Assessment of the road based on PCI and IRI roadroid measurement

Putra, Donny A.; Suprapto, Mamok

doi:10.1051/matecconf/201819504006

Cited by 10 publications

(7 citation statements)

References 3 publications

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“…Y, which is a share of first permits for researchers in the total number of first-residence permits issued for employment-related reasons), i.e. for which the calculated correlation coefficient exceeds 0.6), respectively to a generally recognised scale [30][31][32]. The study defined such drivers as "Research and development (R&D)" sub-pillar (Х 1 ), "Knowledge workers" sub-pillar (Х 2 ), "Innovation linkages" sub-pillar (Х 3 ), and "Knowledge creation" sub-pillar (Х 5 ).…”

Section: Resultsmentioning

confidence: 99%

“…In order to justify the most significant drivers of international migration of researchers, the study applied the interpretation of the calculated correlation coefficients according to the generally accepted guidelines [30][31][32]. The results of the correlation analysis allowed the scholars to choose four drivers of researchers' migration, which have the greatest impact on the dependent variable (i.e.…”

Section: • State Of Cluster Development and Depthmentioning

confidence: 99%

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International Migration of Researchers: Impact of Professional Drivers

Oliinyk¹

2023

IJEBM

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The purpose of the study is to determine the professional drivers which have the greatest influence on the international mobility of research scientists and to assess the level of attractiveness of EU Member States for researchers from other countries on the noted basis. The research paper used the case study of the European Union countries and derived the latest data on first employment permits issued to migrants, intellectual migrants and researchers from the Eurostat database, as well as sub-indices of the Global Innovation Index regarding research and development (R&D), knowledge creation, absorption, impact and diffusion. The author combined the methods of structural and dynamic analysis, correlation analysis and taxonomic analysis in the research process. The results obtained from the structural and dynamic analysis revealed that in recent years, on average every fourth migrant has come to EU countries aiming to find a job. In 2021, Poland topped the list among all EU Member States: every sixth permit was issued to migrants in this country. There is an overwhelming amount of evidence that the EU is one of the most attractive destinations for intellectual migration flows. In 2021, 85.04% of all first residence permits issued for employment-related reasons in the Netherlands were issued to highly skilled migrant. The value of this indicator for Austria was 70.32%, for Ireland it was 58.15%, and for Denmark it was 55.68%. The outcomes of the correlation analysis proved that countries with a high level of economic development prefer to issue the first-residence employment permits mainly to intellectual migrants. At the same time, competent issuing authorities in Germany, Slovakia and Hungary give priority to researchers when issuing the first permits. In order to investigate the importance of the professional drivers of researchers' migration, the author identified seven subpillars of the Global Innovation Index, which directly relate to research and development, creation and migration of knowledge. The results of the correlation analysis allowed the scholars to choose four drivers of researchers' migration, which have the greatest impact on the dependent variable (i.e. Y, which is a share of first permits issued to researchers in the total number of first-residence permits issued for employment-related reasons, %). The study defined such drivers as "Research and development (R&D)" sub-pillar, "Knowledge workers" sub-pillar, "Innovation linkages" subpillar, and "Knowledge creation" sub-pillar. Basing on these sub-pillars, together with the application of the taxonomic analysis, the paper assessed the attractiveness of EU countries for researchers from other countries. Hence, the author conducted an analysis of the differentiation of the EU Member States according to the values of the taxonomic indicator and proved a high level of EU countries' attractiveness for researchers from other countries, as the distribution of the comprehensive indicator is within 0.77-1.0. The practical significance of the suggested me...

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Section: Resultsmentioning

confidence: 99%

Section: • State Of Cluster Development and Depthmentioning

confidence: 99%

International Migration of Researchers: Impact of Professional Drivers

Oliinyk¹

2023

IJEBM

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“…The Pearson correlation between IRI and PCI was À0.23; however, it is considered a weak correlation. This is due to the functional differences between IRI and PCI (Putra and Suprapto, 2018). Sandra and Sarkar (2013) developed a multilinear regression model for pavement roughness based on the density of patching, potholes, rutting and cracking for different severity levels.…”

Section: Pavement Roughness and Surface Distressesmentioning

confidence: 99%

“…The Pearson correlation between IRI and PCI was −0.23; however, it is considered a weak correlation. This is due to the functional differences between IRI and PCI (Putra and Suprapto, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Development of pavement roughness regression models based on smartphone measurements

Al‐Suleiman

Alatoom

2022

JEDT

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Purpose The purpose of this paper was to study the possibility of using smartphone roughness measurements for developing pavement roughness regression models as a function of pavement age, traffic loading and traffic volume variables. Also, the effects of patching and pavement distresses on pavement roughness were investigated. The work focused on establishing pavement roughness prediction models and applying these models to pavement management systems (PMS) to help decision-makers choose the best maintenance and rehabilitation (M&R) options by using cost-effective methods. Design/methodology/approach Signal processing techniques including filtering and processing techniques were used to obtain the International Roughness Index (IRI) from raw acceleration data collected from smartphone accelerometer sensors. The obtained IRI values were inputted as a dependent variable in analytical regression models as well as several independent variables with proper transformations. Findings According to the study results, several regression models were developed with a big variation in the coefficients of determination (R2). However, the best models included pavement age, accumulated traffic volume (∑TV) and construction quality factor (CQF) with R2 equal to 0.63. It was also found that the effects of pavement distresses and patching was significant at a-level < 0.05. The patching effect on pavement roughness was found higher than the effect of other pavement distresses. Practical implications The presented results and methods in this paper could be used in the future predictions of pavement roughness and help the decision-makers to estimate M&R needs. The work focused on establishing IRI prediction models and applying these models to the PMS to help decision-makers choose the best M & R options. Originality/value To develop sound pavement roughness models, it is essential to collect roughness data using automated procedures. However, applying these procedures in developing countries faces several difficulties such as the high price and operation costs of roughness equipment and lack of technical experience. The advantage of using IRI values taken from smartphones is that the roughness evaluation survey may be expanded to cover the full road network at a cheaper cost than with automated instruments. Therefore, if the roughness survey covers more roads, the prediction model’s accuracy will be improved.

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“…The IRI method is commonly used when assessing road conditions based on their inequality with a device. While the pavement condition index method is used when assessing road conditions based on its hardness [6].…”

Section: Introductionmentioning

confidence: 99%

Road Quality Assessment Using International Roughness Index Method and Accelerometer on Android

Setiawan

Nurdin²

2019

LKJITI

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The quality of road conditions can determine comfort in driving. To find out the condition of a road whether it has good surface quality, it can use an accelerometer sensor contained in an android smartphone. This research uses the International Roughness Index (IRI) method combined with the accelerometer sensor and the Global Positioning System (GPS). Application of the results of this study can be used to facilitate the contractor maker and road repair, so they can find out which points need to be repaired. Testing is done using two different vehicles, car and motorcycle. Smartphones with road quality detection applications are attached to the car and motorcycle vehicles using a phone holder. This is to record vibration that occurs while the vehicle is moving based on road conditions. The vibration recording results are then validated in a visual observation to determine the accuracy of the assessment results. Based on the test results the level of accuracy on the car is 90% and the motorcycle is 30%.

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Assessment of the road based on PCI and IRI roadroid measurement

Cited by 10 publications

References 3 publications

International Migration of Researchers: Impact of Professional Drivers

International Migration of Researchers: Impact of Professional Drivers

Development of pavement roughness regression models based on smartphone measurements

Road Quality Assessment Using International Roughness Index Method and Accelerometer on Android

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