PASSENGER RESPONSE TIME DATA-SETS FOR LARGE PASSENGER FERRIES AND CRUISE SHIPS DERIVED FROM SEA TRIALS

2021 ◽  
Vol 155 (A1) ◽  
Author(s):  
R Brown ◽  
E R Galea ◽  
S Deere ◽  
L Filippidis
Keyword(s):  
Response Time ◽  
Response Times ◽  
Response Time Data ◽  
Data Sets ◽  
Time Data ◽  
Data Set ◽  
Analysis Methodology ◽  
Cruise Ships ◽  
Passenger Ships ◽  
Response Time Distributions

This paper describes research that was carried-out under the EU FP7 research project SAFEGUARD and presents passenger response time data generated from five full-scale semi-unannounced assembly trials at sea. The data-sets were generated from three different types of passenger ships, a RO-PAX ferry without cabins (RP1), a cruise ship (CS) and a RO-PAX ferry with cabins (RP2). In total, response times from 2366 people were collected making it the largest response time data-set ever collected – on land or sea. The analysis methodology used to extract the response time data and the resultant response time distributions (RTD) is presented. A number of key findings from the data analysis are presented along with three recommendations to modify the IMO guidelines governing ship evacuation analysis, namely; (a) it is inappropriate to use the same RTD for cruise ships and RO-PAX vessels; (b) a new Day Case RTD is suggested for RO-PAX vessels and (c) new Day and Night RTDs are suggested for cruise ships.

Passenger Response Time Data-Sets for Large Passenger Ferries and Cruise Ships Derived from Sea Trials

2013 ◽  
Vol 155 (A1) ◽  
Keyword(s):  
Response Time ◽  
Response Times ◽  
Response Time Data ◽  
Data Sets ◽  
Time Data ◽  
Data Set ◽  
Analysis Methodology ◽  
Cruise Ships ◽  
Passenger Ships ◽  
Response Time Distributions

This paper describes research that was carried-out under the EU FP7 research project SAFEGUARD and presents passenger response time data generated from five full-scale semi-unannounced assembly trials at sea. The data-sets were generated from three different types of passenger ships, a RO-PAX ferry without cabins (RP1), a cruise ship (CS) and a RO-PAX ferry with cabins (RP2). In total, response times from 2366 people were collected making it the largest response time data-set ever collected – on land or sea. The analysis methodology used to extract the response time data and the resultant response time distributions (RTD) is presented. A number of key findings from the data analysis are presented along with three recommendations to modify the IMO guidelines governing ship evacuation analysis, namely; (a) it is inappropriate to use the same RTD for cruise ships and RO-PAX vessels; (b) a new Day Case RTD is suggested for RO-PAX vessels and (c) new Day and Night RTDs are suggested for cruise ships.

PASSENGER RESPONSE TIME DATA- SETS FOR LARGE PASSENGER FERRIES AND CRUISE SHIPS DERIVED FROM SEA TRIALS

2021 ◽  
Vol 155 (A2) ◽  
Author(s):  
R Brown ◽  
E R Galea ◽  
S Deere ◽  
L Filippidis
Keyword(s):  
Response Time ◽  
Research Group ◽  
Data Sets ◽  
Time Data ◽  
Evacuation Time ◽  
The Public ◽  
Cruise Ships ◽  
Passenger Ships ◽  
Unique Nature ◽  
The Subject

The paper consists of 27 figures; numerous equations and 12 notes/ references, many of which are written by the authors of this paper. Whilst this may indicate a lack of “reading around the subject” it also indicates the unique nature of the topic and that little exists at present in the public domain about this topic. Indeed the authors and the research group they represent are the main contributors to the IMOs discussions and circulars on this subject. Given that background the paper is very detailed and consists of comparisons between the evacuation times of 3 passenger ships, 2 being Ro-Pax vessels and 1 a cruise liner. On board evacuation time statistics have been gathered from significant populations enabling the authors to draw significant conclusions relating to evacuation times in the presented scenarios. The paper is therefore a useful addition to the debates on this subject which is of major relevance to the understanding of evacuation times in passenger vessels. Data and research in this area is difficult to obtain thus the authors should be congratulated for their work.

Measures of phonological typicality

2010 ◽  
Vol 5 (3) ◽  
pp. 281-299 ◽  
Author(s):  
Padraic Monaghan ◽  
Morten H. Christiansen ◽  
Thomas A. Farmer ◽  
Stanka A. Fitneva
Keyword(s):  
Lexical Decision ◽  
Response Time ◽  
Word Processing ◽  
Response Times ◽  
Parameter Variation ◽  
Response Time Data ◽  
Speech Sounds ◽  
Time Data ◽  
Monosyllabic Word ◽  
Lexical Categories

Phonological Typicality (PT) is a measure of the extent to which a word’s phonology is typical of other words in the lexical category to which it belongs. There is a general coherence among words from the same category in terms of speech sounds, and we have found that words that are phonologically typical of their category tend to be processed more quickly and accurately than words that are less typical. In this paper we describe in greater detail the operationalisation of measures of a word’s PT, and report validations of different parameterisations of the measure. For each variant of PT, we report the extent to which it reflects the coherence of the lexical categories of words in terms of their sound, as well as the extent to which the measure predicts naming and lexical decision response times from a database of monosyllabic word processing. We show that PT is robust to parameter variation, but that measures based on PT of uninflected words (lemmas) best predict response time data for naming and lexical decision of single words.

scholarly journals IMO INF Paper Summary - Response Time Data for Large Passenger Ferries and Cruise Ships

2012 ◽  
Author(s):  
Robert Brown ◽  
◽  
Edwin Richard Galea ◽  
Steven Deere ◽  
Lazaros Filippidis ◽  
...  
Keyword(s):  
Response Time ◽  
Response Time Data ◽  
Time Data ◽  
Cruise Ships

scholarly journals Analysis of speed-accuracy trade-offs using the Wiener diffusion model for choice reaction times

2021 ◽  
Author(s):  
Annalise Aleta LaPlume
Keyword(s):  
Response Time ◽  
Diffusion Model ◽  
Response Times ◽  
Reaction Times ◽  
Response Time Data ◽  
Choice Response ◽  
Time Data ◽  
Trade Offs ◽  
R Programming ◽  
Speed Accuracy

A methodology review paper on the utility and challenges of modelling speed-accuracy trade-offs in response time data. The paper reviews the importance of accounting for speed-accuracy trade-offs when measuring response times, and provides background on diffusion models for response time data. It then describes a practical software implementation of the EZ-diffusion model to model speed-accuracy trade-offs in choice response time data using the R programming language.

scholarly journals The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models

2016 ◽  
Author(s):  
Gilles Dutilh ◽  
Chris Donkin
Keyword(s):  
Response Time ◽  
Latent Variables ◽  
Decision Time ◽  
Cognitive Models ◽  
Response Time Data ◽  
Estimation Methods ◽  
Data Sets ◽  
Analysis Tool ◽  
Response Time Models ◽  
Time Data

Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) non–decision time. Inferences about these psychological factors, hinge upon the validity of the models’ parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two–condition data sets, we manipulated properties of participants’ behavior in a two–alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these “modeler’s degrees of freedom” did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models.

Diffusion Models in Experimental Psychology

2013 ◽  
Vol 60 (6) ◽  
pp. 385-402 ◽  
Author(s):  
Andreas Voss ◽  
Markus Nagler ◽  
Veronika Lerche
Keyword(s):  
Cognitive Psychology ◽  
Response Time ◽  
Cognitive Processes ◽  
Response Times ◽  
Diffusion Models ◽  
Time Data ◽  
Binary Decision ◽  
Comprehensive Information ◽  
Decision Biases ◽  
Response Time Distributions

Stochastic diffusion models ( Ratcliff, 1978 ) can be used to analyze response time data from binary decision tasks. They provide detailed information about cognitive processes underlying the performance in such tasks. Most importantly, different parameters are estimated from the response time distributions of correct responses and errors that map (1) the speed of information uptake, (2) the amount of information used to make a decision, (3) possible decision biases, and (4) the duration of nondecisional processes. Although this kind of model can be applied to many experimental paradigms and provides much more insight than the analysis of mean response times can, it is still rarely used in cognitive psychology. In the present paper, we provide comprehensive information on the theory of the diffusion model, as well as on practical issues that have to be considered for implementing the model.

Using response time data to reduce testing time in cognitive tests.

2018 ◽  
Vol 30 (3) ◽  
pp. 328-338 ◽  
Author(s):  
Maria Bertling ◽  
Jonathan P. Weeks
Keyword(s):  
Response Time ◽  
Response Time Data ◽  
Testing Time ◽  
Cognitive Tests ◽  
Time Data
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