Digital Services Based on Vehicle Usage Data: The Underlying Vehicle Data Value Chain

2020 ◽  
pp. 22-43
Author(s):  
Christian Kaiser ◽  
Andreas Festl ◽  
Gernot Pucher ◽  
Michael Fellmann ◽  
Alexander Stocker
Keyword(s):  
Value Chain ◽  
Digital Services ◽  
Vehicle Data ◽  
Data Value ◽  
Vehicle Usage ◽  
Usage Data

scholarly journals Multimodal Data Value Chain (M-DVC): A Conceptual Tool to Support the Development of Multimodal Learning Analytics Solutions

2020 ◽  
Vol 15 (2) ◽  
pp. 113-122 ◽  
Author(s):  
Shashi Kant Shankar ◽  
Maria Jesus Rodriguez-Triana ◽  
Adolfo Ruiz-Calleja ◽  
Luis P. Prieto ◽  
Pankaj Chejara ◽  
...  
Keyword(s):  
Value Chain ◽  
Learning Analytics ◽  
Multimodal Learning ◽  
Conceptual Tool ◽  
Multimodal Data ◽  
Data Value ◽  
Multimodal Learning Analytics

scholarly journals Energy economy analysis of the G-Wiz: a two-year case study based on two vehicles

2011 ◽  
Vol 225 (11) ◽  
pp. 1505-1517 ◽  
Author(s):  
A R Chaudhari ◽  
R H Thring
Keyword(s):  
Energy Consumption ◽  
Electric Vehicles ◽  
Discharge Rate ◽  
Vehicle Data ◽  
Maximum Range ◽  
Energy Economy ◽  
Energy Consumption Patterns ◽  
Vehicle Usage ◽  
Main Factor

This paper presents the data recorded from two G-Wiz Reva electric vehicles (EVs) over a period of two years and approximately 8000 km on each vehicle. The analysis of the vehicle data demonstrates that the range of the vehicle obtained for a certain state-of-charge (SOC) drop was not consistent. The results show that the main factor affecting the available range was irregular vehicle usage. The recharge energy consumption patterns of the vehicle were identified and it was demonstrated that infrequent vehicle usage increased energy consumed by the vehicle. A maximum range of 66.8 km was achieved when the vehicle was regularly used, but this fell to 42.8 km when it was infrequently used. The energy economy when the vehicle was regularly used was 8.3 km/kWh. Additionally, the analysis results identify the need to determine discharge rate of the vehicle batteries to determine the precise effects on the available range and energy consumption of the vehicle.

scholarly journals A Multi-Layer Big Data Value Chain Approach for Security Issues

2020 ◽  
Vol 175 ◽  
pp. 737-744
Author(s):  
Abou Zakaria Faroukhi ◽  
Imane El Alaoui ◽  
Youssef Gahi ◽  
Aouatif Amine
Keyword(s):  
Big Data ◽  
Value Chain ◽  
Security Issues ◽  
Data Value

scholarly journals Digital Transformation in Aeronautics through the ICARUS Aviation Data and Intelligence Marketplace

2019 ◽  
Vol 304 ◽  
pp. 04002
Author(s):  
Fenareti Lampathaki’ ◽  
Michele Sesana ◽  
Dimitrios Alexandrou
Keyword(s):  
Value Chain ◽  
Data Analytics ◽  
Data Gathering ◽  
Digital Transformation ◽  
A Value ◽  
Relative Disadvantage ◽  
Data Value ◽  
Theoretical Foundations ◽  
Enrichment Methods ◽  
Instrumental Role

Today, digital transformation has drifted all industries with its proven capacity to improve operations and boost revenues while building a value chain ecosystem. The aeronautics ecosystem is almost unanimously invested in some way into a digital transformation strategy in which data typically plays an instrumental role. However, despite the vast quantity of data across myriad parameters that never stop flowing across the aircraft-passengers-luggage-cargo journeys, the aviation-related stakeholders are still at a relative disadvantage in terms of data gathering and sharing, especially since the eternal questions of “who owns the aircraft” and “who owns the passenger” remain open. In this contact, the present paper focuses on the design and delivery of the ICARUS data and intelligence platform that aims to enable trusted and fair data sharing and insightful data analytics in an end-to-end secure manner. The methodology followed during the implementation of the ICARUS platform is defined, the aviation data value chain is elaborated, the ICARUS Minimum Viable Product is outlined and the theoretical foundations of the ICARUS data management and value enrichment methods are introduced, giving way to a brief reference to the ICARUS unique selling points and platform implementation.

Toward a Conceptualization of Big Data Value Chain

2019 ◽  
pp. 76-92 ◽  
Author(s):  
Rim Louati ◽  
Sonia Mekadmi
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Value Chain ◽  
Data Capture ◽  
Digital Devices ◽  
Business Executives ◽  
Data Value ◽  
Near Future ◽  
Different Sources

The generation of digital devices such as web 2.0, smartphones, social media and sensors has led to a growing rate of data creation. The volume of data available today for organizations is big. Data are produced extensively every day in many forms and from many different sources. Accordingly, firms in several industries are increasingly interested in how to leverage on these “big data” to draw valuable insights from the various kinds of data and to create business value. The aim of this chapter is to provide an integrated view of big data management. A conceptualization of big data value chain is proposed as a research model to help firms understand how to cope with challenges, risks and benefits of big data. The suggested big data value chain recognizes the interdependence between processes, from business problem identification and data capture to generation of valuable insights and decision making. This framework could provide some guidance to business executives and IT practitioners who are going to conduct big data projects in the near future.

Travel Characteristics of Urban Freight Vehicles and Their Effects on Emission Factors

2005 ◽  
Vol 1941 (1) ◽  
pp. 89-98
Author(s):  
Annie Protopapas ◽  
Arun Chatterjee ◽  
Terry Miller ◽  
Jerry Everett
Keyword(s):  
Commercial Vehicle ◽  
Emission Factors ◽  
Data Sets ◽  
Data Set ◽  
Average Speed ◽  
Driving Pattern ◽  
Vehicle Data ◽  
Input Parameters ◽  
Vehicle Usage ◽  
The U.S

This study collected local commercial vehicle data in Knox County, Tennessee, from the U.S. Postal Service (USPS) and two companies engaged in package pickup and delivery (PUD). Another urban commercial vehicle data set with a wider spectrum of freight companies was obtained from North Carolina for comparative analysis. The two data sets were analyzed to develop two sets of values for input parameters for the U.S. Environmental Protection Agency's MOBILE6 model. Statistical tests permitted four aggregated vehicle usage classes to be formed. Two runs of MOBILE6 modeled the two commercial vehicle data sets in their entirety. Four additional runs modeled each vehicle usage class individually through the use of average speed and starts per day specific to the driving pattern of each class. Differences between the values of input parameters and emission factors based on data collected by this study and those based on the default values of MOBILE6 are discussed. Commercial vehicles examined by this study indicated higher annual mileage accumulation rates than the default values. Also their vehicle miles traveled and engine start distributions by hour of day varied considerably from the default values, occurring primarily between the two daily peak traffic periods (morning and evening). The study found higher volatile organic compounds (VOCs), carbon monoxide (CO), and nitrogen oxide emission factors than in default runs for USPS vehicles, whose driving pattern resulted in lower-than-default average speed. Higher VOC and CO emission factors were found for gasoline and diesel package PUD vehicles due to lower-than-default average speeds and higher-than-default starts per day.

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