulli distribution. Then the conditional probability function can be obtained as…”

Section: ) No Stopped Vehicle Is Detected At Allmentioning

confidence: 99%

Queue Length Estimation Using Connected Vehicle Technology for Adaptive Signal Control

Tiaprasert

Zhang

Wang

et al. 2015

IEEE Trans. Intell. Transport. Syst.

124

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This paper presents a mathematical model for realtime queue estimation using connected vehicle (CV) technology from wireless sensor networks. The objective is to estimate the queue length for queue-based adaptive signal control. The proposed model can be applied without signal timing, traffic volume, or queue characteristics as basic inputs. The model is also developed so that it can work with both fixed-time signals and actuated signals. Furthermore, a discrete wavelet transform (DWT) is applied to the queue estimation algorithm in this paper for the first time. The purpose of the DWT is to enhance the proposed queue estimation to be more accurate and consistent regardless of the randomness in the penetration ratio. Experimental results are provided to validate the proposed model in both pretimed control and actuated control with a microscopic simulator, i.e., VISSIM. The results indicate that the proposed algorithm is able to estimate the queue length from VISSIM in the test case with pretimed signal control reasonably well. The results in actuated control cases, which have not been studied previously, showed that the proposed algorithm remains as accurate as the pretimed control cases. The accuracy of the proposed queue estimation algorithm is obtained without relying on basic inputs that other models typically require but are often impractical to obtain. Therefore, it is expected that the proposed queue estimation model is applicable for adaptive signal control using CV technology in practice.Index Terms-Adaptive signal control, discrete wavelet transform, traffic queue length estimation, wireless vehicle-to-vehicle communications. 1524-9050

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Section: ) No Stopped Vehicle Is Detected At Allmentioning

confidence: 99%

Queue Length Estimation Using Connected Vehicle Technology for Adaptive Signal Control

Tiaprasert

Zhang

Wang

et al. 2015

IEEE Trans. Intell. Transport. Syst.

124

View full text Add to dashboard Cite

show abstract

“…Network simulators should allow a detailed specification of network protocol layers including implementation of new communication standards for PHY/MAC layer, like DSRC (Dedicated Short Range Communications) with IEEE 802.11p and IEEE WAVE (Wireless Access in Vehicular Environments) [17]. VANET transport layer has to support multihop multicast/broadcast communication and considers routing protocols, like AODV (Ad-hoc On-demand Distance Vector) or DSR (Dynamic Source Routing).…”

Section: B Network Simulatorsmentioning

confidence: 99%

Aspects and trends in realistic VANET simulations

Grzybek

Seredynski

Danoy

et al. 2012

2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

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Realistic simulations of Vehicular Ad hoc Networks (VANETs) are necessary to evaluate novel technologies based on such networks and to prove benefits obtained from their implementation. This survey gathers from several research domains aspects that increase the quality of VANET simulations. It explains a multi-fold nature of VANETs and presents main building blocks of their simulation-traffic and network simulators. The paper proposes a comprehensive architecture for VANET simulation platform that focuses on producing reliable results. The architecture contains traffic and network simulators that communicate with each other in a dynamic and bi-directional way. The concept of a realistic traffic generator is introduced. It uses real-world data (e.g. maps, traffic volume counts) to model an activity-based traffic varying in time. The traffic generator aims at reproducing accurate vehicular traces for urban scenario. A higher level of realism can be obtained by modelling of human behaviour with intelligent agents and by the implementation of related subsystems, like traffic management and control or weather factors.

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“…Over the last several years, vehicular ad hoc networks (VANETs) [1], [2] have drawn considerable attention from research community because of the promising services and applications provided through the cooperation of equipped vehicles. The major goal of vehicular ad hoc networks is to increase the safety of passengers and enhance the driving experience by providing services, such as collision warning, lane change assistance, intelligent navigation and road traffic control.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we decided to use a comparison-based aggregation mechanism to substantially compress density information. To lower end-to-end delay, a single virtual gateway 1 should be identified to store local density information for each road segment. Basically, a hierarchy (i.e., tree) is established where a node represents a road segment and the height of a node represents the density of the corresponding road segment.…”

Section: Introductionmentioning

confidence: 99%

DMAP: Density Map Service in City Environments

Sahu

Hafid

Cherkaoui

2015

IEEE Trans. Intell. Transport. Syst.

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Vehicle density information is crucial for efficient functioning of many vehicular applications, including emergency notification, driver assistance, and infotainment applications. This information is used for evacuation planning in accident scenarios, finding alternate routes in the case of road congestion, and providing stable routing paths for uninterrupted internet connections. In a city scenario, it is a tedious task to continuously collect and share large volumes of data containing density information. In this paper, we propose a mechanism to create a density map for city environments. A hierarchy (i.e., tree) is established, where a node represents a road segment and the density is used to determine the height of the node; the root node represents the road segment having highest density. The purpose of this tree is to collect and aggregate density information starting from the leaves until the root node is reached. Then, the aggregated density information (i.e., density map) is forwarded down the hierarchy. For efficient aggregation of density information, we adopt an effective curve-fitting method where data are represented in an equation. Simulation results show that the proposed mechanism allows highly accurate computing of density map while generating low network overhead.Index Terms-Curve fitting, density measurement, traffic control, vehicular ad hoc networks.

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Managing DSRC and WAVE Standards Operations in a V2V Scenario

Cited by 58 publications

References 17 publications

Queue Length Estimation Using Connected Vehicle Technology for Adaptive Signal Control

Queue Length Estimation Using Connected Vehicle Technology for Adaptive Signal Control

Aspects and trends in realistic VANET simulations

DMAP: Density Map Service in City Environments

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