Pervasive computing of adaptable recommendation system for head-up display in smart transportation

Abu‐Khadrah, Ahmed; Jarrah, Muath; Alrababah, Hamza; Alqattan, Zakaria N.M.; Akbar, Habibullah

doi:10.1016/j.compeleceng.2022.108204

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…These study the behaviour of citizens to find patterns and predict more energy-efficient products that might interest them. Then there are the works of Quijano-Sánchez et al (2020) [93] and Mordacchini et al [29], which, in the political and governmental sphere, seek to establish recommendations according to the description of elements available in the city. Employed in nine articles, knowledge-based filtering is based on user references to plan and create smart cities [14,61,68].…”

Section: Recommender System Techniquesmentioning

confidence: 99%

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Emerging Perspectives on the Application of Recommender Systems in Smart Cities

Andrade-Ruiz,

Carrasco,

Porcel

et al. 2024

Electronics

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Smart cities represent the convergence of information and communication technologies (ICT) with urban management to improve the quality of life of city dwellers. In this context, recommender systems, tools that offer personalised suggestions to city dwellers, have emerged as key contributors to this convergence. Their successful application in various areas of city life and their ability to process massive amounts of data generated in urban environments has expedited their status as a crucial technology in the evolution of city planning. Our methodology included reviewing the Web of Science database, resulting in 130 articles that, filtered for relevancy, were reduced to 86. The first stage consisted of carrying out a bibliometric analysis with the objective of analysing structural aspects with the SciMAT tool. Secondly, a systematic literature review was undertaken using the PRISMA 2020 statement. The results illustrated the different processes by which recommendations are filtered in areas such as tourism, health, mobility, and transport. This research is seen as a significant breakthrough that can drive the evolution and efficiency of smart cities, establishing a solid framework for future research in this dynamic field.

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Section: Recommender System Techniquesmentioning

confidence: 99%

“…Public Transport Optimisation: Through data analysis and user feedback, informed decisions can be made on public transport optimisation, e.g., bus frequency can be adjusted based on actual demand, routes can be reorganised to be more efficient, or accessibility and safety at stops can be improved [93].…”

mentioning

confidence: 99%

Emerging Perspectives on the Application of Recommender Systems in Smart Cities

Andrade-Ruiz,

Carrasco,

Porcel

et al. 2024

Electronics

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“…In addition to interface development, the integration of machine learning algorithms has emerged as a prominent area of research in enhancing the capabilities and adaptability of AR-HUD systems. Abu-Khadrah et al (2022) introduced an adaptive algorithm recommendation system that leverages computer vision and depth neural networks to improve the design of head-up display (HUD). By enhancing object detection speed in multi-layer smart city environments, this system reduces processing time, errors, and computational requirements.…”

Section: Related Workmentioning

confidence: 99%

Exploring Enhancement of AR-HUD Visual Interaction Design Through Application of Intelligent Algorithms

Teng

Wan

Kong

2023

International Journal of Information Technologies and Systems Approach

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This study aims to optimize the visual interaction design of AR-HUD and reduce cognitive load in complex driving situations. An immersive driving simulation incorporating eye-tracking technology was utilized to analyze objective physiological indices and measure subjective cognitive load using the NASA-TLX. Additionally, a visual cognitive load index was integrated into a BP-GA neural network model for load prediction, enabling the derivation of an optimal solution for AR-HUD design. The optimized AR-HUD interface demonstrated a significant reduction in cognitive load compared to the previous prototype. The experimental group achieved a mean total score of 25.63 on the WP scale, whereas the control group scored 43.53, indicating a remarkable improvement of 41.4%. This study presents an innovative approach to optimizing AR-HUD design, effectively reducing cognitive load in complex driving situations. The findings demonstrate the potential of the proposed algorithm to enhance user experience and performance.

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