Grouping vehicles in Vehicular Social Networks

Mahdi, Muhammed Abaid; Hasson, Saad Talib

doi:10.24017/science.2017.3.42

Cited by 7 publications

(4 citation statements)

References 22 publications

(24 reference statements)

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“…For highway sections, HCM uses traffic volume as the primary LOS metric [2]. Each LOS's flow is detailed in Table 2 [1]. In this investigation, the LOS was determined hourly based on traffic volume collected by MIDAS sensors.…”

Section: Ground Truth Losmentioning

confidence: 99%

“…For the purpose of estimating traffic performance and state, the Highway Capacity Manual (HCM) defines six LOS. The HCM offers formulas for calculating LOS based on traffic volume and road conditions [1]. An important part of LOS evaluation is the speed, flow, and density of the traffic [1,2,3].…”

Section: -Introductionmentioning

confidence: 99%

“…The HCM offers formulas for calculating LOS based on traffic volume and road conditions [1]. An important part of LOS evaluation is the speed, flow, and density of the traffic [1,2,3]. The transportation agencies often require hourly data on the traffic situation and LOS for various stretches of freeway, either in real time or historically fixed location sensors like remote traffic microwave sensors (RTMS), loop detectors, laser sensors, magnetic sensors, license plate recognition (LPR), and video image systems [4,5] have long been used to collect traffic data (travel time, speed, density, and flow).…”

Section: -Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using a new algorithm in Machine learning Approaches to estimate level-of-service in hourly traffic flow data in vehicular ad hoc networks

Turki¹,

Hasson²

2023

Tikrit J. Pure Sci.

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The primary goals of transportation agencies and researchers studying traffic operations are to ease traffic and increase road safety through the use of vehicular ad hoc networks. Agencies can't achieve their goals without reliable and consistent data on the current traffic situation. The Level-of-Service (LOS) index is a helpful measure of freeway traffic operations. Conventional fixed-location cameras and sensors are impractical and expensive for gathering reliable traffic density data on every road in large networks. Flow data is a new, low-cost option that has the potential to boost safety and operations. This study proposes an algorithm for hourly LOS assessment by incorporating flow data provided by the MIDAS (Motorway Incident Detection and Automatic Signaling) system. The proposed algorithm uses machine learning techniques to classify LOS data based on the flow of traffic. The input features that are subject to prediction are a group of technical indicators. The real-world LOS was determined by analyzing data from stationary sensors. The outcomes demonstrate that technical indicators can be utilized to enhance the accuracy of LOS estimation (Random Forest= 93.1, k-nearest neighbors = 92.5, and Support Vector Machine = 91.4). The current work introduces a novel approach to the selection of technical indicators and their use as features, which allows for highly accurate short-term prediction of LOS estimation.

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Section: Ground Truth Losmentioning

confidence: 99%

Section: -Introductionmentioning

confidence: 99%

Section: -Introductionmentioning

confidence: 99%

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Using a new algorithm in Machine learning Approaches to estimate level-of-service in hourly traffic flow data in vehicular ad hoc networks

Turki¹,

Hasson²

2023

Tikrit J. Pure Sci.

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“…Figure 16, demonstrates an overall view of active topology extraction from social networks. 2017) in [87] discussed different grouping such as classification and clustering which are out of the VANET networks. For example, the main technique in grouping nodes in the classification model is finding the nature of each group based on the similarity that they can have with each other.…”

Section: Recent Work In the Context Of Vsnmentioning

confidence: 99%

A Comprehensive Survey on the Convergence of Vehicular Social Networks and Fog Computing

Miri¹,

Pazzi²

2021

Preprint

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In recent years, the number of IoT devices has been growing fast which leads to a challenging task for managing, storing, analyzing, and making decisions about raw data from different IoT devices, especially for delay-sensitive applications. In a vehicular network (VANET) environment, the dynamic nature of vehicles makes the current open research issues even more challenging due to the frequent topology changes that can lead to disconnections between vehicles. To this end, a number of research works have been proposed in the context of cloud and fog computing over the 5G infrastructure. On the other hand, there are a variety of research proposals that aim to extend the connection time between vehicles. Vehicular Social Networks (VSNs) have been defined to decrease the burden of connection time between the vehicles. This survey paper first provides the necessary background information and definitions about fog, cloud and related paradigms such as 5G and SDN. Then, it introduces the reader to Vehicular Social Networks, the different metrics and the main differences between VSNs and Online Social Networks. Finally, this survey investigates the related works in the context of VANETs that have demonstrated different architectures to address the different issues in fog computing. Moreover, it provides a categorization of the different approaches and discusses the required metrics in the context of fog and cloud and compares them to Vehicular social networks. A comparison of the relevant related works is discussed along with new research challenges and trends in the domain of VSNs and fog computing.

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A Clustering Approach to Model the Data Dissemination in VANETs

Hasson

Abbas

2021

2021 1st Babylon International Conference on Information Technology and Science (BICITS)

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Grouping vehicles in Vehicular Social Networks

Cited by 7 publications

References 22 publications

Using a new algorithm in Machine learning Approaches to estimate level-of-service in hourly traffic flow data in vehicular ad hoc networks

Using a new algorithm in Machine learning Approaches to estimate level-of-service in hourly traffic flow data in vehicular ad hoc networks

A Comprehensive Survey on the Convergence of Vehicular Social Networks and Fog Computing

A Clustering Approach to Model the Data Dissemination in VANETs

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