ABSTRACT

Many Arab countries are starting to suffer from traffic congestion in their capitals cities, as

a consequence of continued increase in car ownership, current public transport vehicles

namely taxi, coaches and microbuses users and also lack of car parks. Tripoli, the capital

city of Libya where most of commuters prefer to use private cars,which is 88% of the city

traffic volume for doing work, study and shopping trips and one of these Arabic cities.

Government statistical data on the city for 2009 revealed that out of the total 945,203

vehicles comprised of 743,796 private cars and 47,031 units of taxis, coaches and

microbuses.In this study, traffic volume characteristics on three Tripoli's main entrances

namely, the eastern entrance covers Tajora (Al-Bive), the western entrance covers the AlGiran entrance and the southern entrance covers the Al-Gabis, Airport Road and Al-Hadaba

(Salah Al-Din) entrances were evaluated.The increase number of private cars and public

transport vehicles in the last ten years has resulted in traffic congestion. Field survey and

questionnaire study were carried out in Tripoli area in summer 2015 to explore the potential

reduction of private cars usage on road through optimal use of suitable public transportation

system. Statistical Package for Social Science (SPSS) version 19, Excel software and

logistic regression method were used to analyse these questionnaires. As a result, Tripoli

roads network users need to change and encourage usage of public transportation system as

public buses and rapid buses into the city.

Key words: Private car, public transport, parking places, entrances, traffic congestion

This paper presents a computational model for measuring diversity in terms of variety, balance and disparity. This model is informed by the Stirling’s framework for understanding diversity from social science and underpinned by semantic techniques from computer science. A case study in learning is used to illustrate the application of the model. It is driven by the desire to broaden learners’ perspectives in an increasingly diverse and inclusive society. For example, interpreting body language in a job interview may be influenced by the different background of observers. With the explosion of digital objects on social platforms, selecting the appropriate ones for learning can be challenging and time consuming. The case study uses over 2000 annotated comments from 51 YouTube videos on job interviews. Diversity indicators are produced based on the comments for each video, which in turn facilitate the ranking of the videos according to the degree of diversity in the comments for the selected domain.

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Dust storms are one of the natural hazards whose incidence has increased in the recent years over Sahara desert, Australia and northern China. Thus, it is important to know the causation, movement and radiation effects of dust storms. Satellite remote sensing is the most common method for monitoring Dust Storms but its use over sandy ground is still limited as they have similar characteristics. Many researchers have studied the detection of dust storms during daytime in a number of different regions of the world including China, Australia, America, and North Africa using a variety of satellite data. However, there have been fewer studies for detecting dust storms at night. The key elements of this study are to use a back-propagation artificial neural network with Brightness Temperature of band 31 and four Brightness Temperature Differences calculated using data from the Moderate Resolution Imaging Spectroradiometers on the Terra and Aqua satellites to develop a method for detecting dust storms during both day and night. Results have shown that the method can detect dust storms at both day and night and also over different land surfaces.