scholarly journals Impact of Carriage Crowding Level on Bus Dwell Time: Modelling and Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yiming Bie ◽  
Yunhao Wang ◽  
Le Zhang
Keyword(s):  
Dwell Time ◽  
Mean Absolute Error ◽  
Time Estimation ◽  
Absolute Error ◽  
Regression Method ◽  
Travel Time Estimation ◽  
The Mean ◽  
Bus Schedule ◽  
The Impact ◽  
Estimation Models

This paper develops two types of estimation models to quantify the impacts of carriage crowding level on bus dwell time. The first model (model I) takes the crowding level and the number of alighting and boarding passengers into consideration and estimates the alighting time and boarding time, respectively. The second model (model II) adopts almost the same regression method, except that the impact of crowding on dwell time is neglected. The analysis was conducted along two major bus routes in Harbin, China, by collecting 640 groups of dwell times under crowded condition manually. Compared with model II, the mean absolute error (MAE) of model I is reduced by 137.51%, which indicates that the accuracy of bus dwell time estimation could be highly improved by introducing carriage crowding level into the model. Meanwhile, the MAE of model I is about 3.9 seconds, which is acceptable in travel time estimation and bus schedule.

scholarly journals A novel reflectance-based model for evaluating chlorophyll concentration of fresh and water-stressed leaves

2013 ◽  
Vol 10 (11) ◽  
pp. 17893-17937
Author(s):  
C. Lin ◽  
S. C. Popescu ◽  
S. C. Huang ◽  
Y. C. Chen ◽  
P. T. Chang ◽  
...  
Keyword(s):  
Water Stress ◽  
Water Deficit ◽  
Mean Absolute Error ◽  
Chlorophyll Concentration ◽  
Absolute Error ◽  
Spectral Indices ◽  
Absorption Bands ◽  
Red Edge ◽  
The Mean ◽  
Estimation Models

Abstract. Water deficit can cause chlorophyll degradation which decreases foliar chlorophyll concentration (Chls). Few studies investigated the effectiveness of spectral indices under water stress conditions. Chlorophyll meters have been extensively used for a wide variety of leaf chlorophyll and nitrogen estimations. Since a chlorophyll meter works based on the sensing of leaves absorptance and transmittance, the reading of chlorophyll concentration will be affected by changes in transmittance as if there is a water deficit in leaves. The overall objective of this paper was to develop a novel and reliable reflectance-based model for estimating Chls of fresh and water stressed leaves using the reflectance at the absorption bands of chlorophyll a and b and the red edge spectrum. Three independent experiments were designed to collect data from three leaf sample sets for the construction and validation of Chls estimation models. First, a reflectance experiment was conducted to collect foliar Chls and reflectance of leaves with varying water stress using the ASD FieldSpec spectroradiometer. Second, a chlorophyll meter (SPAD-502) experiment was carried out to collect foliar Chls and meter reading. These two datasets were separately used for developing reflectance-based or absorptance-based Chls estimation models using linear and nonlinear regression analysis. Suitable models were suggested mainly based on the coefficient of determination (R2). Finally, an experiment was conducted to collect the third dataset for the validation of Chls models using the root mean squared error (RMSE) and the mean absolute error (MAE). In all of the experiments, the observations (real values) of the foliar Chls were extracted from acetone solution and determined by using a Hitachi U-2000 spectrophotometer. The spectral indices in the form of reflectance ratio/difference/slope derived from the chlb absorption bands (ρ645 and ρ455) provided Chls estimates with RMSE around 0.40–0.55 mg g–1 for both fresh and water-stressed samples. We improved Chls prediction accuracy by incorporating the reflectance at red edge position (ρREP) in regression models. An effective chlorophyll indicator with the form of (ρ645–ρ455) / ρREP proved to be the most accurate and stable predictor for foliar Chls concentration. This model was derived with an R2 of 0.90 (P < 0.01) from the training samples and evaluated with RMSE 0.35 and 0.38 mg g–1 for the validation samples of fresh and water stressed leaves, respectively. The average prediction error was within 14% of the mean absolute error.

scholarly journals A novel reflectance-based model for evaluating chlorophyll concentrations of fresh and water-stressed leaves

2015 ◽  
Vol 12 (1) ◽  
pp. 49-66 ◽  
Author(s):  
C. Lin ◽  
S. C. Popescu ◽  
S. C. Huang ◽  
P. T. Chang ◽  
H. L. Wen
Keyword(s):  
Chlorophyll A ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Spectral Indices ◽  
Absorption Bands ◽  
Chlorophyll Meter ◽  
Red Edge ◽  
Chlorophyll A And B ◽  
The Mean ◽  
Estimation Models

Abstract. Water deficits can cause chlorophyll degradation which decreases the total concentration of chlorophyll a and b (Chls). Few studies have investigated the effectiveness of spectral indices under water-stressed conditions. Chlorophyll meters have been extensively used for a wide variety of leaf chlorophyll and nitrogen estimations. Since a chlorophyll meter works by sensing leaves absorptance and transmittance, the reading of chlorophyll concentration will be affected by changes in transmittance as if there were a water deficit in the leaves. The overall objective of this paper was to develop a novel and reliable reflectance-based model for estimating Chls of fresh and water-stressed leaves using the reflectance at the absorption bands of chlorophyll a and b and the red edge spectrum. Three independent experiments were designed to collect data from three leaf sample sets for the construction and validation of Chls estimation models. First, a reflectance experiment was conducted to collect foliar Chls and reflectance of leaves with varying water stress using the ASD FieldSpec spectroradiometer. Second, a chlorophyll meter (SPAD-502) experiment was carried out to collect foliar Chls and meter readings. These two data sets were separately used for developing reflectance-based or absorptance-based Chls estimation models using linear and nonlinear regression analysis. Suitable models were suggested mainly based on the coefficient of determination (R2). Finally, an experiment was conducted to collect the third data set for the validation of Chls models using the root mean squared error (RMSE) and the mean absolute error (MAE). In all of the experiments, the observations (real values) of the foliar Chls were extracted from acetone solution and determined by using a Hitachi U-2000 spectrophotometer. The spectral indices in the form of reflectance ratio/difference/slope derived from the Chl b absorption bands (ρ645 and ρ455) provided Chls estimates with RMSE around 0.40–0.55 mg g−1 for both fresh and water-stressed samples. We improved Chls prediction accuracy by incorporating the reflectance at red edge position (ρREP) in regression models. An effective chlorophyll indicator with the form of (ρ645–ρ455)/ρREP proved to be the most accurate and stable predictor for foliar Chls concentration. This model was derived with an R2 of 0.90 (P < 0.01) from the training samples and evaluated with RMSE 0.35 and 0.38 mg g−1 for the validation samples of fresh and water-stressed leaves, respectively. The average prediction error was within 14% of the mean absolute error.

scholarly journals Dwell Time Estimation of Import Containers as an Ordinal Regression Problem

2021 ◽  
Vol 11 (20) ◽  
pp. 9380
Author(s):  
Laidy De Armas Jacomino ◽  
Miguel Angel Medina-Pérez ◽  
Raúl Monroy ◽  
Danilo Valdes-Ramirez ◽  
Carlos Morell-Pérez ◽  
...  
Keyword(s):  
Dwell Time ◽  
Mean Absolute Error ◽  
Container Terminal ◽  
Time Estimation ◽  
Absolute Error ◽  
Ordinal Regression ◽  
Regression Problem ◽  
Estimation Algorithms ◽  
Import Containers ◽  
Supervised Classifiers

The optimal stacking of import containers in a terminal reduces the reshuffles during the unloading operations. Knowing the departure date of each container is critical for optimal stacking. However, such a date is rarely known because it depends on various attributes. Therefore, some authors have proposed estimation algorithms using supervised classification. Although supervised classifiers can estimate this dwell time, the variable “dwell time” takes ordered values for this problem, suggesting using ordinal regression algorithms. Thus, we have compared an ordinal regression algorithm (selected from 15) against two supervised classifiers (selected from 30). We have set up two datasets with data collected in a container terminal. We have extracted and evaluated 35 attributes related to the dwell time. Additionally, we have run 21 experiments to evaluate both approaches regarding the mean absolute error modified and the reshuffles. As a result, we have found that the ordinal regression algorithm outperforms the supervised classifiers, reaching the lowest mean absolute error modified in 15 (71%) and the lowest reshuffles in 14 (67%) experiments.

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

Visualizing the Variance of a Random Variable

2011 ◽  
Vol 18 (01) ◽  
pp. 71-85
Author(s):  
Fabrizio Cacciafesta
Keyword(s):  
Stochastic Dominance ◽  
Mean Absolute Error ◽  
Random Variable ◽  
Absolute Error ◽  
Second Order ◽  
Taylor Formula ◽  
Order Stochastic Dominance ◽  
The Mean ◽  
Second Order Stochastic Dominance ◽  
Options Theory

We provide a simple way to visualize the variance and the mean absolute error of a random variable with finite mean. Some application to options theory and to second order stochastic dominance is given: we show, among other, that the "call-put parity" may be seen as a Taylor formula.

scholarly journals Direct Calculation of Thermodynamic Wet-Bulb Temperature as a Function of Pressure and Elevation

2013 ◽  
Vol 30 (8) ◽  
pp. 1757-1765 ◽  
Author(s):  
Sayed-Hossein Sadeghi ◽  
Troy R. Peters ◽  
Douglas R. Cobos ◽  
Henry W. Loescher ◽  
Colin S. Campbell
Keyword(s):  
Air Temperature ◽  
Mean Absolute Error ◽  
Direct Calculation ◽  
Ambient Air ◽  
Absolute Error ◽  
Mean Square ◽  
Air Temperatures ◽  
Order Polynomial ◽  
The Mean ◽  
Very High

Abstract A simple analytical method was developed for directly calculating the thermodynamic wet-bulb temperature from air temperature and the vapor pressure (or relative humidity) at elevations up to 4500 m above MSL was developed. This methodology was based on the fact that the wet-bulb temperature can be closely approximated by a second-order polynomial in both the positive and negative ranges in ambient air temperature. The method in this study builds upon this understanding and provides results for the negative range of air temperatures (−17° to 0°C), so that the maximum observed error in this area is equal to or smaller than −0.17°C. For temperatures ≥0°C, wet-bulb temperature accuracy was ±0.65°C, and larger errors corresponded to very high temperatures (Ta ≥ 39°C) and/or very high or low relative humidities (5% &lt; RH &lt; 10% or RH &gt; 98%). The mean absolute error and the root-mean-square error were 0.15° and 0.2°C, respectively.

scholarly journals A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Ludi Wang ◽  
Wei Zhou ◽  
Ying Xing ◽  
Xiaoguang Zhou
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Mean Absolute Error ◽  
Wearable Sensors ◽  
Absolute Error ◽  
Noninvasive Estimation ◽  
Treatment Of Hypertension ◽  
The Mean ◽  
Artificial Neural Network Ann ◽  
Blood Pressure Estimation

The prevention, evaluation, and treatment of hypertension have attracted increasing attention in recent years. As photoplethysmography (PPG) technology has been widely applied to wearable sensors, the noninvasive estimation of blood pressure (BP) using the PPG method has received considerable interest. In this paper, a method for estimating systolic and diastolic BP based only on a PPG signal is developed. The multitaper method (MTM) is used for feature extraction, and an artificial neural network (ANN) is used for estimation. Compared with previous approaches, the proposed method obtains better accuracy; the mean absolute error is 4.02 ± 2.79 mmHg for systolic BP and 2.27 ± 1.82 mmHg for diastolic BP.

Validating the usefulness of sectorwise regression of visual field in the central 10°

2021 ◽  
pp. bjophthalmol-2020-317391
Author(s):  
Takashi Omoto ◽  
Hiroshi Murata ◽  
Yuri Fujino ◽  
Masato Matsuura ◽  
Takehiro Yamashita ◽  
...  
Keyword(s):  
Visual Field ◽  
Mean Absolute Error ◽  
Open Angle Glaucoma ◽  
Absolute Error ◽  
Open Angle ◽  
Clustering Method ◽  
Significant Difference ◽  
The Mean ◽  
Test Points

AimTo evaluate the usefulness of the application of the clustering method to the trend analysis (sectorwise regression) in comparison with the pointwise linear regression (PLR).MethodsThis study included 153 eyes of 101 patients with open-angle glaucoma. With PLR, the total deviation (TD) values of the 10th visual field (VF) were predicted using the shorter VF sequences (from first 3 to 9) by extrapolating TD values against time in a pointwise manner. Then, 68 test points were stratified into 29 sectors. In each sector, the mean of TD values was calculated and allocated to all test points belonging to the sector. Subsequently, the TD values of the 10th VF were predicted by extrapolating the allocated TD value against time in a pointwise manner. Similar analyses were conducted to predict the 11th–16th VFs using the first 10 VFs.ResultsWhen predicting the 10th VF using the shorter sequences, the mean absolute error (MAE) values were significantly smaller in the sectorwise regression than in PLR. When predicting from the 11th and 16th VFs using the first 10 VFs, the MAE values were significantly larger in the sectorwise regression than in PLR when predicting the 11th VF; however, no significant difference was observed with other VF predictions.ConclusionAccurate prediction was achieved using the sectorwise regression, in particular when a small number of VFs were used in the prediction. The accuracy of the sectorwise regression was not hampered in longer follow-up compared with PLR.

Accuracy Assessment of TanDEM-X 90 and CartoDEM Using ICESat-2 Datasets for Plain Regions of Ratlam City and Surroundings

2021 ◽  
Vol 10 (1) ◽  
pp. 59
Author(s):  
Unnati Yadav ◽  
Ashutosh Bhardwaj
Keyword(s):  
Mean Absolute Error ◽  
Accuracy Assessment ◽  
Absolute Error ◽  
Terrain Analysis ◽  
Mean Square ◽  
High Quality ◽  
Ice Cloud ◽  
The Mean ◽  
Cut And Fill ◽  
Statistical Results

The spaceborne LiDAR dataset from the Ice, Cloud, and Land Elevation Satellite (ICESat-2) provides highly accurate measurements of heights for the Earth’s surface, which helps in terrain analysis, visualization, and decision making for many applications. TanDEM-X 90 (90 m) and CartoDEM V3R1 (30 m) elevation are among the high-quality openly accessible DEM datasets for the plain regions in India. These two DEMs are validated against the ICESat-2 elevation datasets for the relatively plain areas of Ratlam City and its surroundings. The mean error (ME), mean absolute error (MAE), and root mean square error (RMSE) of TanDEM-X 90 DEM are 1.35 m, 1.48 m, and 2.19 m, respectively. The computed ME, MAE, and RMSE for CartoDEM V3R1 are 3.05 m, 3.18 m, and 3.82 m, respectively. The statistical results reveal that TanDEM-X 90 performs better in plain areas than CartoDEMV3R1. The study further indicates that these DEMs and spaceborne LiDAR datasets can be useful for planning various works requiring height as an important parameter, such as the layout of pipelines or cut and fill calculations for various construction activities. The TanDEM-X 90 can assist planners in quick assessments of the terrain for infrastructural developments, which otherwise need time-consuming traditional surveys using theodolite or a total station.

Intraocular lenses optic power calculation with seven formulas in short eyes

2021 ◽  
pp. 57-61
Author(s):  
K.B. Pershin ◽  
◽  
N.F. Pashinova ◽  
I.A. Likh ◽  
А.Y. Tsygankov ◽  
...  
Keyword(s):  
Axial Length ◽  
Mean Absolute Error ◽  
Optical Power ◽  
Absolute Error ◽  
Intraocular Lenses ◽  
Power Calculation ◽  
Retrospective Comparison ◽  
Monofocal Iol ◽  
The Mean ◽  
Axial Eye Length

Purpose. The choice of the optimal formula for calculating the IOL optical power in patients with an axial eye length of less than 20 mm. Material and methods. A total of 78 patients (118 eyes) were included in the prospective study. 1st group included 30 patients (52 eyes) with short eyes (average axial eye length of 19.60±0.42 (18.54–20.0) mm), 2nd group consisted of 48 patients (66 eyes) with a axial length 22.75±0.46 (22.0–23.77) mm. Various monofocal IOL models were used. The average follow-up period was 13 months. IOL optical power was calculated using the SRK/T formula, retrospective comparison – according to the formulas Hoffer-Q, Holladay II, Olsen, Haigis, Barrett Universal II and Kane. Results. In 1st group, the mean absolute error was determined for the formulas Haigis, Olsen, Barrett Universal II, Kane, SRK/T, Holladay II and Hoffer-Q (0.85, 0.78, 0.21, 0.17, 0.79, 0.73, 0.19 respectively). When comparing the formulas, significant differences were found for the formulas Hoffer-Q, Barrett Universal II and Kane in comparison with the formulas Haigis, Olsen, SRK/T and Holladay II (p<0.05) in all cases, respectively. In 2nd group, the mean absolute error was determined for the formulas Haigis, Olsen, Barrett Universal II, Kane, SRK/T, Holladay II and Hoffer-Q (0.15, 0.16, 0.23, 0.10, 0.19, 0.23, 0,29 respectively). In 2nd group, there were no significant differences between the studied formulas (p>0.05). Conclusion. This paper presents an analysis of data on the effectiveness of seven formulas for calculating the IOL optical power in short (less than 20 mm) eyes in comparison with the normal axial length. The advantage of the Hoffer-Q, Barrett Universal II and Kane formulas over Haigis, Holladay II, Olsen, and SRK/T is shown. Key words: cataract, hypermetropia, short eyes, calculation of the IOL optical power.

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