Awarenessability and Influences on Raising of Traffic Accidents Through the Content of Social Media in the Internet of Things : A Practical Empirical Study by the Internet of Things and Multimedia on university students in western Saudi Arabia

Alharbe, Mahmood Abdulghani

doi:10.1109/i-smac.2018.8653739

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…After 2016, some studies discussed the RTA issue in relation to different fields, such as data mining for analyzing RTAs [34] using the internet of things (IoT) for classifying reasons for RTAs [35] employing the IoT to create e-awareness application against RTAs [35], and detecting RTAs via the IoT with ANNs [36]. In terms of prediction [37], performed the first study was conducted in 1995 and used three ARIMA models with a Box-Jenkins methodology for time-series data to predict the total traffic accidents, injuries, and fatalities in the KSA.…”

Section: Saudi Arabia As a Case Studymentioning

confidence: 99%

A Deep-Learning Model for Predicting and Visualizing the Risk of Road Traffic Accidents in Saudi Arabia: A Tutorial Approach

Alrajhi¹,

Kamel²

2019

IJACSA

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Around the world, road traffic accidents (RTAs) cause significant concerns for decision makers and researchers on traffic safety. The diversity, rarity, and interconnectivity of historical data on factors causing car accidents point to the need for more focused studies for analyzing, predicting, and visualizing the risk of accidents over the short and long term for preventive purposes. There are many techniques and tools applied to analyze, forecast, and visualize risk. Most RTA studies have applied linear time-series methods to forecasting the risk with limited studies applying machine-learning and deeplearning techniques, especially in Saudi Arabia. Recently, many global studies have applied long short-term memory (LSTM) networks, which can be used to automatically learn the temporal dependence structures for challenging time-series forecasting problems. This paper displays a tutorial for designing a prototype of an interactive analytical tool based on a multivariate LSTM model for time-series data to predict future car accidents, fatalities, and injuries in the Kingdom of Saudi Arabia (KSA). This interactive tool visualizes the real data with the predicted values regionally in a web browser with Python. The tutorial represents the annual data of the period between 1417 (1996) and 1433 (2013), then uses the data with some contributing factors, such as population, gender, nationality, number of vehicles, and length of road, to generate the input data and predict the future values of accidents, fatalities, and injuries up to the year 1452 (2030). After that the real and predicted values are visualized regionally on an interactive map that represents the degree of risk. Finally, the paper discusses the evaluation and utilization of the proposed prototype in the future in the field of road safety.

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Section: Saudi Arabia As a Case Studymentioning

confidence: 99%

A Deep-Learning Model for Predicting and Visualizing the Risk of Road Traffic Accidents in Saudi Arabia: A Tutorial Approach

Alrajhi¹,

Kamel²

2019

IJACSA

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“…Social media platforms like Twitter and Facebook have been engaging millions of users across the globe for years now, with the number of users increasing every day. Twitter is a microblogging website with messages restricted to only 280 characters, which makes it a simple and practical interface for user and smart object interaction [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of an IoT Platform with Virtual Reality and Social Media Integration

Krishnan

Simiscuka

Muntean

2020

2020 IEEE 21st International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

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The Internet of Things (IoT) is a growing network of physical objects where the devices are connected to the Internet through unique addressing schemes and multiple protocols. The increase of IoT devices in the recent years presents significant challenges in terms of security, authentication and usability. The recently introduced Social Internet of Things (SIoT) tries to address these challenges with the virtualisation of IoT devices and the use of an infrastructure where people and IoT devices can communicate with each other, both in the real-world and virtual-world, through a common platform. In the proposed SIoT architecture, IoT devices can be operated by virtual reality (VR) headsets and Twitter, a social media platform. The aim of the platform is to allow users to seamlessly operate IoT devices, using their preferred interface: remotely with text messages (i.e. tweets) and VR headsets or operate the IoT devices directly. This paper also describes the implementation of a testbed and presents the performance analysis of the solution, demonstrating its feasibility and low latency.

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A Cost-Efficient Management Protocol for Mobile Crowd-Sensing in Urban Vehicular Scenarios

Abdulkadhim

Alrashed

Alrammahi

et al. 2020

2020 3rd International Conference on Engineering Technology and Its Applications (IICETA)

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Awarenessability and Influences on Raising of Traffic Accidents Through the Content of Social Media in the Internet of Things : A Practical Empirical Study by the Internet of Things and Multimedia on university students in western Saudi Arabia

Cited by 4 publications

References 12 publications

A Deep-Learning Model for Predicting and Visualizing the Risk of Road Traffic Accidents in Saudi Arabia: A Tutorial Approach

A Deep-Learning Model for Predicting and Visualizing the Risk of Road Traffic Accidents in Saudi Arabia: A Tutorial Approach

Performance Analysis of an IoT Platform with Virtual Reality and Social Media Integration

A Cost-Efficient Management Protocol for Mobile Crowd-Sensing in Urban Vehicular Scenarios

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