A Vehicle Tracking System Using Greedy Forwarding Algorithms for Public Transportation in Urban Arterial

Jimoh, O. D.; Ajao, Lukman Adewale; Adeleke, Oluwafemi Oyetunde; Kolo, Stephen Sunday

doi:10.1109/access.2020.3031488

Cited by 30 publications

(8 citation statements)

References 37 publications

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“…The existing cloud computing data center energy consumption assessment methods can be roughly divided into three categories: one is to directly measure the energy consumption of the data center, the other is the energy consumption estimation method for real physical clusters, and the third is the built-in cloud computing simulation platform. Energy consumption assessment method [4][5].…”

Section: Data Center Energy Consumption Assessment Methodsmentioning

confidence: 99%

“…Due to the high cost of edge servers, the number of deployments is much smaller than the number of SDN-based access points. Therefore, this article reflects the quantitative relationship between the two in the actual scenario by setting the value range of the edge server to [1,4] and the number of APs to [10,40], as shown in Table 1. 1.0 Gbps In this paper, the transmission data rate of each AP is set to 1.0 Gbps.…”

Section: Edge Network Provides Multiple Application Service Architect...mentioning

confidence: 99%

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Data Center Resource Allocation Strategy Based on Edge Computing

2020

IJBDIT

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In recent years, Mobile Edge Computing (MEC) technology can support resource-intensive applications in edge networks. With the expansion of cloud computing service models in edge networks, it provides users with real-time services, which solves traditional cloud computing the high latency barrier when the center provides services is a new technology with very broad application prospects. This article aims to study the data center resource allocation strategy of edge computing. This paper designs an edge server experiment to make full use of the edge computing model to mine the computing power of the edge terminal in the network, perform some or all calculations at the edge terminal, process private data, and reduce the computing, transmission bandwidth load and energy consumption of the cloud computing center. The concept of edge computing was proposed only after the development of cloud computing technology for a period of time. At present, edge computing and cloud computing technology are complementary to each other. As the technical basis for building an interconnected environment of all things, this paper studies in detail the use of mobile edge computing technology to provide users with real-time application services The architecture of this service model analyzes the challenges faced by the service model and proposes an effective solution, which improves the efficiency of resource allocation by nearly 45.73%.

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Section: Data Center Energy Consumption Assessment Methodsmentioning

confidence: 99%

Section: Edge Network Provides Multiple Application Service Architect...mentioning

confidence: 99%

Data Center Resource Allocation Strategy Based on Edge Computing

2020

IJBDIT

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“…Besides this, in current years, modern technology has originated for use correctly via certified employees' employment. Consequently, the reconfiguration of public group records technology has added the idea of general management according to social and technological traits past conventional paperwork through (Jimoh et al, 2020). The survey withinside the context of this looks at changes to make certain a growth withinside the overall appearance values withinside the group withinside the following new IT and application.…”

Section: Introductionmentioning

confidence: 99%

“…The bus sequence of arrival is typically calculable of the rider's trip time at the location. The insufficiency of information seats provides challenges in determining these bus entrance periods(Jimoh et al, 2020). Various researches have explained the dilemma of managing IoT in bus transport method, while the design about optimization constraints managing 'RFID and other tech' correlated to IOT, method execution investigation, an advantage of wire, and sensor channels(Bahrami et al, 2021).…”

mentioning

confidence: 99%

Vehicle Tracking System using Technological support for Effective Management in Public Transportation

Roy¹,

Kalotra²

2022

IJRITCC

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With the modern converting public control approach, it's supposed for public establishments to provide greater green and powerful offerings at a decreased cost. In this study, the overall performance enhancement volume becomes found according to the decided general performance standards of transportation offerings statistics technology in public establishments. The institution's modern-day overall performance becomes evaluated. It has become obtrusive that public transport corporations (BRTC) wished for a statistics gadget to preserve car fleets below control. The effect of the car control utility becomes repeated that performance of the overall performance evaluation made above. It became decided where becomes obtained at minimum decreasing mentioned all types of expenses in public transportation.

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“…Transportation sustainability requires adequate planning for the traffic flow in urban centers, where bus shuttles are used as a public means to convey passengers from one location to another [1]. The passenger traffic flow is described as prevalent interactions between the travelers (passengers and vehicles) and the infrastructure (road networks, traffic control devices, or signage), for the optimal transport network in managing the traffic congestion problems [2,3]. Traffic flow management can be achieved efficiently through timetable scheduling for passenger movement.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Passenger Frequency Prediction for Transportation Sustainability using Kalman Filter Algorithm and Convolutional Neural Network

Jimoh¹,

Ajao²,

Adeleke³

et al. 2022

Preprint

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The passenger prediction flow is very significant to transportation sustainability. This is due to some chaos of traffic jams encountered by the road users during their movement to the offices, schools, or markets at earlier of the days and during closing periods. This problem is peculiar to the transportation system of the Federal University of Technology Minna, Nigeria. However, the prevailing technique of passenger flow estimation is non-parametric which depends on the fixed planning and is easily affected by noise. In this research, we proposed the development of a hybrid intelligent passenger frequency prediction model using the Auto-Regressive Integrated Moving Average (ARIMA) linear model, Convolutional Neural Network (CNN), and Kalman Filter Algorithm (KFA). The passengers’ frequency of arrival at the bus terminals is obtained and enumerated through the closed-circuit television (CCTV) and demonstrated using the Markovian Queueing Systems Model (MQSM). The ARIMA model was used for learning and prediction and compared the result with the combined techniques of using CNN-KFA. The autocorrelation coefficient functions (ACF) and partial autocorrelation coefficient functions (PACF) are used to examine the stationary data with different features. The performance of the models was analyzed and evaluated in describing the short-term passenger flow frequency at each terminal using the Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) values. The CNN-Kalman-filter model was fitted into the short-term series and the MAPE values are below 10%. The Mean Square Error (MSE) shows that the CNN-Kalman Filter model has the overall best performance with 83.33% of the time better than the ARIMA model and provides high accuracy in forecasting.

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A Vehicle Tracking System Using Greedy Forwarding Algorithms for Public Transportation in Urban Arterial

Cited by 30 publications

References 37 publications

Data Center Resource Allocation Strategy Based on Edge Computing

Data Center Resource Allocation Strategy Based on Edge Computing

Vehicle Tracking System using Technological support for Effective Management in Public Transportation

Intelligent Passenger Frequency Prediction for Transportation Sustainability using Kalman Filter Algorithm and Convolutional Neural Network

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