Identification of Road Surface Conditions using IoT Sensors and Machine Learning

Ng, Jin Ren; Wong, Jan Shao; Goh, Vik Tor; Yap, Wen Jiun; Yap, Timothy Tzen Vun; Ng, Hu

doi:10.1007/978-981-13-2622-6_26

Cited by 31 publications

(13 citation statements)

References 9 publications

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“…The results of this research showed that RF outperformed the implemented baseline predictor, the regression tree and a Feed Forward Neural Network in all three measurements for both long and short-term predictions. In [35], the goal is to identify road surface abnormalities. This is achieved by collecting data using accelerometer sensors with Arduino microcontrollers and then apply a set of feature selection and ML algorithms on the data.…”

Section: Ensemblementioning

confidence: 99%

“…k-NN is run multiple times on the testing data in order to select the ideal k factor that minimizes the errors while classifying data. We have already met k-NN in [35], where road surface abnormalities are detected using accelerometer data and applying k-Nearest Neighbour (k-NN), RF, and Support Vector Machine (SVM) algorithms. The parameter k of k-NN represents the number of the test sample's neighbours.…”

Section: Instance Basedmentioning

confidence: 99%

“…We have seen in [35], the comparison of k-NN, RF, and Support Vector Machine (SVM) in order to identify road surface abnormalities. Linear SVM, as well as the other two classifiers, performed very well for this task, with a CCR Rate over 99%.…”

Section: Non-probabilistic Linear Classificationmentioning

confidence: 99%

“…A similar approach to detect road anomalies is used in [34], and the data in this research are also clustered using the k-Means method. Last but not least, in [35] the authors compare the methods k-NN, RF, and SVM for the task of road abnormalities detection.…”

Section: Road Anomalies Detectionmentioning

confidence: 99%

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A Review of Machine Learning and IoT in Smart Transportation

et al. 2019

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With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.

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Section: Ensemblementioning

confidence: 99%

Section: Instance Basedmentioning

confidence: 99%

Section: Non-probabilistic Linear Classificationmentioning

confidence: 99%

Section: Road Anomalies Detectionmentioning

confidence: 99%

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A Review of Machine Learning and IoT in Smart Transportation

et al. 2019

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“…Road surface roughness is based on visual inspections or using specialized instruments to take physical measurements of the road irregularities [10][11][12]. The estimate of the roughness index (IRI) requires a fixed accelerometer in the car cabin, calibration to take into account the tires and the suspension system of the car, and skilled labor.…”

Section: Road Surface Conditions Based On Accelerometers Readingsmentioning

confidence: 99%

A participatory sensing framework to classify road surface quality

Nunes

Mota

2019

J Internet Serv Appl

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Participatory sensing networks rely on gathering personal data from mobile devices to infer global knowledge. Participatory sensing has been used for real-time traffic monitoring, where the global traffic conditions are based on information provided by individual devices. However, fewer initiatives address asphalt quality conditions, which is an essential aspect of the route decision process. This article proposes Streetcheck, a framework to classify road surface quality through participatory sensing. Streetcheck gathers mobile devices' sensors such as Global Positioning System (GPS) and accelerometer, as well as users' ratings on road surface quality. A classification system aggregates the data, filters them, and extracts a set of features as input for supervised learning algorithms. Twenty volunteers carried out tests using Streetcheck on 1,200 km of urban roads of Minas Gerais (Brazil). Streetcheck reached up to 90.64% of accuracy on classifying road surface quality.

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