Accident Detection and Road Condition Monitoring Using Blackbox Module

Jahan, Insan Arafat; Jamil, Insan Arafat; Fahim, Mohammad; Huq, Armana Sabiha; Faisal, Fahim; Nishat, Mirza Muntasir

doi:10.1109/icufn55119.2022.9829589

Cited by 24 publications

(6 citation statements)

References 26 publications

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“…Real-world data evaluation demonstrated the effectiveness of the proposed method in detecting potholes on road surfaces. Janhan et al [53] introduced an embedded solution for RPCM with a focus on accident detection and prevention. The module, installed in vehicles, utilizes data mapping techniques to analyze sensor data, including GPS and acc data, for accident detection and monitoring road conditions.…”

Section: B Dtw Approachmentioning

confidence: 99%

Crowdsensing for Road Pavement Condition Monitoring: Trends, Limitations, and Opportunities

Jan,

Khattak,

Khan

et al. 2023

IEEE Access

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RPCM forms a crucial element of preventive maintenance strategies, particularly in light of escalating vehicular pressures and the advent of extreme weather patterns. Consequently, there is a growing demand for cost-effective solutions that leverage emergent technologies such as the IoT, AI, and cloud computing. This research intends to articulate the evolutionary trajectory of road solutions while delineating the prevalent challenges and offering viable trajectories for future enhancements. To achieve this objective, a systematic literature review was executed using the Scopus and Web of Science databases, the aim of which was to discern the inherent challenges of existing solutions. Following a stringent elimination process of duplicates and irrelevant studies, a corpus of 74 research papers was assembled for review. Assessment criteria encompassed the sensing platforms and algorithms deployed, the variety of road deformities detected, and the overall accuracy of the proposed solutions. The analysis revealed a variety of methodologies applied to RPCM, each bearing distinct advantages and limitations. Notably, SPbased monitoring solutions utilizing ML techniques and improved data gathering methodologies exhibited superior outcomes relative to alternative approaches. To conclude, this research elucidates the wide-ranging methodologies in RPCM, critically examining their respective advantages and drawbacks. Among the methodologies surveyed, SP-based monitoring deploying ML techniques emerges as a compelling approach, demonstrating the potential for enhancing accuracy and data gathering techniques. These insights form a valuable foundation for the conception and development of future cost-effective and efficacious RPCM solutions.

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Section: B Dtw Approachmentioning

confidence: 99%

Crowdsensing for Road Pavement Condition Monitoring: Trends, Limitations, and Opportunities

Jan,

Khattak,

Khan

et al. 2023

IEEE Access

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“…The required time of traffic accident investigation was reduced to 66% with the help of a device that was addressed as road traffic accident information system (RTAIS) by Tai et al [16]. Jahan et al [17], a has implemented a "Blackbox" module that helps to map and record or document the data of a specific road's condition that can be compared with the available accident data of those locations and alert system is enabled for those accident-prone areas. By this approach, the rate of accidents each year can be reduced up to 80%.…”

Section: Introductionmentioning

confidence: 99%

An intelligent road accident reduction system using device-to-device communication

Jabid

Rizvy

Mow

et al. 2023

IJEECS

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<p>Every year thousands of people are dying due to road accidents and most of the accidents are occurring in urban areas and highways. As the number of vehicles is increasing day by day, the probability of the occurrence of the accident is also increasing. A system has been introduced in this paper that can reduce road accidents by sending an alert to drivers via smartphones. For this purpose, an android-based application has been developed. To reduce the accident, every driver of the car must have the application. Firstly, the location of the vehicle will be collected from the driver’s smartphone, then the data will be sent to the server through the application. An algorithm has been also developed using Haversine Formula which calculates the distance among the vehicles. A total of five cases have been implemented according to the dataset and compared for the performance evaluation. An extensive experimental study and comparison have been performed with the other methods where a complete performance is focused that can be claimed to reduce road accidents via the device-to-device communication process.</p>

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“…However, clinical assessment of mass undergraduate students would be unwieldy and resource-heavy, since there can be numerous factors involved in instigating depression. In this regard, Machine Learning (ML) models [16][17][18][19][20][21][22][23][24][25] can perhaps become valuable for detecting and predicting subsequent health issues [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] as well as depressive episodes. Furthermore, the result can be analyzed to identify depression-related trends revealed among young people which can aid higher education institutions to understand the factors better and develop effective strategies to mitigate these factors.…”

Section: Introductionmentioning

confidence: 99%

Impact of mobile connectivity on students’ wellbeing: Detecting learners’ depression using machine learning algorithms

Siraji,

Rahman,

Nishat

et al. 2023

PLoS ONE

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Depression is a psychological state of mind that often influences a person in an unfavorable manner. While it can occur in people of all ages, students are especially vulnerable to it throughout their academic careers. Beginning in 2020, the COVID-19 epidemic caused major problems in people’s lives by driving them into quarantine and forcing them to be connected continually with mobile devices, such that mobile connectivity became the new norm during the pandemic and beyond. This situation is further accelerated for students as universities move towards a blended learning mode. In these circumstances, monitoring student mental health in terms of mobile and Internet connectivity is crucial for their wellbeing. This study focuses on students attending an International University of Bangladesh to investigate their mental health due to their continual use of mobile devices (e.g., smartphones, tablets, laptops etc.). A cross-sectional survey method was employed to collect data from 444 participants. Following the exploratory data analysis, eight machine learning (ML) algorithms were used to develop an automated normal-to-extreme severe depression identification and classification system. When the automated detection was incorporated with feature selection such as Chi-square test and Recursive Feature Elimination (RFE), about 3 to 5% increase in accuracy was observed by the method. Similarly, a 5 to 15% increase in accuracy has been observed when a feature extraction method such as Principal Component Analysis (PCA) was performed. Also, the SparsePCA feature extraction technique in combination with the CatBoost classifier showed the best results in terms of accuracy, F1-score, and ROC-AUC. The data analysis revealed no sign of depression in about 44% of the total participants. About 25% of students showed mild-to-moderate and 31% of students showed severe-to-extreme signs of depression. The results suggest that ML models, incorporating a proper feature engineering method can serve adequately in multi-stage depression detection among the students. This model might be utilized in other disciplines for detecting early signs of depression among people.

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Accident Detection and Road Condition Monitoring Using Blackbox Module

Cited by 24 publications

References 26 publications

Crowdsensing for Road Pavement Condition Monitoring: Trends, Limitations, and Opportunities

Crowdsensing for Road Pavement Condition Monitoring: Trends, Limitations, and Opportunities

An intelligent road accident reduction system using device-to-device communication

Impact of mobile connectivity on students’ wellbeing: Detecting learners’ depression using machine learning algorithms

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