Real-Time Fog Warning System for the Abu Dhabi Emirate (UAE)

Ali, Oualid Walid Ben; Al-Harthei, Hussain; Garib, Atef M.

doi:10.12720/jtle.1.2.213-217

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Fog in the UAE has a large impact on road and air travel (e.g., de Villiers and van Heerden 2007). In major cities like Dubai, Abu Dhabi, Al Ain, and Sharjah, fog creates issues more than dust storms (e.g., Bartok et al 2012;Ali et al 2013), and its frequency has also increased in recent years (Aldababseh and Temimi 2017). Within the present context, understanding the evolution and physical characteristics of fog events over the UAE is crucial.…”

Section: Introductionmentioning

confidence: 98%

On the Investigation of the Typology of Fog Events in an Arid Environment and the Link with Climate Patterns

Mohan

Ajayamohan

Temimi

et al. 2020

Monthly Weather Review

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Abstract The central aim of this work is to investigate the characteristics of fog events over the United Arab Emirates (UAE) and identify the underlying physical processes responsible for fog initiation and dissipation. To achieve this, hourly meteorological measurements at eight airport stations, along with ERA5 reanalysis data (1995–2018), are utilized. The analysis indicates the dominance of radiation fog (RAD) as, on average, 70% of the observed events fall under this category. Fog in the UAE typically forms between 2000 and 0200 local time (LT) and dissipates between 0600 and 0900 LT. During a typical dense fog event recorded during 22–23 December 2017, cooling and moistening tendencies of up to 1.2 K h−1 and 0.7 g kg−1 h−1 are observed ~5–6 h before fog onset. In the vertical, a dry and warm layer above 750 hPa gradually descends from above 500 hPa to promote the development of fog. Similar conclusions are reached when analyzing composites of fog events. Further, the variability of fog occurrence associated with El Niño–Southern Oscillation (ENSO) patterns is explored. It is concluded that the El Niño (warm) and La Niña (cold) phases exhibit very different spatial characteristics with respect to surface meteorological variables. In particular, during El Niño events, the near-surface atmosphere is cooler and moister compared to La Niña events, favoring RAD fog formation over the UAE. Besides, fog events during El Niño years tend to last longer compared to La Niña years due to an earlier onset.

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Section: Introductionmentioning

confidence: 98%

On the Investigation of the Typology of Fog Events in an Arid Environment and the Link with Climate Patterns

Mohan

Ajayamohan

Temimi

et al. 2020

Monthly Weather Review

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“…Another real-time fog and traffic detection system was proposed in Abu Dhabi (Ali et al, 2013). It has three main components: a fog sensing component, a fog density data collection and analysis component, and a traffic sensing component.…”

Section: Evaluation Of Existing Visibility and Traffic Detection Systemsmentioning

confidence: 99%

Assessing the impact of reduced visibility on traffic crash risk using microscopic data and surrogate safety measures

Peng

Abdel-Aty

Shi

et al. 2017

Transportation Research Part C: Emerging Technologies

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Due to the difficulty of obtaining accurate real-time visibility and vehicle based traffic data at the same time, there are only few research studies that addressed the impact of reduced visibility on traffic crash risk. This research was conducted based on a new visibility detection system by mounting visibility sensor arrays combined with adaptive learning modules to provide more accurate visibility detections. The vehicle-based detector, Wavetronix SmartSensor HD, was installed at the same place to collect traffic data. Reduced visibility due to fog were selected and analyzed by comparing them with clear cases to identify the differences based on several surrogate measures of safety under different visibility classes. Moreover, vehicles were divided into different types and the vehicles in different lanes were compared in order to identify whether the impact of reduced visibility due to fog on traffic crash risk varies depending on vehicle types and lanes. Log-Inverse Gaussian regression modeling was then applied to explore the relationship between time to collision and visibility together with other traffic parameters. Based on the accurate visibility and traffic data collected by the new visibility and traffic detection system, it was concluded that reduced visibility would significantly increase the traffic crash risk especially rear-end crashes and the impact on crash risk was different for different vehicle types and for different lanes. The results would be helpful to understand the change in traffic crash risk and crash contributing factors under fog conditions. We suggest implementing the algorithms in realtime and augmenting it with ITS measures such as VSL and DMS to reduce crash risk.

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“…This paper attempts to improve the accura- Network, fuzzy logic, smart image processing, etc. [7]- [16]. This paper extends the work in [1] and adopts the idea of the work in [6] to develop an intelligent engine that improves the accuracy of the position and velocity prediction and incorporate the engine into the existing under fog driving assistance system (No_Collision Apps.…”

Section: Introductionmentioning

confidence: 99%

Improving the Accuracy of Under-Fog Driving Assistance System

Kerim¹

2020

JSIP

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Driving in fog condition is dangerous. Fog causes poor visibility on roads leading to road traffic accident (RTA). RTA in Albaha is common because of its rough terrain, in addition to the climate that is mainly rainy and foggy. The rain season in Albaha region begins in October to February characterized by rainfall and fog. Many studies have reported the adverse effects of the rain on RTA which results in an increased rate of crashes. On the other hand, Albaha region is not supported by a proper intelligent transportation system and infrastructure. Thus, a Driver Assistance System (DAS) that requires minimum infrastructure is needed. A DAS under fog called No_Collision has been developed by a researcher in Albaha University. This paper discusses an implementation of adaptive Kalman Filter by utilizing Fuzzy logic system with the aim to improve the accuracy of position and velocity prediction of the No_Collision system. The experiment results show a promising adaptive system that reduces the error percentage of the prediction up to 56.58%.

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Real-Time Fog Warning System for the Abu Dhabi Emirate (UAE)

Cited by 5 publications

References 5 publications

On the Investigation of the Typology of Fog Events in an Arid Environment and the Link with Climate Patterns

On the Investigation of the Typology of Fog Events in an Arid Environment and the Link with Climate Patterns

Assessing the impact of reduced visibility on traffic crash risk using microscopic data and surrogate safety measures

Improving the Accuracy of Under-Fog Driving Assistance System

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