Road Surface Quality Detection using Smartphone Sensors: Egyptian Roads Case Study

El-Kady, Aya; Emara, Karim; ElEliemy, Mohamed Hamdy; Shaaban, Eman

doi:10.1109/icicis46948.2019.9014721

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…There is a growing trend of relying on collections of crowdsourced data in designing and implementing various concepts of smart transportation and smart city [ 42 ]. Data recorded with sensors of wearable devices, such as smartphones, can be used to categorize driving behavior [ 43 , 44 ], detect transportation hazards [ 45 ], and assess road surface characteristics [ 46 , 47 ]. Smartphone accelerometer, gyroscope, and GPS sensors allow for collecting huge yet typically imbalanced datasets that would be used to train machine learning classifiers for the smart technologies.…”

Section: Case Studymentioning

confidence: 99%

A Machine Learning Framework for Balancing Training Sets of Sensor Sequential Data Streams

Setiawan

Serdült

Kryssanov

2021

Sensors

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The recent explosive growth in the number of smart technologies relying on data collected from sensors and processed with machine learning classifiers made the training data imbalance problem more visible than ever before. Class-imbalanced sets used to train models of various events of interest are among the main reasons for a smart technology to work incorrectly or even to completely fail. This paper presents an attempt to resolve the imbalance problem in sensor sequential (time-series) data through training data augmentation. An Unrolled Generative Adversarial Networks (Unrolled GAN)-powered framework is developed and successfully used to balance the training data of smartphone accelerometer and gyroscope sensors in different contexts of road surface monitoring. Experiments with other sensor data from an open data collection are also conducted. It is demonstrated that the proposed approach allows for improving the classification performance in the case of heavily imbalanced data (the F1 score increased from 0.69 to 0.72, p<0.01, in the presented case study). However, the effect is negligible in the case of slightly imbalanced or inadequate training sets. The latter determines the limitations of this study that would be resolved in future work aimed at incorporating mechanisms for assessing the training data quality into the proposed framework and improving its computational efficiency.

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Section: Case Studymentioning

confidence: 99%

A Machine Learning Framework for Balancing Training Sets of Sensor Sequential Data Streams

Setiawan

Serdült

Kryssanov

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Automatic labeling is crucial to generalize the adopted methodology when dealing with very large datasets since manual labeling is impractical in that case. Authors in [17] introduced a generic methodology for automatically labeling the collected dataset whether each window of sensor readings reflect a road anomaly or normal road. This methodology evaluated K-Means and density-based clustering (DBSCAN) [2] algorithms and found that DBSCAN achieves a better accuracy than K-Means with minimum accuracy of 96% when compared with the manually labeled datasets.…”

Section: E Classification Modulementioning

confidence: 99%

Mobile Crowdsensing Framework for Road Surface Quality Detection.

Emara

El-Kady

Shaaban

et al. 2021

IJICIS

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IoT for measuring road network quality index

Raslan

Alrahmawy²,

Mohammed³

et al. 2022

Neural Comput & Applic

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Egypt has been fighting the issue of ensuring road safety‚ reducing accidents‚ preserving the lives of citizens since its inception. For these reasons‚ precisely identifying the road condition‚ followed by effective and timely maintenance and rehabilitation measures‚ leads to an increase in the road network's safety level and lifespan. This paper presents a multi-input deep learning framework that combines BiLSTM and Depthwise separable convolution to work in parallel for automatic recognition of road surface quality and different road anomalies. Furthermore, we performed an investigation to compare deep networks approaches against other traditional approaches using real-time data sensed and collected from the Egyptian road network. The proposed deep model has achieved an average accuracy of 93.1%‚ which is superior compared to other evaluated approaches. Finally, we utilized the proposed model to estimate a road quality index in the Egyptian cities.

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Road Surface Quality Detection using Smartphone Sensors: Egyptian Roads Case Study

Cited by 5 publications

References 10 publications

A Machine Learning Framework for Balancing Training Sets of Sensor Sequential Data Streams

A Machine Learning Framework for Balancing Training Sets of Sensor Sequential Data Streams

Mobile Crowdsensing Framework for Road Surface Quality Detection.

IoT for measuring road network quality index

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