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.

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.

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.