Vehicle Tracking Application Based on Real Time Traffic

Shibghatullah, Abdul Samad; Jalil, Abdurrahman; Wahab, Mohd Helmy Abd; Soon, Joseph Ng Poh; Subaramaniam, Kasthuri; Eldabi, Tillal

doi:10.18178/ijeetc.11.1.67-73

Cited by 14 publications

(3 citation statements)

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“…In contrast, the original models had a standard layer and used a sigmoid as its activation function. Each deep learning model needs to be compared to the proposed and original models to ascertain which deep learning models perform better [61] [62]. Additionally, the BI-LSTM model required more training time compared to the other models.…”

Section: Resultsmentioning

confidence: 99%

Flood Prediction using Deep Learning Models

Ali¹,

Asmai²,

Abidin³

et al. 2022

IJACSA

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Deep learning has recently appeared as one of the best reliable approaches for forecasting time series. Even though there are numerous data-driven models for flood prediction, most studies focus on prediction using a single flood variable. The creation of various data-driven models may require unfeasible computing resources when estimating multiple flood variables. Furthermore, the trends of several flood variables can only be revealed by analysing long-term historical observations, which conventional data-driven models do not adequately support. This study proposed a time series model with layer normalization and Leaky ReLU activation function in multivariable long-term short memory (LSTM), bidirectional long-term short memory (BI-LSTM) and deep recurrent neural network (DRNN). The proposed models were trained and evaluated by using the sensory historical data of river water level and rainfall in the east coast state of Malaysia. It were then, compared to the other six deep learning models. In terms of prediction accuracy, the experimental results also demonstrated that the deep recurrent neural network model with layer normalization and Leaky ReLU activation function performed better than other models.

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Section: Resultsmentioning

confidence: 99%

Flood Prediction using Deep Learning Models

Ali¹,

Asmai²,

Abidin³

et al. 2022

IJACSA

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“…For instance, inventory management using bar codes, RFID and QR codes in conjunction with the Internet of Things (IoT) is made easier by drones. Inventory auditing by drones eliminates the majority of the requirement for physical employees to climb warehouse racks and carry out other hazardous tasks (Shibghatullah et al, 2022). Likewise, drones provide high-quality video that helps monitor vermin, monitoring temperature-controlled items, sighted objects in the dark and spotting indicators of broken wiring or leaking roofs.…”

Section: Drone Technology Adoption In Warehouse Operationsmentioning

confidence: 99%

Green Warehouse Practice: Critical issues in drone technology adoption

Wahab

Khoo²,

Sundram

et al. 2023

E-BPJ

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Businesses are being revolutionised by drones, which are now used in a variety of sectors, including warehouse operations. Drones are portable, highly manoeuvrable, and have both indoor and outdoor flight capabilities. Drones assist in streamlining the overall inventory management process for monitoring and inspection in support of green warehousing practices (GWP). Although the use of drone technology is rapidly expanding and is trending upward, relatively few studies on the subject have been published and there is a great need for scholarly study in this field. Thus, this study aims to identify several issues faced by warehouse operators. Keywords: Green Warehouse Practice, Drone Technology, Adoption, Issues eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v8i23.4441

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“…According to the findings, a vehicle can be tracked over a short distance using LoRa. Shibghatullah et al (2022) The accuracy of time estimation was emphasized for vehicle tracking systems. An Android vehicle tracking application has been developed in this context, with different versions for the driver and the user.…”

Section: Rahunathan Et Al (2022)mentioning

confidence: 99%

Determining Criteria Weights for Vehicle Tracking System Selection Using Piprecia-S

Ayteki̇n

2022

Jour Proc Manag New Tech

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Vehicle tracking systems are generally used to determine the location of vehicles, monitor them, and guide them when appropriate. This study aims to define the criteria that logistics companies consider when selecting a vehicle tracking system, as well as the relative importance of these criteria. PIPRECIA-S, a multi-criteria decision analysis method, was used in this context. According to the analysis results, the most important criterion in the selection of a vehicle tracking system is real-time tracking of the vehicle's location. When it comes to selecting a vehicle tracking system, logistics firms should prioritize instant vehicle tracking, compliance with local rules, compatibility with new technologies and software, quality certification, and compatibility with external systems-devices over other criteria. Other important criteria to consider when selecting a vehicle tracking system are system maintenance and technical support, providing statistical data collection and effective reporting, allowing the vehicle to be diverted, comprehension, simplicity of implementation and visual geo-information presentation for users, design and quality of hardware, system cost, communication infrastructure, and reducing the operating costs of companies. Also, when developing vehicle tracking systems, system developers can prioritize the aforementioned criteria.

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Vehicle Tracking Application Based on Real Time Traffic

Cited by 14 publications

References 0 publications

Flood Prediction using Deep Learning Models

Flood Prediction using Deep Learning Models

Green Warehouse Practice: Critical issues in drone technology adoption

Determining Criteria Weights for Vehicle Tracking System Selection Using Piprecia-S

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