A study of road traffic accidents in Saudi Arabia

In Saudi Arabia, with its rapid expansion of road construction and increase in the number of vehicles, road traffic accidents are becoming a serious public health problem. The object of this paper is to discuss the magnitude of this problem and to compare the situation with that of other rich, developing countries which have also had rapid expansion of road construction and increase in the number of vehicles. It appears that Saudi Arabia has lower accident rates but higher casualty and fatality rates than Kuwait.     

A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction

The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various systems. Such as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects worldwide. Traffic congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the road. To address this overwhelming problem, in this article, a cloud-based intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy approach. The aim of the study is to reduce the delay in the queues, the vehicles experience at different road junctions across the city. The proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things (IoT) sensors across the road. After due preprocessing over the cloud server, the proposed approach makes use of this data by incorporating the neuro-fuzzy engine. Consequently, it possesses a high level of accuracy by means of intelligent decision making with minimum error rate. Simulation results reveal the accuracy of the proposed model as 98.72% during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%, 95.84%, 97.56% and 98.03%, respectively. As far as the training phase analysis is concerned, the proposed scheme exhibits 99.214% accuracy. The proposed prediction model is a potential contribution towards smart cities environment.     

Prediction of Traffic Volume of Motor Vehicles Based on Mobile Phone Signaling Technology

Urban traffic volume detection is an essential part of traffic planning in terms of urban planning in China. To improve the statistics efficiency of road traffic volume, this thesis proposes a method for predicting motor vehicle traffic volume on urban roads in small and medium-sized cities during the traffic peak hour by using mobile signal technology. The method is verified through simulation experiments, and the limitations and the improvement methods are discussed. This research can be divided into three parts: Firstly, the traffic patterns of small and medium-sized cities are obtained through a questionnaire survey. A total of 19745 residents were surveyed in Luohe, a medium-sized city in China and five travel modes of local people were obtained. Secondly, after the characteristics of residents’ rest and working time are investigated, a method is proposed in this study for the distribution of urban residential and working places based on mobile phone signaling technology. Finally, methods for predicting traffic volume of these travel modes are proposed after the characteristics of these travel modes and methods for the distribution of urban residential and working places are analyzed. Based on the actual traffic volume data observed at offline intersections, the project team takes Luohe city as the research object and it verifies the accuracy of the prediction method by comparing the prediction data. The prediction simulation results of traffic volume show that the average error rate of traffic volume is unstable. The error rate ranges from 10% to 30%. In this thesis, simulation experiments and field investigations are adopted to analyze why these errors occur.     

The economic feasibility of utilizing small-scale solar PV systems in the residential sector based on Saudi regulations

Clean Technologies and Environmental Policy - Recently, the government of Saudi Arabia has adopted the regulations of the small-scale solar PV systems. These regulations allow consumers in the...     

Potential advantages in combining smart and green infrastructure over silo approaches for future cities

Cities are incorporating smart and green infrastructure components in their urban design policies, adapting existing and new infrastructure systems to integrate technological advances to mitigate extreme weather due to climate change. Research has illustrated that smart green infrastructure (SGI) provides not only climate change resilience but also many health and wellbeing benefits that improve the quality of life of citizens. With the growing demand for smart technology, a series of problems and challenges, including governance, privacy, and security, must be addressed. This paper explores the potential to transition from grey, green, or smart silos to work with nature-based solutions and smart technology to help change cities to achieve considerable environmental and socio-economic benefits. The concepts of grey, green, and smart infrastructure are presented, and the needs, benefits, and applications are investigated. Moreover, the advantages of using integrated smart, green nature-based solutions are discussed. A comprehensive literature review is undertaken with keyword searches, including journal papers, stakeholder and case study reports, and local authority action plans. The methodology adopts multimethod qualitative information review, including literature, case studies, expert interviews, and documentary analysis. Published data and information are analysed to capture the key concepts in implementing SGI systems, such as storm-water control, flood and coastal defense, urban waste management, transportation, recreation, and asset management. The paper investigates the elimination of silo approaches and the alleviation of the destructions caused by extreme weather events using these interdependent SGI systems supported by novel data-driven platforms to provide nature-based solutions to boost the health and wellbeing of the residents.     

城市道路交通噪声控制

根据城市日益加剧的交通噪声污染,科学分析交通噪声污染主要因素,实施多项交通噪声控制措施,并取得了一定降噪效果.     

Deep Learning and Holt-Trend Algorithms for Predicting Covid-19 Pandemic

The Covid-19 epidemic poses a serious public health threat to the world, where people with little or no pre-existing human immunity can be more vulnerable to its effects. Thus, developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of lives. In this study, a deep learning algorithm and a Holt-trend model are proposed to predict the coronavirus. The Long-Short Term Memory (LSTM) and Holt-trend algorithms were applied to predict confirmed numbers and death cases. The real time data used has been collected from the World Health Organization (WHO). In the proposed research, we have considered three countries to test the proposed model, namely Saudi Arabia, Spain and Italy. The results suggest that the LSTM models show better performance in predicting the cases of coronavirus patients. Standard measure performance Mean squared Error (MSE), Root Mean Squared Error (RMSE), Mean error and correlation are employed to estimate the results of the proposed models. The empirical results of the LSTM, using the correlation metrics, are 99.94%, 99.94% and 99.91% in predicting the number of confirmed cases in the three countries. As far as the results of the LSTM model in predicting the number of death of Covid-19, they are 99.86%, 98.876% and 99.16% with respect to Saudi Arabia, Italy and Spain respectively. Similarly, the experiment’s results of the Holt-Trend model in predicting the number of confirmed cases of Covid-19, using the correlation metrics, are 99.06%, 99.96% and 99.94%, whereas the results of the Holt-Trend model in predicting the number of death cases are 99.80%, 99.96% and 99.94% with respect to the Saudi Arabia, Italy and Spain respectively. The empirical results indicate the efficient performance of the presented model in predicting the number of confirmed and death cases of Covid-19 in these countries. Such findings provide better insights regarding the future of Covid-19 this pandemic in general. The results were obtained by applying time series models, which need to be considered for the sake of saving the lives of many people.     

Indoor radon survey in dwellings of nine cities in the Eastern and the Western provinces of Saudi Arabia

The results of a first phase of an indoor radon survey in a total of 1610 dwellings distributed in nine cities of the Eastern and the Western provinces of Saudi Arabia are presented. The objective of this radon survey was to obtain representative indoor radon data for seven cities in the Eastern province. Khafji, Hafr Al-Batin, Abqaiq, Qatif, Al-Ahsa, Dammam and Khobar and to compare this with two cities in the Western province, Madina and Taif. So far, detailed radon data is not available for Saudi Arabia: therefore, this radon survey provides a base line for Saudi Arabia in the Radon World Atlas. On average, 200 indoor radon dosemeters were distributed in each city and placed for a period of one year starting from May 2001 to May 2002. The total number of collected dosemeters was 847. A total of 724 houses and 98 schools were covered in this survey. The results of the survey in the cities showed that the overall minimum, maximum and average radon concentrations were 1, 137 and 22 Bq m(-3), respectively. Geometric mean and geometric standard deviations of the radon distribution were found to be 18 and 1.92, respectively. In one of the dwellings in Qatif city, radon concentration, measured by a passive system and then confirmed by an active system, was found to be 535 +/- 23 and 523 +/- 22 Bq m(-3), respectively. The result of a radon survey in 98 schools showed that the minimum, maximum and average radon concentrations were 1, 70 and 19 Bq m(-3), respectively. The average radon concentration for each city was also determined. The lowest average radon concentration (8 Bq m(-3)) was found in Al-Ahsa while the highest average concentration (40 Bq m(-3)) was found in Khafji.     

安徽省城乡居民交通事故死亡状况及对策

安徽省 1 990～ 1 992年城乡居民死亡原因抽样调查结果显示 ,全省目前的交通事故死亡率年平均为 9.2 0 /1 0万 ,每年因交通事故死亡约 5 0 0 0人 ,城市的交通事故死亡率为 1 0 .0 5 /1 0万 ,居意外死因的首位 ;农村的交通事故死亡率为 9.0 3 /1 0万 ,居意外死因的第 3位 ,从死亡水平和死者的性别、年龄特征来看 ,城乡的死亡状况已十分相似。总体来看 ,男性交通事故死亡率明显高于女性 ;分年龄组看 ,随着年龄的增加 ,死亡率逐渐上升 ,但 2 0～ 60岁的死亡人数占全部交通事故死亡的 66.0 4%。结果表明 ,安徽省目前城乡居民的交通事故死亡状况是很严重的 ,大力加强全省交通安全管理。提高全民的交通安全意识是迫切需要解决的问题。     

Malicious Traffic Detection in IoT and Local Networks Using Stacked Ensemble Classifier

Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity. Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic. Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage. Currently, many automated systems can detect malicious activity, however, the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems. The present study focuses on the detection of malicious traffic with high accuracy using machine learning techniques. The proposed approach used two datasets UNSW-NB15 and IoTID20 which contain the data for IoT-based traffic and local network traffic, respectively. Both datasets were combined to increase the capability of the proposed approach in detecting malicious traffic from local and IoT networks, with high accuracy. Horizontally merging both datasets requires an equal number of features which was achieved by reducing feature count to 30 for each dataset by leveraging principal component analysis (PCA). The proposed model incorporates stacked ensemble model extra boosting forest (EBF) which is a combination of tree-based models such as extra tree classifier, gradient boosting classifier, and random forest using a stacked ensemble approach. Empirical results show that EBF performed significantly better and achieved the highest accuracy score of 0.985 and 0.984 on the multi-domain dataset for two and four classes, respectively.     

Indoor radon concentration measurement in the dwellings of Al-Jauf region of Saudi Arabia

Indoor radon concentration measurement in the dwellings of Al-Jauf region of Saudi Arabia was carried out using passive radon dosemeters. The objective of this radon survey was to obtain representative indoor radon data of Al-Jauf region. The study is a continuation of radon survey in main cities of Saudi Arabia which constitutes a baseline for Saudi Arabia in the Radon World Atlas. A total of 318 passive radon dosemeters were distributed randomly in the region and placed for a period of 1 y starting from April 2004 to April 2005. The results of indoor radon concentration measurement in 136 dwellings distributed in Al-Jauf region are presented. The remaining dosemeters were lost in the dwellings or mishandled. The results showed that the average, minimum, maximum radon concentrations and standard deviation were 35, 7, 168 and 30 Bq m(-3), respectively. Geometric mean and geometric standard deviation of the radon distribution were found to be 28 and 1.83, respectively.     

Traffic simulation and forecasting system in Beijing

Transport system is a time-varying, huge and complex system. In order to have the traffic management department make pre-appropriate traffic management measures to adjust the traffic management control program, and release travel information to travelers, to provide optimal path options to ensure that the transport system operates efficiently and safely, we have to monitor the changing of the state of road traffic and to accurately evaluate the state of the traffic, then to pre-predict the future state of traffic. This paper represents the construction of the road traffic flow simulation including the logical structure and the physical structure, meanwhile introduces the system functions of forecasting system in Beijing.     

Integrating Blockchain Technology into Healthcare Through an Intelligent Computing Technique

The blockchain technology plays a significant role in the present era of information technology. In the last few years, this technology has been used effectively in several domains. It has already made significant differences in human life, as well as is intended to have noticeable impact in many other domains in the forthcoming years. The rapid growth in blockchain technology has created numerous new possibilities for use, especially for healthcare applications. The digital healthcare services require highly effective security methodologies that can integrate data security with the available management strategies. To test and understand this goal of security management in Saudi Arabian perspective, the authors performed a numerical analysis and simulation through a multi criteria decision making approach in this study. The authors adopted the fuzzy Analytical Hierarchy Process (AHP) for evaluating the effectiveness and then applied the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique to simulate the validation of results. For eliciting highly corroborative and conclusive results, the study referred to a real time project of diabetes patients’ management application of Kingdom of Saudi Arabia (KSA). The results discussed in this paper are scientifically proven and validated through various analysis approaches. Hence the present study can be a credible basis for other similar endeavours being undertaken in the domain of blockchain research.     

Fuzzy Based Decision Making Approach for Evaluating the Severity of COVID-19 Pandemic in Cities of Kingdom of Saudi Arabia

The World Health Organization declared COVID-19 a pandemic on March 11, 2020 stating that it is a worldwide danger and requires imminent preventive strategies to minimise the loss of lives. COVID-19 has now affected millions across 211 countries in the world and the numbers continue to rise. The information discharged by the WHO till June 15, 2020 reports 8,063,990 cases of COVID-19. As the world thinks about the lethal malady for which there is yet no immunization or a predefined course of drug, the nations are relentlessly working at the most ideal preventive systems to contain the infection. The Kingdom of Saudi Arabia (KSA) is additionally combating with the COVID-19 danger as the cases announced till June 15, 2020 reached the count of 132,048 with 1,011 deaths. According to the report released by the KSA on June 14, 2020, more than 4,000 cases of COVID-19 pandemic had been registered in the country. Tending to the impending requirement for successful preventive instruments to stem the fatalities caused by the disease, our examination expects to assess the severity of COVID-19 pandemic in cities of KSA. In addition, computational model for evaluating the severity of COVID-19 with the perspective of social influence factor is necessary for controlling the disease. Furthermore, a quantitative evaluation of severity associated with specific regions and cities of KSA would be a more effective reference for the healthcare sector in Saudi Arabia. Further, this paper has taken the Fuzzy Analytic Hierarchy Process (AHP) technique for quantitatively assessing the severity of COVID-19 pandemic in cities of KSA. The discoveries and the proposed structure would be a practical, expeditious and exceptionally precise evaluation system for assessing the severity of the pandemic in the cities of KSA. Hence these urban zones clearly emerge as the COVID-19 hotspots. The cities require suggestive measures of health organizations that must be introduced on a war footing basis to counter the pandemic. The analysis tabulated in our study will assist in mapping the rules and building a systematic structure that is immediate need in the cities with high severity levels due to the pandemic.     

Traffic simulation based ship collision probability modeling

Maritime traffic poses various risks in terms of human, environmental and economic loss. In a risk analysis of ship collisions, it is important to get a reasonable estimate for the probability of such accidents and the consequences they lead to. In this paper, a method is proposed to assess the probability of vessels colliding with each other. The method is capable of determining the expected number of accidents, the locations where and the time when they are most likely to occur, while providing input for models concerned with the expected consequences. At the basis of the collision detection algorithm lays an extensive time domain micro-simulation of vessel traffic in the given area. The Monte Carlo simulation technique is applied to obtain a meaningful prediction of the relevant factors of the collision events. Data obtained through the Automatic Identification System is analyzed in detail to obtain realistic input data for the traffic simulation: traffic routes, the number of vessels on each route, the ship departure times, main dimensions and sailing speed. The results obtained by the proposed method for the studied case of the Gulf of Finland are presented, showing reasonable agreement with registered accident and near-miss data.     

Condition of vehicles in Saudi Arabia

Saudi Arabia has experienced an enormous growth in its motorization rate since 1970, and there has been a resultant increase of nearly 600 percent in traffic fatalities. The fatality rate (per 100,000 vehicles) has been found to be at least three times higher than that of most developed countries. One of the factors which may be contributing to the serious accident problem in Saudi Arabia is the poor condition of some of the vehicles. The main goal of this study was to investigate the socioeconomic determinants of vehicle condition, using data gathered in Saudi Arabia. Detailed information on the condition of vehicles and the characteristics of drivers were collected by roadside surveys in the Eastern Province. The results from 508 surveys indicated that: 1. The condition of vehicles was three to four times as bad as in some other states. 2. There was a strong relationship between driver characteristics and vehicle condition. Drivers who were poor, uneducated, and drove without a license were more likely to have unsafe vehicles than their counterparts who were rich, educated and had driver's licences. 3. On the average trucks and vehicles used for public transportation and the movement of goods, and vehicles owned by companies and the government, were in worse condition than other vehicles. 4. There were significant differences among certain makes of vehicles.     

安徽省城乡居民交通事故死亡状况及对策

安徽省１９９０～１９９２年城乡居民死亡原因抽样调查结果显示，全省目前的交通事故死亡率年平均为９．２０／１０万，每年因交通事故死亡约５０００人，城市的交通事故死亡率为１０．０５／１０万，居意外死因的首位；农村的交通事故死亡率为９．０３／１０万。居意外死因的第３位，从死亡水平和死者的性别、年龄特征来看，城乡的死亡状况已十分相似。总体来看，男性交通事故死亡率明显高于女性；分年龄组看，随着年龄的增加，死亡率逐渐上升，但２０～６０岁的死亡人数占全部交通事故死亡的６６．０４％。结果表明，安徽省目前城乡居民的交通事故死亡状况是很严重的，大力加强全省交通安全管理。提高全民的交通安全意识是迫切需要解决的问题。     

Simulation of safety: A review of the state of the art in road safety simulation modelling

Recent decades have seen considerable growth in computer capabilities, data collection technology and communication mediums. This growth has had considerable impact on our ability to replicate driver behaviour and understand the processes involved in failures in the traffic system. From time to time it is necessary to assess the level of development as a basis of determining how far we have come. This paper sets out to assess the state of the art in the use of computer models to simulate and assess the level of safety in existing and future traffic systems. It reviews developments in the area of road safety simulation models. In particular, it reviews computer models of driver and vehicle behaviour within a road context. It focuses on stochastic numerical models of traffic behaviour and how reliable these are in estimating levels of safety on the traffic network. Models of this type are commonly used in the assessment of traffic systems for capacity, delay and general performance. Adding safety to this assessment regime may allow more comprehensive assessment of future traffic systems. To date the models have focused primarily on vehicular traffic that is, cars and heavy vehicles. It has been shown that these models have potential in measuring the level of conflict on parts of the network and the measure of conflict correlated well with crash statistics. Interest in the prediction of crashes and crash severity is growing and new models are focusing on the continuum of general traffic conditions, conflict, severe conflict, crash and severe crashes. The paper also explores the general data types used to develop, calibrate and validate these models. Recent technological development in in-vehicle data collection, driver simulators and machine learning offers considerable potential for improving the behavioural base, rigour and application of road safety simulation models. The paper closes with some indication of areas of future development.     

Cardiovascular disease research in Saudi Arabia: a bibliometric analysis

In the previous three decades, in Saudi Arabia, prevalence of cardiovascular diseases (CVD) has increased and the government has invested significantly in education, healthcare, and research. This study examined the research productivity trends and characterized the types and focus of the all CVD research studies from Saudi Arabia. Data were extracted from studies published up until December 2015 and indexed in the PubMed database. Study eligibility criteria included: (1) sample selected within Saudi Arabia, and (2) CVD or a risk factor for CVD as an outcome, or (3) patients with CVD as study participants. Bibliometric data and study characteristics were extracted from each study; examples include authorship (number, gender, affiliation), journal, publication year, study location, research design, sample size, sample type (general or patient), sample composition (male or female), and sampling strategy (random or non-random). Analysis included 295 studies that pertained to 19 types of CVD; the most common were coronary artery disease (18%), hypertension (16%), stroke (14%), peripheral artery disease (11%), and congenital heart disease (10%). In the past 30 years, the overall productivity, use of a hypothesis-testing design (i.e. case–control, cohort, or trial), international collaborations, and funding increased incrementally. The experimental design constituted only 3% of all studies and less than 10% of the hypothesis-testing design studies. The scientific literature from Saudi Arabia addressed many of the CVD types. However, there were very few experimental studies conducted to date. Funding agencies should consider patronizing more studies with a hypothesis-testing design.