Model Development. The contingencies shown in Table 5-3 were simulated for the cases without and with the SPS.
Model Development. Thermal transfer limits were calculated for summer peak load conditions without and with the SPS. The cases without the SPS (Case 1) and with the SPS (Case 2) are described in Section 3.4.
Model Development. Voltage transfer limits were calculated for summer peak load conditions without and with the SPS. The cases without the Project (Case 1) and with the Project (Case 2) are described in Section 3.4.
Model Development. Different aspects of model development for SNNs were considered: 1) whether the hyperparameters were tuned and which was the performance criterion for model development. 2) how the prognostic variables were scaled, 3) which programming language was used. Hyperparameters are fundamental to the architecture of an XXX. They fine-tune the performance of a prediction model, preventing overfitting and providing generalizability of the model to new ”unseen” data. Choice of hyper- parameters can be a challenge in the modern era of building SNNs with state-of-the-art software that allows for numerous choices. Commonly tuned parameters were penalty terms in the likelihood (e.g., weight decay) and the number of units (nodes) in the hidden layer(s). In the majority of studies (15, 62.5%), the approach to training hyperparameters was unclear, with 6 of these studies (25.0%) failing to report whether parameters were tuned or default values were chosen. In 4 studies (16.7%) parameters were tuned, in 3 studies (12.5%) some parameters were tuned and some were assigned default values, while in 2 studies (8.3%) default values only were chosen for the hyperparameters. The performance criterion for model development (hyperparameter tuning) was examined across the 24 studies. The training criterion was unclear for 6 studies (25.0%). For 5 studies (20.8%), neural network hyperparameters were trained based on the log-likelihood, for 3 studies based on the C-index (12.5%), and for 2 studies (8.3%) based on the Area Under the Curve (AUC). Other criteria used for model development are provided in the Supplementary Material. Better reporting of the choice of hyperparameters (which parameters were selected) and of the training procedure (how they were tuned) is needed. This will help researchers to better understand how the model was developed and will facilitate reproducibility. In ANNs, input features are typically scaled to ensure that all features have a comparable scale, which allows an update of the same rate, resulting in faster algorithm convergence. The procedure was unclear in 10 of the 24 studies (41.7%), scaling was unnecessary in 7 studies (29.2%), and normalization (minimum and maximum values of features are used for scaling) was applied in 5 studies (20.8%). Standardization (mean and standard deviation of features are used for scaling) was applied in only 2 studies (8.3%). A precise description of the scaling approach (normalization or standardization) should be provide...
Model Development. Post-TRIPOD, the number of studies that included predictors based on significance levels in univariable analysis decreased (pre-TRIPOD: 67%, post-TRIPOD: 44%, Figure 2 and Supplementary Table 8) as well as the number of studies using stepwise methods to retain pre- dictors (pre-TRIPOD: 63%, post-TRIPOD: 48%). In general, a larger number of candidate predictors was used in the post-TRIPOD period (median: 25), compared with pre-TRIPOD period (median: 20). Internal validation was more frequently performed in the post-TRIPOD period (74%) compared with the pre-TRIPOD period (62%). When internal validation was performed, bootstrapping was the most frequently used method in both time periods with an increase from 29% in the pre-TRIPOD period to 41% in the post-TRIPOD period. Chapter 7 190 Performance measures The majority of studies presented measures of calibration (pre-TRIPOD: 66%, post-TRIPOD: 87%) and discrimination (pre-TRIPOD: 91%, post-TRIPOD: 100%, Figure 3 and Supple- mentary Table 7).A calibration plot and this increased in the post-TRIPOD period (pre-TRI- POD: 50%, post-TRIPOD: 82%)). Discrimination was primarily assessed with the C-statistic and Area Under the Curve (AUC) methods (pre-TRIPOD: 91%, post-TRIPOD: 100%). Measures of classification were reported in more than half of the studies (pre-TRIPOD: 69%, post-TRIPOD: 58%),mostly assessed with diagnostic test summary statistics (i.e. sensitivity, specificity and positive and negative predictive values) (pre-TRIPOD: 63%, post-TRIPOD: 50%) and to a lesser extent the integrated discrimination improvement (IDI; pre-TRIPOD: 16%, post-TIRPOD: 11%) or the net reclassification improvement (NRI; pre-TRIPOD 25%, post-TRIPOD: 18%).
Model Development. The consultant shall utilize the FDOT approved computer-based tools are to calculate and evaluate signal timing. Since many of these tools assume the presence of under-saturated conditions, it is important to recognize their capabilities and limitations. The requirements for developing timings for saturated and under-saturated conditions should be considered as the model is developed. The consultant should consider the following elements: • Establish a “standards and conventions” document (i.e., file naming, map settings, base data parameters, analysis settings) that provides the user with consistency through the retiming process; • Review the plan development in levels or stages to ensure efficiency; • Coordinate with the respective signal maintaining agencies; and • Include quality assurance and quality control measures.
Model Development. This section describes the methods used to compile data for the hydrologic restoration evaluation. The goal of the hydrologic report is to conduct an evaluation on pre and post hydrologic conditions that will occur during the rehydration of the Breakfast Point mitigation area. The Breakfast Point primary stormwater management system (PSWMS) consists of connected series of natural creeks and ditched canals. Characteristic data was obtained from a new field survey, site visits, interviews, topographic and aerial maps. Survey locations and data are illustrated in Exhibits 3-1 and 3-2. For this study, the existing Breakfast Point PSWMS was represented with sixteen (16) hydrologic basins (storage) connected by thirteen (13) link (conveyance/structure) nodes. A nodal diagram is included in Exhibit 3-3. The nodes identified in the Breakfast Point PSWMS can be classified as either conveyance or storage elements. Conveyance elements include closed conduits, open channels, bridge crossings and road overflows that collect and route runoff through the system. Storage elements (basin nodes) include closed basins, natural depression areas that store and attenuate runoff within the system. Sixteen basins were delineated for this study and are identified in Exhibit 3-4 and represented with the symbols N-10 through N-160. Link structures (culverts, bridges, low areas) at basin outflows are also represented in Exhibit 3-4 and are labeled similar to L-071.
Model Development determine cell number and growth kinetics for use in efficacy model (do we need to create a new luciferized model)
Model Development. Xxxxxxxxx developed a new TransCAD-based travel demand model for the KYTC District 9 region. The model covers eight counties (Bath, Carter, Elliott, Fleming, Greenup, Xxxxx, Xxxxx and Xxxxx) in northeastern Kentucky and three counties (Xxxxx, Xxxxx and Scioto) in southern Ohio. The District 9 Model was developed using KYTC’s standardized modeling procedure. Xxxxxxxxx developed TAZ boundaries, zonal data and roadway network for 2015 base year and 2040 future year. This time-of-day (XXX) model includes four dayparts (AM, midday, PM and night) and integrates an enhanced truck model (by single-unit truck and combination truck) in the model stream, using Quick Response Freight Manual II and origin-destination matrix estimation (ODME) techniques. The modeling process incorporated Big Data (AirSage) for trip generation, trip distribution, XXX distribution and directional pattern estimations. In particular, Xxxxxxxxx conducted a research to assess trip generation rates by purpose in terms of area types, based on AirSage OD data. The derived area type factors were embedded into the trip generation forecasting procedure to improve the model. The 2015 Base Model was validated to KYTC’s validation targets. Xxxxxxxxx enhanced the model’s reporting function to provide further detailed validation results for auto and trucks. A new separate reporting capability was also added into the model to summarize system performance, such as vehicle- miles traveled (VMT), vehicle-hours traveled (VHT), and speed by vehicle class, functional classification and county. KYTC On-Call Modeling. Since the early 2000s Xxxxxxxxx has served the Kentucky Transportation Cabinet as an on-call modeling consultant. This work has covered: • Update and enhancement of the Kentucky Statewide Traffic Model (KYSTM). • Development of a TransCAD model for Ashland, Paducah, Versailles, Owensboro/Xxxxxxxxx, District 9, Lexington, Xxxxxx County and Elizabethtown, KT. • Development of design traffic forecasts for widening I-71 in Louisville, KT. • Development of project planning traffic for KY-101 in Xxxxx’x Grove, KT. • Modeling for the SHIFT program. • Development of the Traffic Forecasting Tool (TFT). • TransModeler microsimulation for Frankfort. • Development of the TREDIS data support tool. • KYTC staff training. Xxxxxxxxx has been selected to serve in this capacity for nine 2-year terms, most recently in 2020.
Model Development. Descriptive statistics were analyzed for outliers and the final model was run with and without cases identified as outliers. Although the removal of outliers produced no changes to the interpretation, an extreme outlier based on UVS was removed (the participant reported 100 more sex acts than the next highest participant). There was minimal missing data (N=13) for the economic index variable and all cases were retained in the analysis. The descriptive statistics for the calibration sample are available by request. The measurement model provided a good fit to the data (CFI=.94 and RMSEA=.05). Paths described by the hypothesized model that demonstrated significant bivariate associations in the measurement model were retained in model testing. The structural model demonstrated a good fit, with a CFI of .94 and an RMSEA of .05. However, it was noted that the latent variable of knowledge did not demonstrate a hypothesized association at the level of p < .10 and it was dropped from the model in the interest of parsimony. The final, more parsimonious model afforded slightly better fit statistics (CFI = .95, RMSEA = .05).
