Fitting a multiple regression line to travel demand forecasting: The case of the prefecture of Xanthi, Northern Greece

Varagouli, E. G.; Simos, T. E.; Xeidakis, G. S.

doi:10.1016/j.mcm.2005.09.010

Cited by 21 publications

(14 citation statements)

References 4 publications

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“…A normal distribution with a log link function is chosen to model these data. This form takes the shape given in equation (4), Varagouli et al [13]:…”

Section: Glmmentioning

confidence: 99%

Utjecaj stanja višetračnih kolnika na promet osobnih vozila

2016

JCE

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Impact of pavement condition on passenger car trafficThe present paper explores the influence of pavement condition, roughness, and longitudinal grade on the operating speed (V 85 ) of passenger car traffic at multi-lane roads. The pavement condition is described as a pavement condition index, while pavement roughness is expressed as an international roughness index. The necessary data are collected at 67 tangent sections and the following three modelling approaches are adopted for analysis: linear regression, multiple regression analysis, and artificial neural network. The obtained results show that the artificial neural network modelling approach is the best one for estimating the operating speed V 85 in terms of main statistical parameters. Utjecaj stanja višetračnih kolnika na promet osobnih vozila U ovom radu istražen je utjecaj stanja kolnika, ravnosti kolnika i uzdužnog nagiba na iznos operativne brzine osobnih vozila (V 85 ) na višetračnim cestama. Stanje kolnika opisano je pomoću indeksa stanja kolnika, dok je ravnost kolnika opisana međunarodnim indeksom ravnosti. Podaci za ispitivanje prikupljeni su na 67 dionica ceste u pravcu, a u analizi tih dionica primijenjena su tri različita postupka modeliranja: linearna regresija, generalizirano linearno modeliranje te umjetna neuronska mreža. Rezultati istraživanja su pokazali da je primjena umjetne neuronske mreže najbolja za procjenu operativne brzine V 85 s obzirom na glavne statističke parametre.

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“…A normal distribution with a log link function is chosen to model these data. This form takes the shape given in equation (4), Varagouli et al [13]:…”

Section: Glmmentioning

confidence: 99%

Utjecaj stanja višetračnih kolnika na promet osobnih vozila

2016

JCE

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“…SAS user's manual [20] applies the maximum log-likelihood technique to estimate the regression coefficients, standard errors, Wald Chi-squared statistics, p-values. In addition, the R 2 (coefficient of determination) and (infinity norm of error vector) ║δ║ values are calculated for each model as Varagouli et al [18]. Finally, the model with minimum ║δ║ and highest R 2 value is selected.…”

Section: Glm Proceduresmentioning

confidence: 99%

Derivation of level of service by artificial neural networks at horizontal curves: a case study in Egypt

Semeida

2015

Eur. Transp. Res. Rev.

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Purpose Multi-lane highways represent the majority of the total length of highway network at many countries. The geometric design of such facilities and the traffic volume which includes heavy vehicles percentage (HV) are considered the most important factors affecting the level of service (LOS), especially on the horizontal curves. Methods This paper aims to explore the relationship between the road geometric characteristics including horizontal curve properties and traffic volume including average annual daily traffic (AADT) and HV in one hand and LOS in the other hand by two methods. First is generalized linear modeling (GLM) procedure and second is the artificial neural networks (ANNs) procedure. In this research, the traffic and road geometric data are collected on 78 horizontal curves that are distributed on four multi-lane highways in Egypt. Two of them are located in desert area and the others in agricultural area.

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“…During the past two decades, a number of researchers have developed various travel demand forecasting models to predict passenger flow and trends, such as conventional travel demand modeling, and multiple regression (e.g., Alfa, 1986;Wirasinghe and Kumarage, 1998;Kulshreshtha and Nag, 2000;Golias, 2002;Jovicic and Hansen, 2003;Bar-Gera and Boyce, 2003;Varagouli et al, 2005;Wardman, 2006;Tsekeris and Stathopoulos, 2006;Zhou and Kockelman, 2009). The conventional travel demand forecasting model is a sequential demand modeling method considering trip generation, trip distribution, mode choice, and assignment modules.…”

Section: Introductionmentioning

confidence: 99%

“…The inconsistent consideration of congestion effects was argued as the shortcoming of sequential demand modeling methods (Bar-Gera and Boyce, 2003). Furthermore, some studies have revealed that the conventional forecasting model of travel demand lacks volume variation when it faces the variants of economic activity (Kulshreshtha and Nag, 2000;Varagouli et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, the multiple regression method and multivariate dynamic econometric time series have been applied to construct transportation demand models of passenger flow (Kulshreshtha and Nag, 2000;Kuby et al, 2004;Varagouli et al, 2005;Wardman, 2006). These methods considered some external factors such as economic variables to predict passenger flow.…”

Section: Introductionmentioning

confidence: 99%

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