Smart Transportation in Developing Countries: An Internet-of-Things-Based Conceptual Framework for Traffic Control

Farman, Haleem; Khan, Zahid; Jan, Bilal; Boulila, ‪Wadii; Habib, Shabana; Koubâa, Anis

doi:10.1155/2022/8219377

Cited by 10 publications

(6 citation statements)

References 28 publications

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“…The study in [127] worked on smart transportation in developing countries. The study highlighted the challenges and consequences of existing transportation system in response to rapid population growth.…”

Section: Systematic Analysis Of the Application Of 4ir Technologies I...mentioning

confidence: 99%

4IR Applications in the Transport Industry: Systematic Review of the State of the Art with Respect to Data Collection and Processing Mechanisms

Ajayi,

Kurien,

Djouani

et al. 2024

Preprint

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Transportation systems through the ages have seen drastic evolutions in terms of transportation methods, speed of transport, infrastructure, technology, connectivity, influence on the environment, and accessibility. The massive transformation seen in the transportation sector has been fueled by the industrial revolutions which have continued expansion and progress into the fourth industrial revolution. However, the methodologies of data collection and processing used by the many drivers of this progress differ. The significance of standardized data and its mode of collection cannot be overstated as it has a major impact on the process outcomes. In order to achieve a better understanding of the impact of this, this study methodically reviewed the literature on the subject of the data collection and processing mechanism of 4IR technologies in the context of the transport industry with an emphasis on the areas of accomplishment of several studies and drawing attention to their limitations. Although the industry has advanced significantly as a result of the adoption and application of various research studies in this field, considerable focus needs to be placed in the areas of standardized data collection and computational approaches, which will ultimately assist in achieving various benefits in the transportation industry.

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Section: Systematic Analysis Of the Application Of 4ir Technologies I...mentioning

confidence: 99%

4IR Applications in the Transport Industry: Systematic Review of the State of the Art with Respect to Data Collection and Processing Mechanisms

Ajayi,

Kurien,

Djouani

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The data is generated at an unprecedented velocity, requiring rapid processing for actionable insights. Given the velocity, volume, and variety, which are the three Vs of Big Data, it becomes evident that traditional data processing systems must be equipped to handle the complexities of data flow and analytics in an IoT-enabled ITS [7]. This enormous scale and complexity of data not only necessitate more advanced Big Data analytics but also makes it imperative to address challenges related to data storage, privacy, integration, and real-time processing [8].…”

Section: Introductionmentioning

confidence: 99%

An Improved Big Data Analytics Architecture Using Federated Learning for IoT-Enabled Urban Intelligent Transportation Systems

Kaleem,

Sohail,

Tariq

et al. 2023

Sustainability

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The exponential growth of the Internet of Things has precipitated a revolution in Intelligent Transportation Systems, notably in urban environments. An ITS leverages advancements in communication technologies and data analytics to enhance the efficiency and intelligence of transport networks. At the same time, these IoT-enabled ITSs generate a vast array of complex data classified as Big Data. Traditional data analytics frameworks need help to efficiently process these Big Data due to its sheer volume, velocity, variety, and significant data privacy concerns. Federated Learning, known for its privacy-preserving attributes, is a promising technology for implementation within ITSs for IoT-generated Big Data. Nevertheless, the system faces challenges due to the variable nature of devices, the heterogeneity of data, and the dynamic conditions in which ITS operates. Recent efforts to mitigate these challenges focus on the practical selection of an averaging mechanism during the server’s aggregation phase and practical dynamic client training. Despite these efforts, existing research still relies on personalized FL with personalized averaging and client training. This paper presents a personalized architecture, including an optimized Federated Averaging strategy that leverages FL for efficient and real-time Big Data analytics in IoT-enabled ITSs. Various personalization methods are applied to enhance the traditional averaging algorithm. Local fine-tuning and weighted averaging tailor the global model to individual client data. Custom learning rates are utilized to boost the performance further. Regular evaluations are advised to maintain model efficacy. The proposed architecture addresses critical challenges like real-life federated environment settings, data integration, and significant data privacy, offering a comprehensive solution for modern urban transportation systems using Big Data. Using the Udacity Self-Driving Car Dataset foe vehicle detection, we apply the proposed approaches to demonstrate the efficacy of our model. Our empirical findings validate the superiority of our architecture in terms of scalability, real-time decision-making capabilities, and data privacy preservation. We attained accuracy levels of 93.27%, 92.89%, and 92.96% for our proposed model in a Federated Learning architecture with 10 nodes, 20 nodes, and 30 nodes, respectively.

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“…Inadequate infrastructure and logistical facilities are one of the main problems [9]. Pakistan's transportation system, which includes its roads, trains, and ports, frequently experiences congestion, delays, and a lack of capacity, which raises transportation costs and extends lead times [10]. Food waste is also a result of inadequate cold storage facilities and ineffective handling of perishable foods after harvest.…”

Section: Introductionmentioning

confidence: 99%

The Digitalization Paradigm: Impacts on Agri-Food Supply Chain Profitability and Sustainability

Dong,

Ahmad,

Irshad

et al. 2023

Sustainability

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Digitization has completely changed the landscape of supply chain management, which enables businesses to streamline their processes and attain higher levels of profitability and sustainability. This study investigates the relationships between digitalization and supply chain elements, particularly integration, communication, operation, and distribution, and their effects on corporate profitability and sustainability. The research is based on an empirical investigation conducted through a questionnaire survey of agri-food industries in Pakistan. PLS-SEM was used for the analysis of data. The results show a positive relationship between digitalization and supply chain integration, processes, operation, and distribution. Moreover, a positive and significant relationship exists between digitalized supply chain integration, processes, operation, and distribution with business profitability and sustainability. The research concludes that the synergistic effect of digital advancements leads to increased business profitability and sustainability. Business organizations may put themselves at the forefront of supply chain excellence by adopting digitalization, benefiting from effective integration, communication, operations, and distribution with increased profitability and sustainability. The findings have a lot of practical and theoretical implications for the excellence of supply chain management and help attain several sustainable development goals, e.g., SDG-8, SDG-9, SDG-11, and SDG-12.

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Smart Transportation in Developing Countries: An Internet-of-Things-Based Conceptual Framework for Traffic Control

Cited by 10 publications

References 28 publications

4IR Applications in the Transport Industry: Systematic Review of the State of the Art with Respect to Data Collection and Processing Mechanisms

4IR Applications in the Transport Industry: Systematic Review of the State of the Art with Respect to Data Collection and Processing Mechanisms

An Improved Big Data Analytics Architecture Using Federated Learning for IoT-Enabled Urban Intelligent Transportation Systems

The Digitalization Paradigm: Impacts on Agri-Food Supply Chain Profitability and Sustainability

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