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All Journal Journal of ICT Research and Applications Journal of Robotics and Control (JRC)
Salawudeen Ahmed Tijani
University of Jos
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Optimized Feedback-based Traffic Congestion Pricing and Control for Improved Return on Investment (ROI) Obari Johnson; Sikiru Humble Tajudeen; Muhammed Bashir Mu’azu; Salawudeen Ahmed Tijani
Journal of Robotics and Control (JRC) Vol 2, No 3 (2021): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.2365

Abstract

Traffic congestion is a serious problem in any developing society. One of the approaches used in addressing this problem is congestion pricing. In this paper, the effects of social behavior on congestion pricing and control were considered and a scenario of a 1 x 2 traffic tolling system is used. Also, this work considers the rate of return on investment (RoI) of toll facilities in order to justify the worthiness of the design to investors. In earlier works on feedback-based traffic congestion pricing, the traffic parameters in the logit expression were selected arbitrarily and this made it difficult for traffic designers to arrive at optimum parameters within a reasonable amount of time. In order to address this challenge, the traffic parameter problem is formulated into a traffic congestion control optimization problem whose goal is to maximize the congestion price. The constraints are boundaries for the traffic parameters and the investment boundary conditions. The fitness of the formulated optimization problem was determined using genetic algorithm (GA). A number of simulations were performed by considering different multiplication factors and results were obtained for each multiplication factor (m.f). The simulation results justify the exactness of the formulated optimization problem and the superior performance of this work over the one that involves manually selection of traffic parameters.
Development of Hybrid Automatic Segmentation Technique of a Single Leaf from Overlapping Leaves Image Jibrin Bala; Habeeb Bello Salau; Ime Jarlath Umoh; Adeiza James Onumanyi; Salawudeen Ahmed Tijani; Basira Yahaya
Journal of ICT Research and Applications Vol. 14 No. 3 (2021)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2021.14.3.4

Abstract

The segmentation of a single leaf from an image with overlapping leaves is an important step towards the realization of effective precision agricultural systems. A popular approach used for this segmentation task is the hybridization of the Chan-Vese model and the Sobel operator CV-SO. This hybridized approach is popular because of its simplicity and effectiveness in segmenting a single leaf of interest from a complex background of overlapping leaves. However, the manual threshold and parameter tuning procedure of the CV-SO algorithm often degrades its detection performance. In this paper, we address this problem by introducing a dynamic iterative model to determine the optimal parameters for the CV-SO algorithm, which we dubbed the Dynamic CV-SO (DCV-SO) algorithm. This is a new hybrid automatic segmentation technique that attempts to improve the detection performance of the original hybrid CV-SO algorithm by reducing its mean error rate. The results obtained via simulation indicate that the proposed method yielded a 1.23% reduction in the mean error rate against the original CV-SO method.
Co-Authors Adeiza James Onumanyi Basira Yahaya Habeeb Bello Salau Ime Jarlath Umoh Jibrin Bala Muhammed Bashir Mu’azu Obari Johnson Sikiru Humble Tajudeen