Intelligent Motorcycle Monitoring Scheme using IoT with Expert System in Bangladesh

Tashfia, Saima Siddique; Islam, Rahabul; Sultan, Sadee Ibn; Rahman, Wahidur; Habib, Ahsan; Pinky, Lubna Yasmin

doi:10.1109/iccit51783.2020.9392675

Cited by 5 publications

(2 citation statements)

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“…Ensuring the secure operation of shared mobility systems mainly depends on monitoring the behavior of road users, especially bike and scooter riders, and minimizing the vulnerabilities they are exposed to on the road [17]. Specifically, most studies on ensuring bike/scooter rider safety focused on the use of smart helmets [18][19][20][21][22] or smart bikes [23][24][25] along with mobile applications [19,[23][24][25] or cloud-based databases [22,25].…”

Section: Literature Reviewmentioning

confidence: 99%

“…A smart bike architecture, proposed by [24], integrated a microcontroller, an accelerometer/gyroscope module to monitor bike movements, and a GSM/GPRS module to collect the bike's location and send it over the cellular network to emergency contacts. The study in [25] used a similar system along with the use of MQTT protocol to transmit the collected data to a cloud-based database.…”

Section: Literature Reviewmentioning

confidence: 99%

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Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility

Elwy,

Aburukba,

Al-Ali

et al. 2023

Future Internet

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Shared mobility is one of the smart city applications in which traditional individually owned vehicles are transformed into shared and distributed ownership. Ensuring the safety of both drivers and riders is a fundamental requirement in shared mobility. This work aims to design and implement an adequate framework for shared mobility within the context of a smart city. The characteristics of shared mobility are identified, leading to the proposal of an effective solution for real-time data collection, tracking, and automated decisions focusing on safety. Driver and rider safety is considered by identifying dangerous driving behaviors and the prompt response to accidents. Furthermore, a trip log is recorded to identify the reasons behind the accident. A prototype implementation is presented to validate the proposed framework for a delivery service using motorbikes. The results demonstrate the scalability of the proposed design and the integration of the overall system to enhance the rider’s safety using machine learning techniques. The machine learning approach identifies dangerous driving behaviors with an accuracy of 91.59% using the decision tree approach when compared against the support vector machine and K-nearest neighbor approaches.

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