A Big Data Application for Low Emission Heavy Duty Vehicles

Dimokas, Nikos; Margaritis, Dimitris; Gaetani, Manuel; Köprübasi, Kerem; Bekiaris, Evangelos

doi:10.2478/ttj-2020-0021

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…Although in the previous study the dominant topic in the field of logistics and transportation were intelligent transportation systems (ITS), in recent years, logistics planning has gained slightly more prominence. Routing optimization (Dimokas et al, 2020;Wiegmans et al, 2020) and traffic monitoring (Ibrahim et al, 2020;Hwang, 2021;George & Santra, 2020; prevail in the ITS domain. In addition, process traceability (Yineng Harrison et al, 2020) is a newly emerged topic in literature in this context.…”

Section: In What Areas Of Scm Is Da Being Applied?mentioning

confidence: 99%

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Darbanian,

Brandtner,

Falatouri

et al. 2024

OSCM: An Int. Journal

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In recent years, there has been a growing surge of interest in the application of data analytics (DA) within the realm of supply chain management (SCM), attracting attention from both practitioners and researchers. This paper presents a comprehensive examination of recent implementations of DA in SCM. Employing a systematic literature review (SLR), we conducted a meticulous analysis of over 354 papers. Building upon a prior SLR conducted in 2018, we identify contemporary areas where DA has been applied across various functions within the supply chain and scrutinize the DA models and techniques that have been employed. A comparison between past findings and the current literature reveals a notable upsurge in the utilization of DA across most SCM functions, with a particular emphasis on the prevalence of predictive analytics models in contemporary SCM applications. The findings of this paper offer a detailed insight into the specific DA models and techniques currently in use across various SCM functions. Additionally, a discernible increase in the adoption of mixed or hybrid DA models is observed. However, several research gaps persist, including the need for more attention to real-time DA in SCM, the integration of publicly available data, and the application of DA to mitigate uncertainty in SCM. To address these areas and guide future research endeavors, the paper concludes by delineating six concrete research directions. These directions offer valuable avenues for further exploration in the field.

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Section: In What Areas Of Scm Is Da Being Applied?mentioning

confidence: 99%

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Darbanian,

Brandtner,

Falatouri

et al. 2024

OSCM: An Int. Journal

View full text Add to dashboard Cite

show abstract

“…An analysis of these collected data revealed that the map of annual weekly NO, NO 2 , and black carbon results had a steeply persistent pattern of contamination of up to 5 to 8 times. The authors of [37] proposed a system that recommends routes and vehicle speeds that minimize NOx emissions and use on-demand route optimization. Finally, In [38], a spatial econometric model was used to study the impact of smart transportation on CO 2 emissions in China.…”

Section: Predict and Minimize Transportation Emissionsmentioning

confidence: 99%

Smart Mobility with Big Data: Approaches, Applications, and Challenges

2023

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Many vehicles are connected to the Internet, and big data are continually created. Various studies have been conducted involving the development of artificial intelligence, machine learning technology, and big data frameworks. The analysis of smart mobility big data is essential and helps to address problems that arise as society faces increased future mobility. In this paper, we analyze application issues such as personal information leakage and data visualization due to increased data exchange in detail, as well as approaches focusing on analyses exploiting machine learning and architecture research exploiting big data frameworks, such as Apache Hadoop, Apache Spark, and Apache Kafka. Finally, future research directions and open challenges are discussed.

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“…Data-driven solutions in the form of data collection and analysis and also in the form of predictive analytics to anticipate traffic congestion, optimize public transportation schedules, and improve the efficiency of transportation networks [77,78].…”

Section: P17mentioning

confidence: 99%

A Fuzzy Cognitive Map and PESTEL-Based Approach to Mitigate CO2 Urban Mobility: The Case of Larissa, Greece

Kokkinos

Nathanail

2023

Sustainability

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The CO2 reduction promise must be widely adopted if governments are to decrease future emissions and alter the trajectory of urban mobility. However, from a long-term perspective, the strategic vision of CO2 mitigation is driven by inherent uncertainty and unanticipated volatility. As these issues emerge, they have a considerable impact on the future trends produced by a number of exogenous and endogenous factors, including Political, Economic, Social, Technological, Environmental, and Legal aspects (PESTEL). This study’s goal is to identify, categorize, and analyze major PESTEL factors that have an impact on the dynamics of urban mobility in a rapidly changing environment. For the example scenario of the city of Larissa, Greece, a Fuzzy Cognitive Map (FCM) approach was employed to examine the dynamic interactions and behaviors of the connected criteria from the previous PESTEL categories. An integrative strategy that evaluates the interaction of linguistic evaluations in the FCM is used to include all stakeholders in the creation of a Decision Support System (DSS). The methodology eliminates the uncertainty brought on by a dearth of quantitative data. The scenarios in the study strands highlight how urbanization’s effects on sustainable urban transportation and the emergence of urban PESTEL actors impact on CO2 reduction decision-making. We focus on the use case of Larissa, Greece (the city of the CIVITAS program), which began putting its sustainable urban development plan into practice in 2015. The proposed decision-making tool uses analytics and optimization algorithms to point responsible authorities and decision-makers in the direction of Larissa’s sustainable urban mobility and eventually the decarbonization of the urban and suburban regions.

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A Big Data Application for Low Emission Heavy Duty Vehicles

Cited by 6 publications

References 7 publications

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Smart Mobility with Big Data: Approaches, Applications, and Challenges

A Fuzzy Cognitive Map and PESTEL-Based Approach to Mitigate CO2 Urban Mobility: The Case of Larissa, Greece

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