Accuracy improvement of tropospheric delay correction models in space geodetic data. Case study: Egypt

The tropospheric delay still remains a limiting factor to the accuracy of space based positioning techniques. The estimation of station positioning, especially height component, which is particularly important for more applications is susceptible to errors in modeling the tropospheric delay due to correlations between the station positioning and residual troposphere delay parameters. As the demand on positioning accuracy and precision has increased, it has begun a necessary of relaying on large external data sets, rather than relatively simple models for treating the tropospheric delay. This method has been possible by advances made in numerical weather models which provide accurate representations of global atmospheric conditions and by advances in computing speed which allow us to perform a large number of computations over a short period of time. The purpose of this work is to develop a new model for estimating the tropospheric delay and then assess the benefits of applying this model at various geographic atmospheric conditions of Egypt. By comparing new model with some common models such as Saastamoinen model, Hopfield model, Niell-MF, Black & Eisner-MF, UNB3 model and Vienna-MF, the results show that, new model for estimation tropospheric delay has an acceptable level of accuracy in describing the dry tropospheric delay in Egypt as it agrees closely with the numerical integration based model. The mean accuracy of this new model has been assessed to be about 9.64 mm with rms 11 mm at an elevation angle of 30° and for an elevation angle of 5°, the mean accuracy is about 83.23 mm with rms 96.42 mm for atmospheric conditions of Egypt

Multinomial Logit Utility Model for Tanta City Transportation System considering Ridesharing Mode

Recently, ridesharing has been noticed as an effective and sustainable mode of transport. In which, each passenger carries out a journey that benefits travelers and society greatly, such as reducing travel costs, reducing journey times, relieving road traffic congestions, preserving fuel and reducing air pollution. While the importance and efficiency of ridesharing, ridesharing among travelers has not been commonly utilized. This paper introduces an upgraded disaggregated utility model for a mode choice step in the four-step traffic demand model for transport of passengers in Tanta city - Gharbiya governorate. For the first time, the analysis in this paper includes ridesharing mode in addition to other three common modes; private vehicle, bus, and taxi . The aim of this research is to involve the ridesharing mode and new parameters in the mode choice analysis of the case study. This is done by including additional parameters such as travel cost and comfortable in addition to travel time. Based on surveys, the developed Multinomial logit utility models have been assessed using the McFadden pseudo R2 values. The values demonstrated high compatibility of results with real data as pseudo R2 values ranges between 0.2 and 0.4. In addition, all utility models developed for modes are found to have P-values less than 0.05 indicating the significance of the considered utility characteristics

Factors Affecting Accidents Risks among Truck Drivers In Egypt

Egypt is ranked among the countries with the highest rates of road accidents. According to the American Chamber of Commerce more than 96% of Egypt's goods are transported by trucks and due to their large volume and excessive weight, the severity and number of truck accident fatalities are much higher than other vehicles in Egypt. The present study aims at identifying truck driver's behavior and its influence on crash involvement. Due to the shortage in recording accident data and the inaccurate road accident audit, data was collected from several governorates in Egypt through questionnaire. Questionnaire forms were filled out through personal interviews with truck drivers. The total number of respondents was 643. The final analysis was made on the 615 questionnaires with complete answers. The data was analyzed and logistic regression was applied to accident related data to examine the contributing factors affecting accident occurrence of truck drivers. Results showed that fatigue in terms of driving hours (continuous and total) and lack of sleep, drug use during driving, and driver obesity are the most influencing factors on the occurrence of truck accidents in Egypt. The findings of this research highlight the important role human factors have on the risk of crash involvement amongst Egypt's truck drivers and the need to improve their work conditions

Factors Affecting Accidents Risks among Truck Drivers In Egypt

Egypt is ranked among the countries with the highest rates of road accidents. According to the American Chamber of Commerce more than 96% of Egypt's goods are transported by trucks and due to their large volume and excessive weight, the severity and number of truck accident fatalities are much higher than other vehicles in Egypt. The present study aims at identifying truck driver's behavior and its influence on crash involvement. Due to the shortage in recording accident data and the inaccurate road accident audit, data was collected from several governorates in Egypt through questionnaire. Questionnaire forms were filled out through personal interviews with truck drivers. The total number of respondents was 643. The final analysis was made on the 615 questionnaires with complete answers. The data was analyzed and logistic regression was applied to accident related data to examine the contributing factors affecting accident occurrence of truck drivers. Results showed that fatigue in terms of driving hours (continuous and total) and lack of sleep, drug use during driving, and driver obesity are the most influencing factors on the occurrence of truck accidents in Egypt. The findings of this research highlight the important role human factors have on the risk of crash involvement amongst Egypt's truck drivers and the need to improve their work conditions

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population