Evaluating the impact of COVID-19 on traffic congestion and safety skills using structural equation modeling (SEM) and Auto-Regressive Integrated Moving Average (ARIMA).

The current work presented a comparative analysis of traffic demand and safety skills before and after control measures during the COVID-19 epidemic, acquired time-series change data curves, and constructed a prediction model after determining the trend of traffic demand over time. From a data analysis perspective, the paper draws some interesting conclusions about long span, coarse sampling studies. In terms of the study population, the paper did focus on the specificity of the global epidemic. Kuwait was selected as a case study. Traffic demand analysis was conducted using a Structural Equation Model (SEM), Auto-Regressive Integrated Moving Average (ARIMA), and safety skills questionnaire along with flow charts and demographic variables. These methods were utilized to study the impact of COVID-19 on traffic congestion and safety skills as well as to forecast the future traffic volumes. Results showed that traffic congestion had a significant reduction during COVID-19 as a result of the preventive safety measures taken to control the spread of the virus. Such reduced traffic volume was associated with a decrease in traffic violations and an increase in the safety skills and PM skills of drivers.

The effect of traffic at speed bumps in residential areas on noise and air pollution.

Although transportation systems are an increasing necessity in today's connected world, the over-reliance on internal combustion engine vehicles has caused an increase in air and noise pollution. Air and noise pollutions are among the negative environmental factors that contribute to disease occurrence due to their negative health consequences. Literature has showed that air and noise pollutions were responsible for thousands of premature deaths in Europe. This has pushed scientists to search for models to calculate the effect of traffic on air and noise pollution, to help predict future scenarios, and to work on ways to mitigate that increase in pollution. In this paper, a statistical model is done by using data measured for 25 speed bump locations in Kuwait, which included traffic flow data in the form of vehicle count and classification, as well as noise level measurements taken through an Amprobe SM20 sound meter, in addition to air pollutant data obtained from the Environment Public Authority (EPA) in Kuwait. The results of the multivariate linear regression model showed that high traffic counts resulted in significantly higher noise levels, reaching more than 70 decibels in certain locations, which is considered unhealthy for extended time periods. The model also showed that sulfur dioxide levels were impacted by both light and heavy vehicles, while particulate matter less than 10 µm was affected mainly by heavy vehicles. An online survey on speed bumps was completed by 803 participants to understand the behavior of people at speed bumps in Kuwait and understand if age and gender might be a predictor of how people would behave, and this was done by doing Pearson's chi-squared correlation tests on the results obtained from the survey.

Customer perception and acceptance of autonomous delivery vehicles in the State of Kuwait during COVID-19.

COVID-19 is one of the most important dilemmas that took place during the last few years. Logisticians worked hard to present a new mechanism called Autonomous Delivery Vehicles (ADVs) by which they afford help making life easier for people during pandemic while trying to reduce pollution on road as well. This work mainly aimed to explore Unified Theory of Acceptance and Use of Technology (UTAUT2) and the convenience of users - according to gender - to the idea of using Autonomous Delivery Vehicles (ADVs). A survey-based method was applied and presented. It was distributed online where a total of 450 participants had taken part to express their ideas. Structural Equation Modeling (SEM) was used to analyze the data and the results were discussed thoroughly. The model was conducted according to nine hypotheses. Results showed that all of them were supported except hypothesis 7, which is the trust in technology that negatively influenced the perceived risk leading to rejecting the hypothesis that supposes the validity of H7. It was concluded that the perceived risk and behavioral intention relationship were only significant for males while the perceived risk and trust in technology relationship were only significant for females.

COVID-19 environmental and operational impact for public transport in Kuwait.

The corona virus disease pandemic (COVID-19) is one of the recent issues that spread in the world, which disrupted life, impacted the economy, and led to heavy losses, whether for government sectors or private companies. This paper focuses on the Kuwait public transport company KPTC and Kuwait Airways' experience during the pandemic, since they incurred major losses due to the decline of their users. Public transport is a place to catch COVID-19, as it is subjected to the use of a large number of passengers daily within a small closed environment. The causes that led to the spread of the virus among public transport users and develop solutions to limit its spread and preserve public transport pioneer's safety were discussed in the paper. Additionally, the environmental impact resulting from the reduction of public transportation using was also addressed. Data was obtained from the KPTC, Kuwait Airways office, the Ministry of Health (MOH) database, and the Environment Public Authority (EPA) database. A questionnaire was distributed to public transport users to determine the reasons for the decline in its user's number and their aspirations to reconsider their use and ensure their satisfaction. For airplane data, the risk of importation of COVID-19 was calculated. For KPTC data, COVID-19 impact on the emissions generated per passenger-km was computed where the emissions were estimated by MOVES. The survey responses were statically analyzed using the chi-square test on the SPSS program, and they were compared to numerical analysis results. The results showed the impact of COVID-19 on people's willingness to use public transportation which was associated with the increase in the number of buses to implement social distancing has negatively affected the environment. Thus, a comprehensive strategy solution was presented consisting of three basic approaches: providing a healthy, risk-free environment for public transportation users, achieving social distancing at a low cost to offset the losses, and ensuring a healthy environment.

Analysis of risk factors affecting traffic accident injury in United Arab Emirates (UAE).

The mortality and severe injuries due to traffic accidents in United Arab Emirates (UAE) are hastening the necessity for a study that can identify the consequential risk factors. This study was conducted by utilizing a 5740 traffic accidents police reports that occurred in Abu Dhabi, UAE between 2008 and 2013. A multinomial logit regression model was applied to determine the significant factors among the 14 potential risk factors that were used in this study. The dependent variable was the level of injury that consisted of four categories: slight injury, medium injury, severe injury, and fatal injury. The results showed that pedestrian, the unutilized seatbelt, roads that had four or more than four lanes, male casualty, 100 km/h speed limit or higher, and casualty older than 60 years were found to be the factors that can increase the probability of being involved in a fatal traffic accident. In contrast, rear-end collisions and intersections had a lower probability of causing fatal injury. Then, the eight significant predictors were included in a neural network to compare the performance of both methods and to identify the normalized importance values for the significant independent variables. The neural network had proven to be more accurate in general than the traditional regression models such as the multinomial logit model.

Environment-friendly recycled asphalt pavement design for road maintenance applications.

Potholes are one of the most common road distresses in Kuwait especially after winter season in 2018. Pavement deterioration rate significantly increases as the pavement exposes to moisture. Paving road requires using high-quality materials. This paper aims to investigate the feasibility of using recycled asphalt pavement in hot mix asphalt (HMA) design. Questionnaires and a series of laboratory tests were conducted to analyze the effects of pores on the society and test the recycled and the regular mixtures performance to choose the best option regarding it. Marshal test portrayed that the recycled mixture has a high stability and flow. According to the tensile strength ratio test requirements which set a minimum ratio of 75%, it is recommended to reduce the proportion of recycled aggregate. The Hamburg wheel tracker (HWTD) test proved that the recycled asphalt mixture has a low rutting depth for wet and dry samples compared to the general one. On the other hand, the wet recycled mixture requires a reduction by of 20% to satisfy the specifications. The physical properties for both mixtures were compared, and the voids filled with asphalt (VFA) and voids in mineral aggregate (VMA) of the recycled mixture are lower than those in the regular mixture. The recycled mixture reveals a higher efficiency in saving costs and improving mixtures used for potholes maintenance activities. All the results proved that the mixtures consisting of recycled asphalt are most preferred since they are affordable and perform reasonably well compared to mixtures made of regular asphalt. As a future work, smaller percentages of recycled aggregate should be tested to check the robustness and sustainability of the designed recycled asphalt mixture using different tests such as Hamburg wheel tracker test (wet samples) and indirect tensile strength test. Furthermore, more experiments can be run to test other mix parameters and properties such as durability.

Scenario-based preference modeling to examine the robustness of airport mega projects initiatives.

Airports are a high complex type of projects that are exposed to many disruptiveness. Proper management between airport expansion projects and airport operations is needed to ensure safety and efficiency of the project and the aviation. Scenario-based preference modeling is one of the robustness analysis techniques that describes the influence of disruptive scenarios on the project initiatives across different criteria. A scenario-based preference model was applied in this work to investigate the influence of different scenarios on Kuwait International Airport expansion. Scenario-based preference is a multi-criteria assessment, which allows the involvement of multiple stakeholders. The key target of the model was to illustrate the most and least robust initiatives, and the highest and least ranked initiatives over the scenarios. The analysis also ranked the scenarios based on their level of disruptiveness. The outcomes of this method can be used to mitigate the system and improve the project robustness by understanding each kind of risk impact. The results showed that the most and least disruptive scenarios were S3, economic crisis, and S5, compelling circumstance, and the most robust initiative was x1, completion of the main terminal building (T2). It is important to discuss the contribution and insights extracted from these analyses. For example, showing the most and least disruptive scenarios is not enough. It is important to mention what insights the readers can gain by knowing the importance of these scenarios. It is very important to highlight the significance of the results. The results showed that the most disruptive scenario was economic crisis. This indicates the fact that current Covid 19 pandemic had significantly affected the local economy, reduced country income that is based mainly on oil and consumed considerable budget on medical related activities. This had resulted in a lower expenditure on mega infrastructure projects as the airport, which caused considerable delay and interruption to major activities. Furthermore, the results showed that the most robust initiative was completion of the main terminal building (T2). This is mainly true due to the fact that the government is very keen to complete this major terminal in the airport as it will release the pressure on the old portion of the airport and increase the airport capacity. Based on these facts, it is clear that priorities in airport infrastructure activities and construction initiatives were affected considerably by the global and local circumstances and come first the global pandemic.

Risk analysis of traffic accidents' severities: An application of three data mining models.

Traffic accidents are costing the world more than a million lives yearly alongside monetary losses, especially in the Gulf Cooperation Council region. This situation raised the need to examine potential risk factors contributing to traffic accident severities. In this paper, three data mining models were applied to provide a comprehensive analysis of risk factors related to traffic accidents' severities. One of the used models was a decision tree to examine the correlations between potential risk factors. The other applied models were Bayesian Network and linear Support Vector Machine. The results confirmed that pedestrians were the most vulnerable road users compared to drivers and passengers. Male drivers and front seat-passengers were more exposed to severe or fatal injury. Similarly, elderly drivers had higher odds of having severe or fatal injuries. Road classifications and accident types were also considered significant variables related to traffic accidents' injuries. Utilizing seat belt could lessen the level of injury. Regarding the performance of the applied models, Bayesian network was more accurate in predicting the variables compared to other models.

Vehicle inspection policy and emission analysis in Kuwait.

Technical vehicle inspection centers are widespread in Kuwait. All vehicles should pass the test every 2 years if not older than 6 years or every 1 year for other vehicles. This study provided an insight into the data collected from test centers and the emission test utilized in Kuwait. Data were collected from test centers in the six Kuwaiti governorates. European standards were selected as limits for emission violations. Independent variables included the place of vehicle manufacture, vehicle's age, and odometer reading. A multinomial logit model was used to identify the significant predictors and determine the correlation between dependent and independent variables. Artificial neural network was employed to compare prediction estimates of neural network and multinomial logit. The findings showed that the place of vehicle's manufacture, vehicle's age, and odometer reading were significant regarding violating emission standards of carbon monoxide (CO). Asian vehicles, vehicles with more than 150,000 km mileage, and vehicles older than 15 years had a higher probability of failing the CO test compared to the place of manufacture. In contrast, the odometer reading was the only significant indicator for vehicles that have failed the hydrocarbons test, especially for vehicles with 150,000 km odometer reading. The findings of this study can reduce air-pollution, time, and money by targeting the most polluting vehicles; thus, more efficient test can be performed.

Bayesian hierarchical statistics for traffic safety modelling and forecasting.

Road traffic accidents (RTAs) represent a serious problem globally causing losses in many ways. Gulf Cooperation Council (GCC) countries have a high rate of RTAs compared to other high-income countries. In this study, a Bayesian hierarchical model was utilized for accident counts forecasting in Abu Dhabi, United Arab Emirates. This work will help traffic planners and decision makers to enhance road safety levels and decrease accident fatality rate. Accidents data along 5 years from 2008 to 2012 at 143 road sites in Abu Dhabi with 5,511 accidents were used. The proposed model considered a number of covariates; speed limit, type of road, number of lanes, type of area, weather, time, surface condition and seat belt usage. Five sites with the highest numbers of accidents were studied. Year 2012 was used to validate predictions. The model prediction accuracy was 72%.

Enhancing pedestrian safety, walkability and traffic flow with fuzzy logic.

The continuous congestion in the Arabian Gulf Road, located in Kuwait, contributes to air pollution in the area and causes discomfort for both drivers and pedestrians. The objective of this work is to enhance walkability and safety of pedestrians in the Gulf road while facilitating traffic flow. The study had been conducted for the road and surrounding area staring from the Society of Engineers till the British Embassy. Two methods were established to enhance walkability at each intersection. The first method used Synchro software to improve the traffic condition, lessen the delay time, and add pedestrian phase for each intersection. In the second method, fuzzy logic code was scripted using MATLAB to adjust traffic lights duration, for creating an adaptive traffic system. The best-established solution for traffic phasing at the Engineering Society intersection was mitigation scenario one, which contributed to decreasing the delay time by 63.82%, reaching only 5 min of delay. As for the British embassy intersection, the delay had been reduced by 11.82% using mitigation scenario 2. Several adjustments had been implemented in the study area that included replacing the current parking space with a wide green area, adding underground parking, and designating a particular lane for bicycles. The green area was provided with a shaded pathway using photovoltaic panels, jugging pathway, retail shops, and playing grounds to encourage walkability and reduce dependence on vehicles. A LEED-certified restaurant model had been designed that scored a gold certificate. Two additional restaurants were proposed in the area and a pond to attract more visitors.

The impact of highway base-saturation flow rate adjustment on Kuwait's transport and environmental parameters estimation.

Traditional transportation systems' management and operation mainly focused on improving traffic mobility and safety without imposing any environmental concerns. Transportation and environmental issues are interrelated and affected by the same parameters especially at signalized intersections. Additionally, traffic congestion at signalized intersections has a major contribution in the environmental problem as related to vehicle emission, fuel consumption, and delay. Therefore, signalized intersections' design and operation is an important parameter to minimize the impact on the environment. The design and operation of signalized intersections are highly dependent on the base saturation flow rate (BSFR). Highway Capacity Manual (HCM) uses a base-saturation flow rate of 1900-passenger car/h/lane for areas with a population intensity greater than or equal to 250,000 and a value of 1750-passenger car/h/lane for less populated areas. The base-saturation flow rate value in HCM is derived from a field data collected in developed countries. The adopted value in Kuwait is 1800passengercar/h/lane, which is the value that used in this analysis as a basis for comparison. Due to the difference in behavior between drivers in developed countries and their fellows in Kuwait, an adjustment was made to the base-saturation flow rate to represent Kuwait's traffic and environmental conditions. The reduction in fuel consumption and vehicles' emission after modifying the base-saturation flow rate (BSFR increased by 12.45%) was about 34% on average. Direct field measurements of the saturation flow rate were used while using the air quality mobile lab to calculate emissions' rates.

A review of traffic safety status in Abu Dhabi city, UAE (2008-2013).

Large numbers of traffic accidents were experienced on the road networks of Arab Gulf Countries including United Arab Emirates (UAE). This had resulted in enormous loss of lives and economy. This article through using Abu Dhabi city, UAE capital as a case study is aiming to understand the reasons behind such safety problem through analysing a large accidents data-set extending over the period from 2008 to 2013. The traffic accidents data-set was obtained from Abu Dhabi traffic police department records and covers wide range of accident's attributes. A wide spectrum of accident attributes was analysed in the paper including but not limited to the time of accident, accident location, type of accident, reasons behind the accident, driver characteristics, road conditions, and many other accident attributes. A specific reasoning for each of these attributes was given by authors. Furthermore, recommendations to enhance the safety levels were introduced.

Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks.

Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%-16%) more accuracy than without using clustering, while the percentage split achieves (2%-5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

Urban traffic pollution reduction for sedan cars using petrol engines by hydro-oxide gas inclusion.

UNLABELLED: Petrol cars, in particular nonhybrid cars, contribute significantly to the pollution problem as compared with other types of cars. The originality of this article falls in the direction of using hydro-oxy gas to reduce pollution from petrol car engines. Experiments were performed in city areas at low real speeds, with constant engine speeds in the average of 2500 rpm and at variable velocity ratios (first speed was 10-20 km/hr, second speed was 20-35 km/hr, and third speed was 35-50 km/hr). Results indicated that through using hydro-oxy gas, a noticeable reduction in pollution was recorded. Oxygen (O2) percentage has increased by about 2.5%, and nitric oxide (NO) level has been reduced by about 500 ppm. Carbon monoxide (CO) has decreased by about 2.2%, and also CO2 has decreased by 2.1%. It's worth mentioning that for hybrid system in cars at speeds between 10 and 50 km/hr, the emission percentage change is zero. However, hybrid cars are less abundant than petrol cars. IMPLICATIONS: The originality of this paper falls in the direction of using hydro-oxy gas to reduce pollution from petrol car engines. Experiments were performed in city areas at low real speeds, with constant engine speeds in the average of 2500 rpm and at variable velocity ratios (first speed was 10-20 km/hr, second speed was 20-35 km/hr, and third speed was 35-50 km/h).