scholarly journals Model for Estimating Travel Time on Dynamic Highway Networks in Akure, Ondo State Nigeria

2020 ◽  
Vol 5 (3) ◽  
pp. 275-281
Author(s):  
Onyemaechi John Nnamani ◽  
Victor Ayodele Ijaware ◽  
Joseph Olalekan Olusina ◽  
Timothy Oluwadare Idowu
Keyword(s):  
Regression Model ◽  
Travel Time ◽  
Traffic Volume ◽  
Real Field ◽  
Percentage Error ◽  
Time Variability ◽  
Time Model ◽  
Independent Variables ◽  
Highway Networks ◽  
Significant Difference

Travel time variability or distribution is very important to travel time reliability studies in transportation systems. This study aimed at developing a multivariate regression model for estimating travel times for dynamic highway networks in Akure Metropolis. The independent variables for the model are Traffic volume, density, speed of vehicles, and traffic flow while the dependent response variable is the Travel time. The estimated travel time was compared with the observed travel time from the real field data and the estimation using the regression model reveals a significant level of accuracy. Also, it was discovered that traffic volume, speed, density, and flow were highly correlated with travel time. The result analyzed using descriptive statistics in the SPSS software environment reveals an R2 value of 0.998, thereby indicating that the independent variables accounted for 99% of travel time in the study area. The Hypothesis tested at 95% confidence level using ANOVA unveils that there is no significant difference between the observed and estimated travel time model. The Mean Absolute Percentage Error (MAPE) of 0.049 shows that the model performed very well and was very efficient for analyzing the probabilistic relation between travel time and the independent variables. The study recommends the use of the developed travel time model for estimating travel time within the study area.

scholarly journals GMAT And Other Determinants Of GPA In An MBA Program

2010 ◽  
Vol 3 (12) ◽  
pp. 77-86
Author(s):  
Bharat Kolluri ◽  
Rao Singamsetti ◽  
Mahmoud Wahab
Keyword(s):  
Regression Model ◽  
Grade Point Average ◽  
Potential Influence ◽  
Chi Square ◽  
Grade Point ◽  
Independent Variables ◽  
Mba Students ◽  
Mba Program ◽  
Significant Difference ◽  
And Gender

This paper reports on the influence of waiving the GMAT requirement on academic performance as measured by grade-point-average at graduation for 833 University of Hartford MBA students who graduated between 2003 and 2009. In seeking to better understand what factors might be influencing graduation GPA, we examined a variety of traditional attributes. In addition, we examined the potential influence of GMAT Waivers on graduation GPA because there was some thought that students who waived this test might have lower graduation GPAs than those who took the examination. The results of this study indicated that the most important factor for determining MBA graduation GPA was an individual’s undergraduate GPA, with higher undergraduate GPAs being associated with higher MBA graduation GPAs. Marginally significant differences in graduation GPA were also found based on the number of credits waived at entry into the MBA program, with more credits being waived contributing to a higher graduation GPA. We also found that women graduated with higher GPAs than men. Of particular interest to us in this study, however, was whether or not our GMAT Waiver policy was influencing graduation GPAs. In this case, we found no significant difference in graduation GPA, regardless of whether or not the GMAT requirement was waived. These results were confirmed using chi-square tests and two-sample t-tests. To gain additional insights into these issues, we estimated a regression model to explain graduation GPA using several attributes as independent variables. The regression results indicate that undergraduate GPA and gender seemed to most reliably predict differences in graduation GPA.

scholarly journals Bridge Construction Cost Prediction using Multiple Linear Regression

2019 ◽  
Vol 8 (9) ◽  
pp. 3115-3121
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Correlation Method ◽  
Multiple Linear Regression Model ◽  
Percentage Error ◽  
Cost Prediction ◽  
Independent Variables ◽  
Qualitative Variables ◽  
Leave One Out

Cost of construction of bridges is predicted using multiple linear regression model, based on data of bridges from Maharashtra state in India. Cost per unit area is taken as an appropriate dependent variable. Using both conventional and double log regression techniques, models for cost/m2 and log of cost/m2 are developed. Total 6 independent variables, which include both qualitative and quantitative variables, are used to develop the model. Height of bridge, average span length and depth of foundation are used as quantitative variables. Zone of construction, deck type and foundation type are used as qualitative variables in developing model. Strength of these independent variables with dependent variable is found out using pearson’s correlation method. Model is then verified using Leave One Out Cross Validation (LOOCV) technique. The most suited regression model obtained from the data experiment is double log regression with R2 of 0.850 and a Mean Absolute Percentage Error (MAPE) of 17.74%, as compared to 25% MAPE observed in past for studies related to traditional cost prediction.

Method for Estimating Vehicle-to-Vehicle Travel Time Variability Models at the Link and Network Levels of Freeways/Expressways through Censoring Mechanism

2019 ◽  
Vol 2673 (5) ◽  
pp. 548-563
Author(s):  
Whoibin Chung ◽  
Mohamed Abdel-Aty ◽  
Qing Cai ◽  
Raj Ponnaluri
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Travel Time ◽  
Linear Regression Model ◽  
Rainfall Amount ◽  
Time Variability ◽  
Speed Limits ◽  
Vehicle To Vehicle ◽  
Vehicle Travel Time ◽  
Travel Time Variability

A method was proposed to estimate vehicle-to-vehicle travel time variability (TTV) at the link and network levels of the entire freeway network. Standard deviation (SD) of travel time rate (TTR) was selected for the TTV. Models estimating the TTV were developed through a Tobit model using a left-censored limit. For the analysis of impact factors on TTV including day-to-day, the model included various types of variables: density, occupancy, traffic flow, link lengths, lane count, speed limits, rainfall amount, crash indicator, weekend indicator, and holiday indicator. According to the exploration and modeling results, TTR and its SD (vehicle-to-vehicle and day-to-day) have a statistical positive significant relationship at the link and network levels. Furthermore, it was confirmed that there is network fundamental diagram (NFD) at the network level. According to the modeling results, an increase in the number of lanes and speed limits, and crash occurrence, raises vehicle-to-vehicle and day-to-day TTV. However, TTV decreases if the link length is long. A high rainfall amount would reduce vehicle-to-vehicle TTV, but raise day-to-day TTV. Weekends and holidays increase vehicle-to-vehicle TTV but diminish day-to-day TTV. Finally, a linear regression model between TTV and TTR at the network level was developed. Through the relationship between the linear regression model and NFD, it is possible to develop new traffic management strategies and algorithms optimizing the vehicle-to-vehicle TTV at the network level. The developed vehicle-to-vehicle TTV models can be applied to validate the mobility improvement potential of vehicle-to-everything (V2X) communication applications on a segment, corridor, and regional scale.

Quantile Regression Analysis of Transit Travel Time Reliability with Automatic Vehicle Location and Farecard Data

2017 ◽  
Vol 2652 (1) ◽  
pp. 19-29 ◽  
Author(s):  
Zhenliang Ma ◽  
Sicong Zhu ◽  
Haris N. Koutsopoulos ◽  
Luis Ferreira
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Travel Time ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Central Tendency ◽  
Automatic Vehicle Location ◽  
Conditional Mean ◽  
Vehicle Location ◽  
Travel Time Variability

Transit agencies increasingly deploy planning strategies to improve service reliability and real-time operational control to mitigate the effects of travel time variability. The design of such strategies can benefit from a better understanding of the underlying causes of travel time variability. Despite a significant body of research on the topic, findings remain influenced by the approach used to analyze the data. Most studies use linear regression to characterize the relationship between travel time reliability and covariates in the context of central tendency. However, in many planning applications, the actual distribution of travel time and how it is affected by various factors is of interest, not just the condition mean. This paper describes a quantile regression approach to analyzing the impacts of the underlying determinants on the distribution of travel times rather than its central tendency, using supply and demand data from automatic vehicle location and farecard systems collected in Brisbane, Australia. Case studies revealed that the quantile regression model provides more indicative information than does the conditional mean regression method. Moreover, most of the coefficients estimated from quantile regression are significantly different from the conditional mean–based regression model in terms of coefficient values, signs, and significance levels. The findings provide information related to the impacts of planning, operational, and environmental factors on speed and its variability. On the basis of this information, transit designers and planners can design targeted strategies to improve travel time reliability effectively and efficiently.

scholarly journals Transversal craniofacial development between skeletal maturation stages: A multi-center posteroanterior cephalometric study

2021 ◽  
Vol 0 ◽  
pp. 1-9
Author(s):  
Türkan Sezen Erhamza ◽  
Burçin Akan ◽  
Saadet Çınarsoy Ciğerim ◽  
Yasemin Nur Korkmaz ◽  
Fatma Nazik Ünver
Keyword(s):  
Regression Model ◽  
Skeletal Maturation ◽  
Cross Sectional ◽  
Independent Variables ◽  
Significant Difference ◽  
Dependent Variables ◽  
Maturation Stages ◽  
Width Measurements ◽  
Cephalometric Study ◽  
Age Range

Objectives: The aim of this study is to evaluate the dentofacial transversal norms according to the stages of skeletal maturation in growing Turkish individuals and to determine differences between the genders. Materials and Methods: In our multi-centered, cross-sectional retrospective study, in which transversal measurements were made according to skeletal maturation stages (SMSs), posteroanterior radiographs of 572 individuals (292 female, 280 male) with skeletal and dental Class I relationships and good occlusion were examined at the age range of 7–18 years. SMSs were determined using Björk, Grave and Brown hand-wrist radiography. A linear regression model was used for changes of transversal measurements between SMSs, and t-test was used to determine transverse changes between the genders. Results: There was no statistically significant difference between females and males in cranial, facial, and nasal width values up to SMS 5. In maxillary, mandibular, maxillary intermolar, and mandibular intermolar width measurements, males had higher values in most stages of skeletal maturation compared to females. Apart from nasal width and maxillomandibular ratio values in females, the regression model in which transversal measurements were dependent variables, and SMS were independent variables was found to be significant. According to cumulative growth percentages, the growth completion in transversal measurements occurred earlier in females. Conclusion: Transversal measurements determined according to the stages of skeletal maturation can be a guide for orthodontists in the clinic to determine values that deviate from normal.

Road Pricing under Travel Time Variability

2019 ◽  
Author(s):  
Gege Jiang ◽  
Hong Kam LO ◽  
Zheng LIANG
Keyword(s):  
Travel Time ◽  
Road Pricing ◽  
Time Variability ◽  
Travel Time Variability

scholarly journals Empirical α–β runout modelling of snow avalanches in the Catalan Pyrenees

2021 ◽  
pp. 1-12
Author(s):  
Pere Oller ◽  
Cristina Baeza ◽  
Glòria Furdada
Keyword(s):  
Comparative Analysis ◽  
Regression Model ◽  
Extreme Values ◽  
Multiple Linear Regression Model ◽  
Present Knowledge ◽  
Snow Avalanches ◽  
Independent Variables ◽  
Horizontal Length ◽  
Length And Area ◽  
Runout Modelling

Abstract A variation in the α−β model which is a regression model that allows a deterministic prediction of the extreme runout to be expected in a given path, was applied for calculating avalanche runout in the Catalan Pyrenees. Present knowledge of major avalanche activity in this region and current mapping tools were used. The model was derived using a dataset of 97 ‘extreme’ avalanches that occurred from the end of 19th century to the beginning of 21st century. A multiple linear regression model was obtained using three independent variables: inclination of the avalanche path, horizontal length and area of the starting zone, with a good fit of the function (R2 = 0.81). A larger starting zone increases the runout and a larger length of the path reduces the runout. The new updated equation predicts avalanche runout for a return period of ~100 years. To study which terrain variables explain the extreme values of the avalanche dataset, a comparative analysis of variables that influence a longer or shorter runout was performed. The most extreme avalanches were treated. The size of the avalanche path and the aspect of the starting zone showed certain association between avalanches with longer or shorter runouts.

scholarly journals Measuring Recurrent and Nonrecurrent Traffic Congestion

2003 ◽  
Vol 1856 (1) ◽  
pp. 118-124 ◽  
Author(s):  
Alexander Skabardonis ◽  
Pravin Varaiya ◽  
Karl F. Petty
Keyword(s):  
Los Angeles ◽  
Travel Time ◽  
San Francisco ◽  
Traffic Congestion ◽  
Real Life ◽  
Time Variability ◽  
Loop Detectors ◽  
Bay Area ◽  
Freeway Corridors ◽  
Travel Time Variability

A methodology and its application to measure total, recurrent, and nonrecurrent (incident related) delay on urban freeways are described. The methodology used data from loop detectors and calculated the average and the probability distribution of delays. Application of the methodology to two real-life freeway corridors in Los Angeles, California, and one in the San Francisco, California, Bay Area, indicated that reliable measurement of congestion also should provide measures of uncertainty in congestion. In the three applications, incident-related delay was found to be 13% to 30% of the total congestion delay during peak periods. The methodology also quantified the congestion impacts on travel time and travel time variability.

Choice of a teleseismicP-wave travel-time model and the associated source corrections

1972 ◽  
Vol 101 (1) ◽  
pp. 74-89
Author(s):  
S. K. Arora ◽  
C. A. Krishnan
Keyword(s):  
Travel Time ◽  
Time Model ◽  
Wave Travel

Dampak Brand Image dan Harga terhadap Keputusan Pembelian Produk Miniso

2021 ◽  
Vol 2 (2) ◽  
pp. 75-87
Author(s):  
Kardinah Indrianna Meutia ◽  
Hadita Hadita ◽  
Wirawan Widjarnarko
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Brand Image ◽  
Multiple Linear Regression Model ◽  
Purchasing Decisions ◽  
Business World ◽  
Independent Variables ◽  
Spss Software ◽  
Fierce Competition

The economy in the current era of globalization has fierce competition, especially in the business world, where each company moves to continue to make products primarily to meet what is needed by consumers and companies are always innovating to make products that are different from before and from  competitors and strive to be superior to other products.  This study was conducted with the aim of analyzing the independent variables which include brand image and price variables on their influence on the dependent variable, namely purchasing decisions.  This study uses multiple linear regression model and with classical assumption test using SPSS software version 24. Data were obtained primarily by distributing questionnaires to 162 students at Bhayangkara University, Jakarta Raya.  This study states that brand image and price variables can partially and significantly influence consumer purchasing decisions positively. The F test explains that the brand image and price variables together can influence purchasing decisions with results showing f-count>f-table.

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