Measurement of Street Pavement Roughness in Urban Areas Using Smartphone

Alatoom, Yazan Ibrahim; Obaidat, Turki I. Al-Suleiman

doi:10.1007/s42947-021-00069-3

Cited by 17 publications

(11 citation statements)

References 36 publications

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“…The results of the comparative analysis based on ANOVA tests showed that the best factors were the double integration method, 200 samples/sec and vent mount. More detailed information about smartphone pavement roughness measurements could be found in Alatoom (2021). Figure 5 illustrates the methodology flow chart for the development of a pavement roughness database using smartphone.…”

Section: Methodology and Data Collectionmentioning

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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Section: Methodology and Data Collectionmentioning

confidence: 99%

Development of pavement roughness regression models based on smartphone measurements

Al‐Suleiman

Alatoom

2022

JEDT

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“…The premise is that when a vehicle circulates on a distressed road surface (e.g., characterized by potholes, cracking, and unmaintained manholes), it produces more vibrations compared to a perfectly maintained road pavement surface [5]. This is the principle used both for the evaluation of standardized index evaluation, such as the IRI index [7][8][9][10], and also with reference to non-standardized procedures and indices [11][12][13][14][15][16].…”

Section: State Of the Artmentioning

confidence: 99%

A Vibration-Based Methodology to Monitor Road Surface: A Process to Overcome the Speed Effect

Meocci

2024

Sensors

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Road pavement monitoring represents the starting point for the pavement maintenance process. To quickly fix a damaged road, relevant authorities need a high-efficiency methodology that allows them to obtain data describing the current conditions of a road network. In urban areas, large-scale monitoring campaigns may be more expensive and not fast enough to describe how pavement degradation has evolved over time. Furthermore, at low speeds, many technologies are inadequate for monitoring the streets. In such a context, employing black-box-equipped vehicles to perform a routine inspection could be an excellent starting point. However, the vibration-based methodologies used to detect road anomalies are strongly affected by the speed of the monitoring vehicles. This study uses a statistical method to analyze the effects of speed on road pavement conditions at different severity levels, through data recorded by taxi vehicles. Likewise, the study introduces a process to overcome the speed effect in the measurements. The process relies on a machine learning approach to define the decision boundaries to predict the severity level of the road surface condition based on two recorded parameters only: speed and pavement deterioration index. The methodology has succeeded in predicting the correct damage severity level in more than 80% of the dataset, through a user-friendly real-time method.

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“…They claimed that such a method could be helpful in assessing roughness in pedestrian and cycle lanes. Alatoom and Obaidat presented a smartphone-based approach for evaluating street pavement roughness in urban areas using IRI ( 14 ). Their results showed that the proposed method could estimate IRI with great accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Smartphone-Based and Drone-Based Approaches for Assessing Road Roughness

Afsharnia

Ghavami

2023

Transportation Research Record: Journal of the Transportation R

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Continuous road-pavement condition monitoring is essential to maintain the effectiveness and performance of road infrastructure. It also helps to optimize resource allocation and prioritization for pavement rehabilitation programs. The traditional methods that have been used for assessing road pavement condition are high cost and time consuming. Contrary to conventional methods, low-cost and new emerging technologies such as smartphones and drones can be used. The study aims to investigate the merits of using two different types of device for assessing road roughness: the smartphone as a response-type and the drone as a non-contact-type device. To this end, firstly, linear vertical acceleration data is collected by using a smartphone that is mounted on a car dashboard. Then, the collected data is used for calculating vertical displacement data using a fast Fourier transform. The displacement data is used for calculating international roughness index (IRI) as a measure of road roughness. Secondly, a drone flies over the studied road to construct a 3D model, extract the longitudinal profile, and eventually calculate the accurate IRI values of the road. Finally, the results of the approaches above will be compared with the results of a road surface profiler (RSP) as a reference method. Although the results reveal a significant correlation (above 0.8) of both methods with the RSP method, from an operational point of view, the smartphone was more cost and time effective, and also an easy-to-use approach for the purpose.

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Measurement of Street Pavement Roughness in Urban Areas Using Smartphone

Cited by 17 publications

References 36 publications

Development of pavement roughness regression models based on smartphone measurements

Development of pavement roughness regression models based on smartphone measurements

A Vibration-Based Methodology to Monitor Road Surface: A Process to Overcome the Speed Effect

Comparison of Smartphone-Based and Drone-Based Approaches for Assessing Road Roughness

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