Big data for transport and logistics: A review

Borgi, Tawfik; Zoghlami, Nesrine; Abed, Mourad

doi:10.1109/aset.2017.7983742

Cited by 29 publications

(11 citation statements)

References 9 publications

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“…This industry captures a tremendous amount of data which are able to help companies to improve the model capacity, demand, revenue, pricing, customer service, and cost through the use of predictive analytics. In particular, we can optimize freight movements and routing, determine the inventory on hand, improve the end-to-end customer experience, or reduce the environmental impact optimizing the fuel input [95]. Today, autonomous driving cars represent a major innovation for the automotive industry, and autonomous vehicles (AVs) are expected to be a need for all the world.…”

Section: Applications In Technological Financial and Other Successfmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

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Section: Applications In Technological Financial and Other Successfmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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“…An increase of up to 530% of globally generated data is forecast, from 33 ZB (zettabytes) in 2018 to 175 ZB in 2025 [21]. One reason for this increase can be the use of new technologies and concepts, such as sensors, Machine to Machine Communication, IoT, RFID, and so forth, [22]. Additionally, methods for using the data have also improved, for example, by using AI methods for the analysis of the data or cloud computing for the technical infrastructure.…”

Section: Data Availabilitymentioning

confidence: 99%

Towards Digital Twins of Multimodal Supply Chains

et al. 2021

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Both modern multi- and intermodal supply chains pose a significant challenge to control and maintain while offering numerous optimization potential. Digital Twins have been proposed to improve supply chains. However, as of today, they are only used for certain parts of the entire supply chain. This paper presents an initial framework for a holistic Digital Supply Chain Twin (DSCT) capable of including an entire multimodal supply chain. Such a DSCT promises to enable several improvements all across the supply chain while also be capable of simulating and evaluate several different scenarios for the supply chain. Therefore, the DSCT will not only be able to optimize multi- and intermodal supply chains but also makes them potentially more robust by identifying possible issues early on. This paper discusses the major requirements that such a DSCT must fulfil to be useful and how several information technologies that matured in recent years or are about the mature are the key enablers to fulfil these requirements. Finally, a suggested high-level architecture for such a DSCT is presented as a first step towards the realization of a DSCT, as presented in this work

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“…This massive generation of data provides new opportunities for discovering new values. It raises the challenges in terms of management and analysis and has given rise to a new concept, referred to as Big Data [7].…”

Section: Overview Of Previous Researchmentioning

confidence: 99%

Data-Driven Methodology for Sustainable Urban Mobility Assessment and Improvement

et al. 2021

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The transport system is sensitive to external influences generated by various economic, social and environmental changes. The society and the environment are changing extremely fast, resulting in the need for rapid adjustment of the transport system. Traffic system management, especially in urban areas, is a dynamic process, which is why transport planners are in need of a proven and validated methodology for fast and efficient transport data collection, fusion and analytics that will be used in sustainable urban mobility policy creation. The paper presents a development of a methodology in data rich reality that combines traditional and novel data science approach for transport system analysis and planning. The result is overall process consisting of 150 steps from first desktop research to final solution development. It enables urban mobility stakeholders to identify transport problems, analyze the urban mobility situation and to propose dedicated measures for sustainable urban mobility strengthening. The methodology is based on a big data research and analysis on anonymized big data sets originating from mobile telecommunication network, where the extraction of mobility data from the big dataset is the most innovative part of the proposed process. The extracted mobility data were validated through a “conventional” field research. The methodology was, for additional testing, applied in a pilot study, performed in the City of Rijeka in Croatia. It resulted in a set of alternative measures for modal shift from passenger cars to sustainable mobility modes, that were validated by the local public and urban mobility stakeholders.

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Big data for transport and logistics: A review

Cited by 29 publications

References 9 publications

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Towards Digital Twins of Multimodal Supply Chains

Data-Driven Methodology for Sustainable Urban Mobility Assessment and Improvement

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