1,720,997 research outputs found
D9.1 Report on vehicle survey operator needs: Safe tolerance zone calculation and interventions for driver-vehicle-environment interactions under challenging conditions
No full textnot availabl
D7.1 Methodology for the evaluation of interventions Safe tolerance zone calculation and interventions for driver-vehicle-environment interactions under challenging conditions
No full textThis deliverable aims to provide the methodology for the evaluation of both real-time and posttrip safety interventions, which will be developed to improve driver safety through keeping the
driver within the boundaries of the ‘Safety Tolerance Zone’. In particular, the methodology will
cover all the features and particularities of each one of the interventions examined, as well as
the statistical issues involved in effectiveness assessment of interventions.
In order to evaluate the effectiveness of the safety interventions, the logic model of change
behind the i-DREAMS interventions (comprising of Safety Outcomes (SO), Safety Promoting
Goals (SPG), Performance Objectives (PO) and Change Objectives (CO) is presented and the
dependency among the different levels was highlighted. Moreover, it was sought to link the
SO, SPG, PO, and CO with driving behavior and safety critical indicators, in order to identify
the potential measurements to be provided from the i-DREAMS platform and will be utilized for
intervention assessment. For the interventions taking place in a professional work setting, data
analysis and interpretation of results will have to take companies’ safety climate into account,
as this can be expected to be a crucial environmental factor influencing intervention
effectiveness. In line with corporate safety climate, individual user acceptance is also to be
included in the analysis and interpretation of intervention effectiveness.
In addition, an overview of past methodologies and frameworks from literature that have been
used to assess interventions was described. It was revealed that safety promoting goals and
performance objectives had the greatest effect on the assessment of interventions. Although
safety constitutes the cornerstone of the i-DREAMS project, little evidence for safety outcomes
was identified, due to the limited time framework of interventions and the fact that the crashes
were rare events. Safety promoting goals (i.e. driver fitness, vehicle control, speed
management) appeared to have an influence in a great extend for the assessment of
interventions. Moreover, performance objectives, and especially, speeding, harsh
acceleration, harsh braking, lane deviation and left turns had the strongest impact on the
evaluation of interventions, while driver related characteristics such as distraction, stress,
fatigue, drowsiness, attentions, concentration and blind spot appeared to have lower impact.
The final section of the deliverable deals with the evaluation methodology, based on the
aforementioned different safety levels of the logic model of change. The ultimate purpose of
the methodology is a summative assessment focusing on outcome and process evaluation. At
the beginning of the evaluation, appropriate research questions need to be defined and
indicators, measures and determinants need to be outlined. The criteria, KPIs and user
acceptance, acceptability and reliability factors, which will support the assessment are also
thoroughly described in this document.
Methodologically, three different methods are proposed: before-after analysis, case-control
trials and questionnaires. With regards to before-after analysis, both quantitative (i.e. safety
outcomes, safety promoting goals, performance objectives) and observed qualitative (i.e.
change objectives) indicators can be utilized, and comparisons can be drawn using beforeafter or case-control study designs. Questionnaires, will be exploited mostly for the evaluation
of qualitative indicators (i.e. change objectives)
Finally, following the design of the assessment methodology, the crucial next step within the iDREAMS project is connected with the organization of the back-office database, which will
provide all necessary data for the realization of the individual evaluations as well as the
comparisons between different countries and transportation modes
D7.1 Methodology for the evaluation of interventions Safe tolerance zone calculation and interventions for driver-vehicle-environment interactions under challenging conditions
No full textThis deliverable aims to provide the methodology for the evaluation of both real-time and posttrip safety interventions, which will be developed to improve driver safety through keeping the
driver within the boundaries of the ‘Safety Tolerance Zone’. In particular, the methodology will
cover all the features and particularities of each one of the interventions examined, as well as
the statistical issues involved in effectiveness assessment of interventions.
In order to evaluate the effectiveness of the safety interventions, the logic model of change
behind the i-DREAMS interventions (comprising of Safety Outcomes (SO), Safety Promoting
Goals (SPG), Performance Objectives (PO) and Change Objectives (CO) is presented and the
dependency among the different levels was highlighted. Moreover, it was sought to link the
SO, SPG, PO, and CO with driving behavior and safety critical indicators, in order to identify
the potential measurements to be provided from the i-DREAMS platform and will be utilized for
intervention assessment. For the interventions taking place in a professional work setting, data
analysis and interpretation of results will have to take companies’ safety climate into account,
as this can be expected to be a crucial environmental factor influencing intervention
effectiveness. In line with corporate safety climate, individual user acceptance is also to be
included in the analysis and interpretation of intervention effectiveness.
In addition, an overview of past methodologies and frameworks from literature that have been
used to assess interventions was described. It was revealed that safety promoting goals and
performance objectives had the greatest effect on the assessment of interventions. Although
safety constitutes the cornerstone of the i-DREAMS project, little evidence for safety outcomes
was identified, due to the limited time framework of interventions and the fact that the crashes
were rare events. Safety promoting goals (i.e. driver fitness, vehicle control, speed
management) appeared to have an influence in a great extend for the assessment of
interventions. Moreover, performance objectives, and especially, speeding, harsh
acceleration, harsh braking, lane deviation and left turns had the strongest impact on the
evaluation of interventions, while driver related characteristics such as distraction, stress,
fatigue, drowsiness, attentions, concentration and blind spot appeared to have lower impact.
The final section of the deliverable deals with the evaluation methodology, based on the
aforementioned different safety levels of the logic model of change. The ultimate purpose of
the methodology is a summative assessment focusing on outcome and process evaluation. At
the beginning of the evaluation, appropriate research questions need to be defined and
indicators, measures and determinants need to be outlined. The criteria, KPIs and user
acceptance, acceptability and reliability factors, which will support the assessment are also
thoroughly described in this document.
Methodologically, three different methods are proposed: before-after analysis, case-control
trials and questionnaires. With regards to before-after analysis, both quantitative (i.e. safety
outcomes, safety promoting goals, performance objectives) and observed qualitative (i.e.
change objectives) indicators can be utilized, and comparisons can be drawn using beforeafter or case-control study designs. Questionnaires, will be exploited mostly for the evaluation
of qualitative indicators (i.e. change objectives)
Finally, following the design of the assessment methodology, the crucial next step within the iDREAMS project is connected with the organization of the back-office database, which will
provide all necessary data for the realization of the individual evaluations as well as the
comparisons between different countries and transportation modes
Methodology for the Evaluation of Safety Interventions
No full textIn recent decades, automotive telematics and driver monitoring systems have been introduced in the industry in
order to provide real-time and post-trip interventions and feedback to the driver. A few driver monitoring
technologies and platforms have been used to record driving performance, focus on key risk indicators and provide
safety interventions. Within that group of tools, interventions have been indicated to significantly enhance driving
behavior and road safety. The purpose of the current study is to provide a methodology for safety intervention
evaluation in order to keep driver behavior within acceptable boundaries of safe operation (i.e. Safety Tolerance
Zone). To that aim, the most appropriate assessment variables from the i-DREAMS platform, related to the logic
model of change were identified and some recommendations for the i-DREAMS project were provided. In order
for the methodology to be designed, past experience on similar projects was exploited in order to derive a list of
methods, indicators, utilized Key Performance Indicators (KPIs) and evaluation criteria mostly suitable for
evaluating the project’s safety interventions. Three different methods (i.e. before-after analysis, case-control trials
and questionnaires) were identified and therefore, the evaluation was conducted in terms of the outcomes proposed
in the logic model of change. Results from literature review indicated that safety promoting goals and performance
objectives had the greatest effect on the assessment of interventions. Driver behavior indicators, such as speeding,
harsh acceleration or braking had the strongest impact on the interventions evaluation, while driver related
characteristics, such as distraction, stress, fatigue, drowsiness and attention appeared to have lower impact. Taking
into account the experimental studies, the design of a customized feedback strategy will assist in performing the
appropriate evaluation of interventions needed for the improvement of driver behavior.The research was funded by the European Union's Horizon 2020 i-DREAMS project (Project Number: 814761) funded by European Commission under the MG-2-1-2018 Research and Innovation Action (RIA)
Methodology for the Evaluation of Safety Interventions
No full textIn recent decades, automotive telematics and driver monitoring systems have been introduced in the industry in
order to provide real-time and post-trip interventions and feedback to the driver. A few driver monitoring
technologies and platforms have been used to record driving performance, focus on key risk indicators and provide
safety interventions. Within that group of tools, interventions have been indicated to significantly enhance driving
behavior and road safety. The purpose of the current study is to provide a methodology for safety intervention
evaluation in order to keep driver behavior within acceptable boundaries of safe operation (i.e. Safety Tolerance
Zone). To that aim, the most appropriate assessment variables from the i-DREAMS platform, related to the logic
model of change were identified and some recommendations for the i-DREAMS project were provided. In order
for the methodology to be designed, past experience on similar projects was exploited in order to derive a list of
methods, indicators, utilized Key Performance Indicators (KPIs) and evaluation criteria mostly suitable for
evaluating the project’s safety interventions. Three different methods (i.e. before-after analysis, case-control trials
and questionnaires) were identified and therefore, the evaluation was conducted in terms of the outcomes proposed
in the logic model of change. Results from literature review indicated that safety promoting goals and performance
objectives had the greatest effect on the assessment of interventions. Driver behavior indicators, such as speeding,
harsh acceleration or braking had the strongest impact on the interventions evaluation, while driver related
characteristics, such as distraction, stress, fatigue, drowsiness and attention appeared to have lower impact. Taking
into account the experimental studies, the design of a customized feedback strategy will assist in performing the
appropriate evaluation of interventions needed for the improvement of driver behavior.The research was funded by the European Union's Horizon 2020 i-DREAMS project (Project Number: 814761) funded by European Commission under the MG-2-1-2018 Research and Innovation Action (RIA)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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