Demystifying Transportation Using Big Data Analytics

Trueblood, Fletcher; Rodríguez, D.; Hernandez, Jese; Salomon, Michelle; Soundarajan, Sanjay; Pirouz, Matin

doi:10.1109/csci49370.2019.00240

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…A critical look at the transportation ecosystem shows that these technologies are enhancing safety and efficiency, streamlining operations, and enabling data-driven decision-making. Several systematic literature review research works have been carried out on the general influence and impact of the 4IR technologies on the transportation sector ( ), while others discussed the different contributive areas of specific technologies, such as Internet of Things(IoT)/Sensors ( [33][34][35][36][37][38][39][40]) to achieve the interconnection of vehicles, infrastructure and users through a network of sensors and smart devices; Autonomous Systems ( [41,42]) which ensures safety, efficiency and accessibility; Big data ( [36,[43][44][45][46][47][48][49][50][51][52][53][54][55][56]) to ease the burden of large data analytics; Artificial Intelligence ( ) which enhances analytical capabilities and achieving streamlined transportation operations; Machine Learning/Deep Learning ( [39,) which are deployed to achieve predictive data analysis, optimization and decision support; Computing Paradigms ( [35,40,[106][107][108][109][110][111][112]) for storage and processing of vast amount of generated and real-time data; GIS ( [46,[113][114]…”

Section: Introductionmentioning

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: Introductionmentioning

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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Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches

Ang

Seng

Ngharamike

et al. 2022

IJGI

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With the recent increase in urban drift, which has led to an unprecedented surge in urban population, the smart city (SC) transportation industry faces a myriad of challenges, including the development of efficient strategies to utilize available infrastructures and minimize traffic. There is, therefore, the need to devise efficient transportation strategies to tackle the issues affecting the SC transportation industry. This paper reviews the state-of-the-art for SC transportation techniques and approaches. The paper gives a comprehensive review and discussion with a focus on emerging technologies from several information and data-driven perspectives including (1) geoinformation approaches; (2) data analytics approaches; (3) machine learning approaches; (4) integrated deep learning approaches; (5) artificial intelligence (AI) approaches. The paper contains core discussions on the impacts of geo-information on SC transportation, data-driven transportation and big data technology, machine learning approaches for SC transportation, innovative artificial intelligence (AI) approaches for SC transportation, and recent trends revealed by using integrated deep learning towards SC transportation. This survey paper aimed to give useful insights to researchers regarding the roles that data-driven approaches can be utilized for in smart cities (SCs) and transportation. An objective of this paper was to acquaint researchers with the recent trends and emerging technologies for SC transportation applications, and to give useful insights to researchers on how these technologies can be exploited for SC transportation strategies. To the best of our knowledge, this is the first comprehensive review that examines the impacts of the various five driving technological forces—geoinformation, data-driven and big data technology, machine learning, integrated deep learning, and AI—in the context of SC transportation applications.

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Demystifying Transportation Using Big Data Analytics

Cited by 2 publications

References 12 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

Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches

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