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All Journal International Journal of Electrical and Computer Engineering
El Gonnouni, Amina
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Computer Science & IT Electrical & Electronics Engineering

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Pedestrian level of service for sidewalks in Tangier City Benhadou, Marwane; El Gonnouni, Amina; Lyhyaoui, Abdelouahid
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp1048-1057

Abstract

The pedestrian level of service (PLOS) is a measure that quantifies walkway comfort levels. PLOS defined into six categories (A, B, C, D, E, and F) each level defines the range of values, for example, a good level (best traffic condition) is defined with the letter A until reaching the worst level, F (high congestion). This article aims to define the PLOS on sidewalks considering walking conditions in Tangier City (Morocco). Sidewalks are analyzed using video recording in the urban center of Tangier City. The collected data are pedestrian flow and effective sidewalk width. Each level contains a range of values that corresponds to the pedestrian flow that defines the level of service. Clustering techniques are used to identify the threshold of each level using a self-organizing map (SOM). The results are different from those obtained with other methods because pedestrian traffic differs from country to country.
Taxi-out time prediction at Mohammed V Casablanca Airport Zbakh, Douae; El Gonnouni, Amina; Benkacem, Abderrahmane; Said Kasttet, Mohammed; Lyhyaoui, Abdelouahid
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2126-2134

Abstract

Airports are vital for global connectivity. However, the increasing volume of air travel has presented significant challenges in airport managing. Accurate predictions of taxi-out times (TXOT) offer potential to enhance airport performance, minimize delays, optimize airline schedules, and enhance customer satisfaction. This paper focuses on developing a machine learning model to forecast taxi-out times at Mohammed V Airport. Historical taxiing data from various airports will be analyzed to predict taxi-out times based on diverse runway-stand combinations and congestion levels. we used neural network (NN), support vector machines (SVM), and regression tree (RT) in order to create a real-time model that forecasts TXOT and congestion levels for different runway-stand combinations. The result showed that the NN model outperformed other forecasting models when their performances are compared using the mean absolute percentage error, root mean square error as accuracy measures.
Co-Authors Benhadou, Marwane Benkacem, Abderrahmane Lyhyaoui, Abdelouahid Said Kasttet, Mohammed Zbakh, Douae