A Developed Realistic Urban Road Traffic in Erbil City Using Bi-directionally Coupled Simulations

Hasoon, Saad Talib; Mahdi, Muhammed Abaid

doi:10.25212/lfu.qzj.2.2.03

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…These strategies collect information about traffic through the use of cutting-edge technologies like connected vehicles (CVs), Wi-Fi, Bluetooth sensors, cellular networks and smartphones [7,8]. These tools not only open up new possibilities for collecting crowdsourced data, but also produce valuable information that can be used in a variety of transportation analyses, including those concerned with traffic safety [9,10,11,12], public transit [13,14], and energy consumption and emissions [15,16]. Big data is being used in the transportation sector to propose novel ideas and solutions that have not been explored before [17].…”

Section: -Introductionmentioning

confidence: 99%

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: -Introductionmentioning

confidence: 99%

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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Grouping vehicles in Vehicular Social Networks

Mahdi

Hasson

2017

KJAR

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As vehicles have become intelligent objects, creating social friendships among them is justifiable. Internet of Vehicles (IoVs) and Vehicular Ad-hoc NETworks (VANETs) are associated terms that are recently highlighted to improve the transportation systems. Intelligent vehicles that can communicate with each other are the main component of vehicular networks (VNs). Indeed, they have resources for that intelligence: CPU, transceiver, sensors and memory. On the other side, social networks (SNs) are also brought to light to mainly study the human relationships. In this context, gathering these vehicles in social groups is reasonable to share their information by bridging VN and SN in a multidisciplinary research direction called vehicular social networks (VSNs).This paper will present a model to group vehicles in two types of social groups. Casual groups and permanent or formal groups will be discussed to increase social ties among vehicles that have the same interests. The results will be analyzed to clarify the most important factors that affect such these relationships.

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A Developed Realistic Urban Road Traffic in Erbil City Using Bi-directionally Coupled Simulations

Cited by 2 publications

References 11 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

Grouping vehicles in Vehicular Social Networks

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