Traffic‐aware routing protocol with cooperative coverage‐oriented information collection method for VANET

Lo, Chun-Chih; Kuo, Yiyo

doi:10.1049/iet-com.2015.1016

Cited by 27 publications

(11 citation statements)

References 18 publications

(13 reference statements)

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“…In this paper, the fuzzy c-means (FCM) clustering algorithm was used to solve the threshold values of different traffic and opening states. The opening data and traffic flow at the boundary are continuously random, so it was necessary to build a clustering model to distinguish the opening state from others [11][12][13][14][15][16].…”

Section: Fcm Clustering Analysis Of Opening Traffic Data At Boundarymentioning

confidence: 99%

Dynamic Evaluation on the Traffic State of an Urban Gated Community by Opening the Micro-Inter-Road Network

2018

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The opening of a gated community to expand the micro-road network in an urban traffic system is an importance research topic related to urban congestion. To satisfy the demands of opening an early choosing case, this paper proposes a comprehensive selection framework on qualified communities and their appropriate opening times by describing the traffic state at the boundary road network accurately. The traffic entropy model and fuzzy c-means (FCM) method are used in this paper. In the framework, a new opening evaluation entropy model is built using basic theory of the thermodynamic traffic entropy method. The traffic state entropy values of the boundary road network and entropy production are calculated to determinate the opening time. In addition, a specific fuzzy range evaluation standard at a preset gated community is drawn with an FCM algorithm to verify the opening determination. A case study based on the traffic information in a simulated gated community in Shanghai is evaluated and proves that the findings of opening evaluation are in accordance with the actual situation. It is found that the micro-inter-road network of a gated community should be opened as the entropy value reaches 2.5. As the travel time is less than 20 s, the correlation between the opening entropy value and the journey delay time exhibits a good linear correlation, which indicates smooth traffic flow.

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Section: Fcm Clustering Analysis Of Opening Traffic Data At Boundarymentioning

confidence: 99%

Dynamic Evaluation on the Traffic State of an Urban Gated Community by Opening the Micro-Inter-Road Network

2018

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“…Taking into account the three factors, according to above equations, the overall weight value ( ) for the chosen CH is calculated using (7).…”

Section: B Cluster Head Selectionmentioning

confidence: 99%

“…In particular, velocity is considered as the only parameter for road condition. Lo and Kuo [7] also proposed a method of avoiding traffic congestion. A velocity map of the road where vehicles pass by is drawn by every vehicle, and is exchanged with neighbors in order to choose the road with low density.…”

Section: Introductionmentioning

confidence: 99%

“…A velocity map of the road where vehicles pass by is drawn by every vehicle, and is exchanged with neighbors in order to choose the road with low density. Similarly, the method proposed by Chi et al [8] is for vehicles to detect the average speed and queue length, and then send this information to the roadside units (RSUs). The RSUs receive the information, compare with the standard threshold, and then estimate the road condition and adjust the traffic flow.…”

Section: Introductionmentioning

confidence: 99%

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A New Traffic Congestion Detection and Quantification Method Based on Comprehensive Fuzzy Assessment in VANET

2018

KSII TIIS

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Recently, road traffic congestion is becoming a serious urban phenomenon, leading to massive adverse impacts on the ecology and economy. Therefore, solving this problem has drawn public attention throughout the world. One new promising solution is to take full advantage of vehicular ad hoc networks (VANETs). In this study, we propose a new traffic congestion detection and quantification method based on vehicle clustering and fuzzy assessment in VANET environment. To enhance real-time performance, this method collects traffic information by vehicle clustering. The average speed, road density, and average stop delay are selected as the characteristic parameters for traffic state identification. We use a comprehensive fuzzy assessment based on the three indicators to determine the road congestion condition. Simulation results show that the proposed method can precisely reflect the road condition and is more accurate and stable compared to existing algorithms.

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“…Accordingly, recent research works are analyzed with a detailed remark in advantages and the shortcomings. Here, 4 research papers from the year of 2017, 3 papers from the year of 2016, 2 papers from the year of 2015, 3 papers from the year of 2013, and 1 paper from the year of 2012 are taken for review. According to the existing research works, routing protocol in VANET is classied into 4 groups, namely, geographical‐based routing, traffic aware routing, urban environment‐based network, and stable routing.…”

Section: Review Of Related Workmentioning

confidence: 99%

FGWSO‐TAR: Fractional glowworm swarm optimization for traffic aware routing in urban VANET

Rewadkar¹,

Doye²

2017

Int J Communication

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SummaryIn mobile distributed applications, such as traffic alert dissemination, dynamic route planning, file sharing, and so on, vehicular ad hoc network (VANET) has emerged as a feasible solution in recent years. However, the performance of the VANET depends on the routing protocol in accord with the delay and throughput requirements. Many of the routing protocols have been extensively studied in the literature. Although there are exemptions, they escalate research challenges in traffic aware routing (TAR) protocol of VANET. This paper introduces the fractional glowworm swarm optimization (FGWSO) for the TAR protocol of VANET in an urban scenario that can identify the optimal path for the vehicle with less traffic density and delay time. The proposed FGWSO searches the optimal routing path based on the fitness function formulated in this paper. Fractional glowworm swarm optimization is the combination of the GWSO and fractional theory. Moreover, exponential weighted moving average is utilized to predict the traffic density and the speed of the vehicle, which is utilized as the major constraints in the fitness function of the optimization algorithm to find the optimal traffic aware path. Simulation of FGWSO shows the significant improvement with a minimal end-to-end delay of 6.6395 seconds and distance of 17.3962 m, respectively, in comparison with the other existing routing approaches. The simulation also validates the optimality of the proposed TAR protocol.KEYWORDS fractional theory, glowworm optimization, traffic aware routing protocol, traffic density, VANET | INTRODUCTIONNetworking concept has received increasing attention in the recent years with the advancement in the wireless communication technology. 1 The vehicular network has the advanced applications in the field of car manufacturing. The vehicular network has a wireless technology and has major advantages, such as intelligent transportation, urban monitoring, and internal access. 2 The vehicular network consists of vehicles with short range wireless communication devices 3,4 in a different environment. These substantial advantages made the wireless vehicular networks as an active area of research. Vehicular ad hoc network (VANET) is a new type of vehicular network, and it is a form of mobile ad hoc network.

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Traffic‐aware routing protocol with cooperative coverage‐oriented information collection method for VANET

Cited by 27 publications

References 18 publications

Dynamic Evaluation on the Traffic State of an Urban Gated Community by Opening the Micro-Inter-Road Network

Dynamic Evaluation on the Traffic State of an Urban Gated Community by Opening the Micro-Inter-Road Network

A New Traffic Congestion Detection and Quantification Method Based on Comprehensive Fuzzy Assessment in VANET

FGWSO‐TAR: Fractional glowworm swarm optimization for traffic aware routing in urban VANET

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