Crowdsourcing from the True crowd: Device, vehicle, road-surface and driving independent road profiling from smartphone sensors

Alam, Munshi Yusuf; Nandi, Akash; Kumar, Abhay; Saha, Sujoy; Saha, Mousumi; Nandi, S.; Chakraborty, Sandip

doi:10.1016/j.pmcj.2019.101103

Cited by 28 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their experiments were carried out with real-world information and the results showed an accuracy above 95%. Alam et al ( 9 ) proposed a system that performs both threshold tuning and machine learning in two phases. First, the system applies three-dimensional rotation object rotation technique to the auto-orient accelerometer and linear regression algorithm to auto-tune thresholds for primary detection of anomalies.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial Roadway Condition-Assessment Mapping Utilizing Smartphones and Machine Learning Algorithms

Kyriakou

Christodoulou

Dimitriou

2021

Transportation Research Record

View full text Add to dashboard Cite

The paper presents a data-driven framework and related field studies on the use of supervised machine learning and smartphone technology for the spatial condition-assessment mapping of roadway pavement surface anomalies. The study explores the use of data, collected by sensors from a smartphone and a vehicle’s onboard diagnostic device while the vehicle is in movement, for the detection of roadway anomalies. The research proposes a low-cost and automated method to obtain up-to-date information on roadway pavement surface anomalies with the use of smartphone technology, artificial neural networks, robust regression analysis, and supervised machine learning algorithms for multiclass problems. The technology for the suggested system is readily available and accurate and can be utilized in pavement monitoring systems and geographical information system applications. Further, the proposed methodology has been field-tested, exhibiting accuracy levels higher than 90%, and it is currently expanded to include larger datasets and a bigger number of common roadway pavement surface defect types. The proposed system is of practical importance since it provides continuous information on roadway pavement surface conditions, which can be valuable for pavement engineers and public safety.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Spatial Roadway Condition-Assessment Mapping Utilizing Smartphones and Machine Learning Algorithms

Kyriakou

Christodoulou

Dimitriou

2021

Transportation Research Record

View full text Add to dashboard Cite

show abstract

“…Their system achieved a classification performance of 90% in a five-class problem considering the following road qualities: Good, Average, Fair, Poor and obstacles. Alam et al (2020) aimed to develop a system that detects three road events, speed-breakers, potholes and broken road patches, over smooth and rough roads. The system's first phase used robust auto-orientation and autotune thresholding algorithms, the second phase utilized decision tree based classifier to reduce false-negative and false-positives and the third phase applied a k-medoids clustering to geo-localized detected events over a map service.…”

Section: Literature Reviewmentioning

confidence: 99%

A pavement rating system based on machine learning

Kyriakou¹,

Christodoulou²

2021

Proceedings of the 2021 European Conference on Computing in Construction

View full text Add to dashboard Cite

The evaluation of roadways utilizing complex contemporary datasets is currently conducted periodically because of the collection methods' high cost.The study presents a data-driven framework on the use of a vehicle, a smartphone, an on-board diagnostic (OBD) device and machine learning for the rating of pavement surfaces. The proposed system architecture has been fieldtested for the detection of pavement anomalies and the classification of five rating categories. Further, the proposed system may provide daily information on roadway pavement surface conditions, which can be used by engineers for automating the planning of pavement maintenance operations and improving public safety.

show abstract

“…Different approaches in the literature have been proposed to classify road anomalies based on features obtained from the accelerometer sensor. Especially the machine learning algorithms which are quite diverse ( Eriksson et al, 2008 ; Perttunen et al, 2011 ; Carlos et al, 2018 ; Bridgelall & Tolliver, 2020 ; Alam et al, 2020 ). In Basavaraju et al (2019) ; Silva et al (2017) , the authors employed Multilayer Perceptron (MLP) and they made comparisons with other models such as Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT).…”

Section: Introductionmentioning

confidence: 99%

“…In Basavaraju et al (2019) ; Silva et al (2017) , the authors employed Multilayer Perceptron (MLP) and they made comparisons with other models such as Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT). Other researchers used a decision tree-based classifier ( Alam et al, 2020 ; Kalim, Jeong & Ilyas, 2016 ) to detect and classify different types of road anomalies. Also, Support Vector Machines have been widely used in many works ( Eriksson et al, 2008 ; Perttunen et al, 2011 ; Carlos et al, 2018 ; Bridgelall & Tolliver, 2020 ; Alam et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…Other researchers used a decision tree-based classifier ( Alam et al, 2020 ; Kalim, Jeong & Ilyas, 2016 ) to detect and classify different types of road anomalies. Also, Support Vector Machines have been widely used in many works ( Eriksson et al, 2008 ; Perttunen et al, 2011 ; Carlos et al, 2018 ; Bridgelall & Tolliver, 2020 ; Alam et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

How to get best predictions for road monitoring using machine learning techniques

Ferjani

Alsaif

2022

PeerJ Computer Science

View full text Add to dashboard Cite

Road condition monitoring is essential for improving traffic safety and reducing accidents. Machine learning methods have recently gained prominence in the practically important task of controlling road surface quality. Several systems have been proposed using sensors, especially accelerometers present in smartphones due to their availability and low cost. However, these methods require practitioners to specify an exact set of features from all the sensors to provide more accurate results, including the time, frequency, and wavelet-domain signal features. It is important to know the effect of these features change on machine learning model performance in handling road anomalies classification tasks. Thus, we address such a problem by conducting a sensitivity analysis of three machine learning models which are Support Vector Machine, Decision Tree, and Multi-Layer Perceptron to test the effectiveness of the model by selecting features. We built a feature vector from all three axes of the sensors that boosts classification performance. Our proposed approach achieved an overall accuracy of 94% on four types of road anomalies. To allow an objective analysis of different features, we used available accelerometer datasets. Our objective is to achieve a good classification performance of road anomalies by distinguishing between significant and relatively insignificant features. Our chosen baseline machine learning models are based on their comparative simplicity and powerful empirical performance. The extensive analysis results of our study provide practical advice for practitioners wishing to select features effectively in real-world settings for road anomalies detection.

show abstract

Crowdsourcing from the True crowd: Device, vehicle, road-surface and driving independent road profiling from smartphone sensors

Cited by 28 publications

References 12 publications

Spatial Roadway Condition-Assessment Mapping Utilizing Smartphones and Machine Learning Algorithms

Spatial Roadway Condition-Assessment Mapping Utilizing Smartphones and Machine Learning Algorithms

A pavement rating system based on machine learning

How to get best predictions for road monitoring using machine learning techniques

Contact Info

Product

Resources

About