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Proceedings Articleβ€’DOIβ€’

RoadCare: A Deep-learning Based Approach to Quantifying Road Surface Quality

15 Jun 2020-The Compass (Association for Computing Machinery, Inc)-pp 231-242
TL;DR: This paper proposes a deep-learning based approach to road surface quality monitoring, using accelerometer and GPS sensor readings, which enables several useful smart-city applications such as spatio-temporal monitoring of the city's roads, early warning of bad road conditions, as well as choosing the "smoothest" road route to a destination.
Abstract: Roads form a critical part of any region's infrastructure. Their constant monitoring and maintenance is thus essential. Traditional monitoring mechanisms are heavy-weight, and hence have insufficient coverage. In this paper, we explore the use of crowd-sourced intelligent measurements from commuters' smart-phone sensors. Specifically, we propose a deep-learning based approach to road surface quality monitoring, using accelerometer and GPS sensor readings. Through extensive data collection of over 36 hours on different kinds of roads, and subsequent evaluation based on this, we show that the approach can achieve high accuracy (98.5%) in a three-way classification of road surface quality. We also show how the classification can be extended to a finer grained 11-point scale of road quality. The model is also efficient: it can be implemented on today's smart-phones, thus making it practical. Our approach, called RoadCare, enables several useful smart-city applications such as spatio-temporal monitoring of the city's roads, early warning of bad road conditions, as well as choosing the "smoothest" road route to a destination.
Citations
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Journal Articleβ€’
TL;DR: Understanding of the occurrence and development of road traffic injuries will contribute to the prevention and control of crash and to the implementation of "everybody has the right to enjoy health" proposed by WHO.
Abstract: The appearance of cars has raised materialistic civilization and living standard to an unprecedented level. Today, it is hard to imagine how we human beings can live without cars. Yet, motor vehicles can cause a great number of deaths and injuries as well as considerable economic losses, which have constituted the global burden. Understanding of the occurrence and development of road traffic injuries will contribute to the prevention and control of crash and to the implementation of "everybody has the right to enjoy health" proposed by WHO.

312Β citations

Journal Articleβ€’DOIβ€’
Moez Krichen1β€’
TL;DR: In this article, the authors proposed a short survey on the use of smartphone sensors in the detection of various kinds of anomalies in several fields namely environment, agriculture, healthcare and road/traffic conditions.
Abstract: With smartphones being so ubiquitous, more connected and largely fitted with several types of sensors such as GPS, microphones, cameras, magnetometers, accelerometers, etc; there is an increasing opportunity in the development of smartphone-based sensor systems. In this article, we propose a short survey on the use of smartphone sensors in the detection of various kinds of anomalies in several fields namely environment, agriculture, healthcare and road/traffic conditions. We also list the main advantages and limitations of the use of smartphone sensors systems in such fields.

36Β citations

Journal Articleβ€’DOIβ€’
TL;DR: In this article , the authors describe the body of knowledge in smartphone-based roughness assessment, report knowledge gaps and cast light on future research directions. But, they focus on practical factors that are expected to affect the accuracy and robustness of smartphonebased methods, including data collection speed, vehicle type, smartphone specifications and mounting configuration.

11Β citations

Journal Articleβ€’DOIβ€’
TL;DR: The results of this review show that road surface anomaly detection and classification performed through vibration-based methods have achieved relatively high performance, however, there are challenges related to the reproduction and heterogeneity of the results that have been reported that are influenced by the limited testing conditions, sample size, and lack of publicly available datasets.
Abstract: Road surfaces suffer from sources of deterioration, such as weather conditions, constant usage, loads, and the age of the infrastructure. These sources of decay generate anomalies that could cause harm to vehicle users and pedestrians and also develop a high cost to repair the irregularities. These drawbacks have motivated the development of systems that automatically detect and classify road anomalies. This study presents a narrative review focused on road surface anomaly detection and classification based on vibration-based techniques. Three methodologies were surveyed: threshold-based methods, feature extraction techniques, and deep learning techniques. Furthermore, datasets, signals, preprocessing steps, and feature extraction techniques are also presented. The results of this review show that road surface anomaly detection and classification performed through vibration-based methods have achieved relatively high performance. However, there are challenges related to the reproduction and heterogeneity of the results that have been reported that are influenced by the limited testing conditions, sample size, and lack of publicly available datasets. Finally, there is potential to standardize the features computed through the time or frequency domains and evaluate and compare the diverse set of settings of time-frequency methods used for feature extraction and signal representation.

6Β citations

References
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Bookβ€’
31 Jan 1986
TL;DR: The International Roughness Index (IRI) as discussed by the authors is based on simulation of the roughness response of a car travelling at 80 km/h and is used for road roughness measurement.
Abstract: Road roughness is gaining increasing importance as an indicator of road condition, both in terms of pavement performance, and as a major determinant of road user costs. This paper defines roughness measurement systems hierachically into four groups, ranging from profilometric methods (2 groups), through response type road roughness measuring systems (RTRRMS's), and, subjective evaluation. The International Roughness Index (IRI) is defined, and the programs for it's calculation are provided. The IRI is based on simulation of the roughness response of a car travelling at 80 km/h. The report explains how all roughness measurements can be related to this scale, also when travelling at lower speeds than 80 km/h. The IRI emerges as a scale that can be used both for calibration and for comparative purposes.

323Β citations


"RoadCare: A Deep-learning Based App..." refers methods in this paper

  • ...RTRRMs require specialized vehicles with known suspension and require calibration with the International Roughness Index (IRI) developed by the World Bank [2]....

    [...]

Journal Articleβ€’
TL;DR: Understanding of the occurrence and development of road traffic injuries will contribute to the prevention and control of crash and to the implementation of "everybody has the right to enjoy health" proposed by WHO.
Abstract: The appearance of cars has raised materialistic civilization and living standard to an unprecedented level. Today, it is hard to imagine how we human beings can live without cars. Yet, motor vehicles can cause a great number of deaths and injuries as well as considerable economic losses, which have constituted the global burden. Understanding of the occurrence and development of road traffic injuries will contribute to the prevention and control of crash and to the implementation of "everybody has the right to enjoy health" proposed by WHO.

312Β citations

Proceedings Articleβ€’DOIβ€’
04 Dec 2009
TL;DR: A high-accuracy human activity recognition system based on single tri-axis accelerometer for use in a naturalistic environment that exploits the discrete cosine transform, the Principal Component Analysis (PCA) and Support Vector Machine for classification human different activity.
Abstract: This paper developed a high-accuracy human activity recognition system based on single tri-axis accelerometer for use in a naturalistic environment. This system exploits the discrete cosine transform (DCT), the Principal Component Analysis (PCA) and Support Vector Machine (SVM) for classification human different activity. First, the effective features are extracted from accelerometer data using DCT. Next, feature dimension is reduced by PCA in DCT domain. After implementing the PCA, the most invariant and discriminating information for recognition is maintained. As a consequence, Multi-class Support Vector Machines is adopted to distinguish different human activities. Experiment results show that the proposed system achieves the best accuracy is 97.51%, which is better than other approaches.

262Β citations


"RoadCare: A Deep-learning Based App..." refers background in this paper

  • ...We extract both raw as well as aggregated features from a single sample of 3 seconds and use them to train the SVM classifier....

    [...]

  • ...In this section we present the evaluation results for the SVM classifier in Section 4.1 and neural network based classification in Section 4.2....

    [...]

  • ...The evaluation of SVM with accelerometer features (π‘†π‘‰π‘€π‘Žπ‘π‘ ) is shown in Section 4.1....

    [...]

  • ...We have evaluated the SVM classifier on our 𝐿𝑙𝑏𝑙 dataset for the set of features described in Section 3.2....

    [...]

  • ...Table 2 shows the classification accuracy for using SVM with β€˜accelerometer only’ features (π‘†π‘‰π‘€π‘Žπ‘π‘ ) as well as with combined accelerometer and speed features (π‘†π‘‰π‘€π‘Žπ‘π‘+𝑠𝑝𝑒𝑒𝑑 ), for both linear and RBF (radial basis function) kernels....

    [...]

Bookβ€’
03 Oct 2008

237Β citations

Journal Articleβ€’DOIβ€’
TL;DR: The authors developed a model in which public capital is both an engine of growth and a determinant of the distributions of wealth, income, and welfare, and showed that public investment increases wealth inequality over time, regardless of its financing.
Abstract: We develop a model in which public capital is both an engine of growth and a determinant of the distributions of wealth, income, and welfare. Government investment increases wealth inequality over time, regardless of its financing. The time path of income inequality is, however, highly sensitive to financing policies, and is often characterized by sharp intertemporal tradeoffs, with income inequality declining in the short run but increasing in the long run. Public investment generates a positive correlation between growth and income inequality along the transition path, but their short-run and long-run relationship depends critically on (i) how externalities impinge on allocation decisions, (ii) financing policies, and (iii) the time period of consideration. Finally, these policies also generate sharp trade-offs between average welfare and its distribution, with government investment improving average welfare, but also increasing its dispersion. Our results are obtained numerically but extensive sensitivity analysis confirms their robustness across key parameter values.

106Β citations