Outlier Detection and Effects on Modeling

A trip generation manual and database are important for transportation planners and engineers to forecast new trip generation for any new development. Nowadays, many petrol stations have fast-food restaurant outlets. However, this land use category has yet to be included in the Malaysian Trip Generation Manual. Therefore, this study attempted to develop a new trip generation model for the new category of “petrol station with convenience store and fast-food restaurant”. Significant factors influencing the trip generation were also determined. Manual vehicle counts at the selected sites were conducted for 3 h during morning, afternoon and evening peak hours. Regression analysis was used in this study to develop the model. A simple trip generation model based on the independent variable number of restaurant seats showed a greater value for the coefficient of determination, R2, compared with the independent variables gross floor area in thousand square feet and number of pumps. The multivariable trip generation model using three independent variables generated the highest R2 among all of the models but was still below a satisfactory level. Further study is needed to improve the model for this new land use category. We must ensure more accuracy in trip generation estimation for future planning and development.

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“…an outlier [53]. All data points indicate d < 3, which means there were no potentially highinfluence points.…”

Section: Simple Linear Regressionmentioning

confidence: 98%

“…Standardized residuals were computed to identify the existence of outliers in the model. A large, standardized residual of (d > 3) indicates an outlier [53]. All data points indicate d < 3, which means there were no potentially high-influence points.…”

Section: Simple Linear Regressionmentioning

confidence: 99%

A Trip Generation Model for a Petrol Station with a Convenience Store and a Fast-Food Restaurant

2021

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“…This result causes the prediction as far from the actual observation. Such misrepresentations can lead to incorrect conclusions and findings [13]. The first step before the data analysis phase is identifying outliers using the diagnostic method.…”

Section: Outlier Identificationmentioning

confidence: 99%

“…These conditions can cause the appearance of outliers. The existence of outliers in the data leads to a non-robust model which causes errors in parameter estimation [13]. The impact is that the resulting interpretation becomes inaccurate, one of which is districts/cities that should have a high poverty rate but become low and vice versa.…”

Section: Introductionmentioning

confidence: 99%

Outlier Identification on Penalized Spline Regression Modeling for Poverty Gap Index in Java

Fadilah

Fitrianto²,

Sumertajaya³

2022

BAREKENG: J. Math. & App.

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Java is one of the islands in Indonesia which has good establishment acceleration. Even though economic growth was good, poverty is still a serious problem. Three of six provinces, including DI Yogyakarta, Central Java, and East Java still have poverty rates above national rates in March 2020. This problem indicates that an imbalance in poverty happens between those regions. Several regions have extreme conditions or known as outliers. Besides that, poverty gap data have a complex pattern so modeling using a non-parametric approach is suitable. This study aims to build an appropriate model to support the success of poverty alleviation in Java and the identification of outliers was carried out using an adjusted boxplot. The best-penalized regression spline model for Poverty Gap Index in Java Island was obtained by Generalized minimum Cross-Validation (GCV) using optimum smoothing parameter (λ) 0,12 and knot combination (1, 2, 4, 1, 5, 3, and 1) for seven predictor variables. The result shows that penalized spline regression model has a higher R2 than OLS regression. The R2 is obtained 69,10%, so the model is feasible to explain the variability of the poverty gap in Java. Moreover, based on the outliers’ identification shows a dependency between outlier in data and residual because some districts/cities are identified as outliers in both.

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“…As the modulus of the Si electrode generally decreases with increasing x , we assume that there are some potential outliers. Therefore, standardized residual (SR) analysis was performed to identify the outliers, , and the measured moduli with |SR| > 3, 2.5, and 2 were excluded from the data set. The average moduli of the Si thin film electrode throughout the first lithiation with outliers (|SR| > 2.5) excluded are presented in Figure a.…”

mentioning

confidence: 99%

Operando Nanomechanical Mapping of Amorphous Silicon Thin Film Electrodes in All-Solid-State Lithium-Ion Battery Configuration during Electrochemical Lithiation and Delithiation

Putra,

Matsushita,

Ohnishi

et al. 2024

J. Phys. Chem. Lett.

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An operando bimodal atomic force microscopy system was constructed to perform nanomechanical mapping of an amorphous Si thin film electrode deposited on a Li 6.6 La 3 Zr 1.6 Ta 0.4 O 12 solid electrolyte sheet during electrochemical lithiation/ delithiation. The evolution of Young's modulus maps of the Si electrode was successfully tracked as a function of apparent Li content x in lithium silicide (Li x Si) simultaneously with real-time surface topography observation. At the initial stage of lithiation, the average modulus steeply decreased due to the generation of Li x Si from intrinsic Si, followed by a moderate modulus reduction until the electrode capacity reached 3300 mAh g −1 (Li content x = 3.46). In the following delithiation, the gradual recovery of the average modulus of Li x Si was observed up to 1467 mAh g −1 (Li content x = 1.54) at which delithiation stopped due to the significant volume change induced by phase transformation of Li x Si.

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Outlier Detection and Effects on Modeling

Cited by 15 publications

References 12 publications

A Trip Generation Model for a Petrol Station with a Convenience Store and a Fast-Food Restaurant

A Trip Generation Model for a Petrol Station with a Convenience Store and a Fast-Food Restaurant

Outlier Identification on Penalized Spline Regression Modeling for Poverty Gap Index in Java

Operando Nanomechanical Mapping of Amorphous Silicon Thin Film Electrodes in All-Solid-State Lithium-Ion Battery Configuration during Electrochemical Lithiation and Delithiation

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