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Discrete Outcome Models
Published in Simon Washington, Matthew Karlaftis, Fred Mannering, Panagiotis Anastasopoulos, Statistical and Econometric Methods for Transportation Data Analysis, 2020
Simon Washington, Matthew Karlaftis, Fred Mannering, Panagiotis Anastasopoulos
A concern with all models is whether or not their estimated parameters are transferable spatially (among regions or cities) or temporally (over time). From a spatial perspective, transferability is desirable because it means that parameters of models estimated in other places are used, thus saving the cost of additional data collection and estimation. Temporal transferability ensures that forecasts made with the model have some validity in that the estimated parameters are stable over time. When testing spatial and temporal transferability, likelihood ratio tests are applied. Suppose the transferability of parameters between two regions, a and b is tested (or equivalently between two time periods). One could conduct the following likelihood ratio tests to test for this, () X2=−2[LL(βT)−LL(βa)−LL(βb)]
Mental Workload Assessment Methods
Published in Neville A. Stanton, Paul M. Salmon, Laura A. Rafferty, Guy H. Walker, Chris Baber, Daniel P. Jenkins, Human Factors Methods, 2017
Neville A. Stanton, Paul M. Salmon, Laura A. Rafferty, Guy H. Walker, Chris Baber, Daniel P. Jenkins
Typical MWL assessments use a selection of techniques from each of the three categories described above. The multi-method approach to the assessment of MWL is designed to ensure comprehensiveness. The suitability of MWL assessment techniques can be evaluated on a number of dimensions. Wierwille and Eggemeier (1993) suggest that for a MWL assessment technique to be recommended for use in a test and evaluation procedure, it should possess the following properties: Sensitivity: the degree to which the technique can discriminate between differences in the levels of MWL imposed on a participant.Limited intrusiveness: the degree to which the assessment technique intrudes upon primary task performance.Diagnosticity: the degree to which the technique can determine the type or cause of the workload imposed on a participant.Global sensitivity: the ability to discriminate between variations in the different types of resource expenditure or factors affecting workload.Transferability: the degree to which the technique can be applied in different environments other than what it was designed for.Ease of implementation: the level of resources required to use the technique, such as technology and training requirements.
Explainable AI in Machine/Deep Learning for Intrusion Detection in Intelligent Transportation Systems for Smart Cities
Published in Mohamed Lahby, Utku Kose, Akash Kumar Bhoi, Explainable Artificial Intelligence for Smart Cities, 2021
Andria Procopiou, Thomas M. Chen
Transferability denotes the model’s ability to transfer its acquired knowledge to other problems correctly and not let humans misinterpret its results and make incorrect assumptions (Caruana et al., 2015; Szegedy et al., 2013). Furthermore, transferability also deals with a model’s ability to set boundaries, allowing for a more-in depth comprehension and understanding of its implementation.
Testing and enhancing spatial transferability of artificial neural networks based travel behavior models
Published in Transportation Letters, 2022
Anil NP Koushik, M Manoj, N Nezamuddin, AP Prathosh
The transferred models’ results need to be compared with that of a standard to test transferability. The standard used in this study is a locally developed model called the base model. The base model is trained using 80% of local data and tested on the remaining 20%. The accuracy values of the base model for each of the nine municipalities are as follows: GR – 0.77, AL – 0.76, NM – 0.76, UT – 0.75, AD – 0.74, HA −0.70, SG – 0.78, RT – 0.76 and EI – 0.74. The results of all the transferred models are compared with the results of the base model (local model) from the application context.
Estimating pedestrian delay at signalized intersections using high-resolution event-based data: a finite mixture modeling method
Published in Journal of Intelligent Transportation Systems, 2022
Abolfazl Karimpour, Jason C. Anderson, Sirisha Kothuri, Yao-Jan Wu
The transferability of a model is defined as the application of a formulated and trained model from one context to another context. For effective model transferability, theoretical and practical conditions should be met. The theoretical condition describes the underlying behavioral process of the model in the application and in the context where the model was estimated. The practical condition describes the availability of similar data sources in both the application and in the context where the model was estimated.
When clothing designers become business people: a design centred training methodology for empowerment incubation
Published in International Journal of Fashion Design, Technology and Education, 2018
Transferability relates to the extent to which data can be transferred to other similar situations (Babbie & Mouton, 2001). Data are reported in the context that they were collected, with contextual details about the case. Furthermore, transcriptions and field notes were considered to contextualise data.