Big Data for Supporting Low-Carbon Road Transport Policies in Europe: Applications, Challenges and Opportunities

Gennaro, Michele De; Paffumi, Elena; Martini, Giorgio

doi:10.1016/j.bdr.2016.04.003

Cited by 71 publications

(44 citation statements)

References 23 publications

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“…environment, social and economic) is scant. There are some studies which have attempted to study the impact of big data and predictive analytics on environmental sustainability (Keeso, 2014;Bin et al, 2015;Koo et al, 2015;Braganza et al, 2016;De Gennaro et al, 2016;Lokers et al, 2016;Pan et al, 2016;Wolfert et al, 2017;Koseleva and Ropaite, 2017).…”

Section: Introductionmentioning

confidence: 99%

Impact of big data and predictive analytics capability on supply chain sustainability

Jeble

Dubey

Childe

et al. 2018

IJLM

194

179

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Section: Introductionmentioning

confidence: 99%

Impact of big data and predictive analytics capability on supply chain sustainability

Jeble

Dubey

Childe

et al. 2018

IJLM

194

179

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show abstract

“…A couple of studies have explicitly focused on estimating the energy demand of road vehicles using large real-world datasets. For instance, Gennaro et al (2016) [29] monitored two conventional car fleets in the Italian provinces of Modena (52,834 cars) and Firenze (40,459 cars) during May 2011. The data was obtained from on-board logging devices that recorded GPS (Global Positioning System) coordinates, engine status, instantaneous speed, and driven distance.…”

Section: Utilization Of Large Datasets In Energy Researchmentioning

confidence: 99%

OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

Dreier¹,

Howells²

2019

Energies

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Recent open-data movements give access to large datasets derived from real-world observations. This data can be utilized to enhance energy systems modeling in terms of heterogeneity, confidence, and transparency. Furthermore, it allows to shift away from the common practice of considering average values towards probability distributions. In turn, heterogeneity and randomness of the real-world can be captured that are usually found in large samples of real-world data. This paper presents a methodological framework for an empirical deterministic–stochastic modeling approach to utilize large real-world datasets in long-term energy systems modeling. A new software system—OSeMOSYS-PuLP—was developed and is available now.It adds the feature of Monte Carlo simulations to the existing open-source energy modeling system (the OSeMOSYS modeling framework). An application example is given, in which the initial application example of OSeMOSYS is used and modified to include real-world operation data from a public bus transport system.

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“…Estimates of how many charging stations are needed at a national or local level to fulfill future demand are described [2][3][4][5]. Geographically-oriented models calculate the distribution of charging stations based on GPS driving patterns of non-EV-vehicles or mathematical models [3,[6][7][8][9]. Liu [9] conducts an "initial analysis of the charging infrastructure assignment for the early EV market in Beijing" based on the geographical distribution of petrol refueling stations, parking lots and transmission stations, knowledge about petrol refueling behavior, and residential data.…”

Section: Charging Infrastructure Planning Design and Locationmentioning

confidence: 99%

Performance of Electric Vehicle Charging Infrastructure: Development of an Assessment Platform Based on Charging Data

Maase¹,

Dilrosun²,

Kooi³

et al. 2018

WEVJ

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Developers of charging infrastructure, be it public or private parties, are highly dependent on accurate utilization data in order to make informed decisions where and when to expand charging points. The Amsterdam University of Applied Sciences, in close cooperation with the municipalities of Amsterdam, Rotterdam, The Hague, Utrecht, and the Metropolitan Region of Amsterdam Electric, developed both the back- and front-end of a charging infrastructure assessment platform that processes and represents real-life charging data. Charging infrastructure planning and design methods described in the literature use geographic information system data, traffic flow data of non-EV vehicles, or geographical distributions of, for example, refueling stations for combustion engine vehicles. Only limited methods apply real-life charging data. Rolling out public charging infrastructure is a balancing act between stimulating the transition to zero-emission transport by enabling (candidate) EV drivers to charge, and limiting costly investments in public charging infrastructure. Five key performance indicators for charging infrastructure utilization are derived from literature, workshops, and discussions with practitioners. The paper describes the Data Warehouse architecture designed for processing large amounts of charging data, and the web-based assessment platform by which practitioners get access to relevant knowledge and information about the current performance of existing charging infrastructure represented by the key performance indicators developed. The platform allows stakeholders in the decision-making process of charging point installation to make informed decisions on where and how to expand the already existing charging infrastructure. The results are generalizable beyond the case study regions in the Netherlands and can serve the roll-out of charging infrastructure, both public and semi-public, all over the world.

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Big Data for Supporting Low-Carbon Road Transport Policies in Europe: Applications, Challenges and Opportunities

Cited by 71 publications

References 23 publications

Impact of big data and predictive analytics capability on supply chain sustainability

Impact of big data and predictive analytics capability on supply chain sustainability

OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

Performance of Electric Vehicle Charging Infrastructure: Development of an Assessment Platform Based on Charging Data

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