Impact of Horizontal Curves and Percentage of Heavy Vehicles on Right Lane Capacity at Multi-lane Highways

In the present research, the influence of road geometric properties and traffic characteristics on the right lane capacity value is explored for horizontal curves. The non-traditional procedure (artificial neural networks - ANNs), is adopted for modelling. The research utilizes 78 horizontal curves that provide the traffic and road geometry data, of which55 curves are classified as four-lane and the rest as six-lane ones. Two types of models are introduced to explore the right lane capacity as capacity at curves, and the capacity loss between curves and tangents. The results show that, for horizontal curves, the most effective variables affecting both road types are the percentage of heavy vehicles in traffic composition (HV) followed by radius of curve (R), and the lane width (LW). Furthermore, the capacity loss is also highly affected by R followed by HV. The derived outcomes present a remarkable move towards the beginning of an Egyptian highway design guide.</p

Impact of pavement condition on passenger car traffic

U ovom radu istražen je utjecaj stanja kolnika, ravnosti kolnika i uzdužnog nagiba na iznos operativne brzine osobnih vozila (V85) 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 V85 s obzirom na glavne statističke parametre.The present paper explores the influence of pavement condition, roughness, and longitudinal grade on the operating speed (V85) 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 V85 in terms of main statistical parameters

Impact of Horizontal Curves and Percentage of Heavy Vehicles on Right Lane Capacity at Multi-lane Highways

In the present research, the influence of road geometric properties and traffic characteristics on the right lane capacity value is explored for horizontal curves. The non-traditional procedure (artificial neural networks - ANNs), is adopted for modelling. The research utilizes 78 horizontal curves that provide the traffic and road geometry data, of which55 curves are classified as four-lane and the rest as six-lane ones. Two types of models are introduced to explore the right lane capacity as capacity at curves, and the capacity loss between curves and tangents. The results show that, for horizontal curves, the most effective variables affecting both road types are the percentage of heavy vehicles in traffic composition (HV) followed by radius of curve (R), and the lane width (LW). Furthermore, the capacity loss is also highly affected by R followed by HV. The derived outcomes present a remarkable move towards the beginning of an Egyptian highway design guide

New models to evaluate the level of service and capacity for rural multi-lane highways in Egypt

Multi-lane highways represent the majority of the total length of highway network in Egypt. The road geometry and the percentage of heavy vehicles (HVs) are considered the most important factors affecting the level of service (LOS) and capacity for any roadway. Therefore, this paper aims to explore the relationship between the road geometric characteristics and HV, and the LOS and capacity by two ways. First is the statistical modeling and second is the modeling by artificial neural networks (ANNs). In this research, the traffic and road geometric data are collected from mid-tangent points at 45 different sites that are located in desert and agricultural highways. The results showed that the ANN modeling gives the best models for estimating LOS and capacity. Also, it is better for analysis to separate the desert and agricultural sites. In addition, the most influential variables on LOS and capacity in desert sites are HV and lane width (LW), respectively, while in agricultural sites are LW and existence of side access (SA), respectively. These results are so important for road authorities in Egypt as they can determine LOS and capacity for different tangent sections and improve the traffic performance of them in the future

Derivation of travel demand forecasting models for low population areas: the case of Port Said Governorate, North East Egypt

In the last decades, there has been substantial development in modeling techniques of travel demand estimation. For low population areas the external trip estimation is important but usually neglected in travel demand modeling process. In Egypt, the researches in this field are scarce due to lack of data. Accordingly, this paper aims to identify and estimate the main variables that affect the travel demand in low population areas, and to develop models to predict them. The study focused on the Port Said Governorate in North East Egypt. A special questionnaire had been prepared in 2010 depending on interviews of passengers at basic taxi terminals in Port Said. And 2211 filled questionnaires were offering for research. To analyze the data, two modeling procedures were used. One is the multiple linear regression and the other is the generalized linear modeling (GLM) applying normal distributions. It is found that GLM procedure offers more suitable and accurate approach than the linear regression for developing number of trips. The final demand models have statistics within the acceptable regions and, also, they are conceptually reasonable. These results are so important for Egyptian highway authorities to improve the efficiency of highway transportation system in Egypt

Derivation of the downward velocity of the flaring region of 26 June 1999

In the present study, three methods have been used to compute the downward velocity of the flare plasma of the solar flare on June 26, 1999. The first method is used to determine the plasma velocity of the studied flare from the Hα line asymmetries by using the asymmetry method developed by Edward (2009). The second one is to obtain the downward velocity of the flare plasma from the far wings of the excess profiles by the bisector method. This method was employed by; for example, Ichimoto and Kurokawa (1984), Falchi et al. (1992), and Ding et al. (1995). The third method is the modified cloud model which is described by Liu and Ding (2001a,b), Gu and Ding (2002), Semeida et al. (2004) and Berlicki (2007)

Statistical Study of Confined Filament/Prominence Eruptions during Solar Cycle 23

International audienceFilament/prominence eruptions can have a significant impact on Earth's upper atmosphere and space environment, and are the primary drivers of what is now called space weather. To distinguish the different types of filament eruptions we statistically examine them during the 23rd Solar cycle. In this study we use 159 filament eruptions using the List of interplanetary (IP) Shocks Observed during Solar Cycle 23 (May 1996-January 2008) and their Source Information Environmental Satellites (GOES) X-ray plots (see Gopalswamy et al. [ 15 ]). It is found that 69% of the filament eruptions are confined eruptions, while 31% are ejective eruptions. Confined eruptions are 110 and 34 events (21%). They are due to active filaments and 76 events (48%) are due to disappearing filaments. The occurrences of active and disappearing filaments during the increasing phase of solar cycle 23 is found to be 80% while in the decreasing phase they are 13%. We have found that the dominant X-ray flare energy of confined eruptions is that of C class. The most common filaments field extent is located between 5 and 15 degrees. The most common flare duration is between 16 and 40 minutes