Pavement roughness evaluation method based on the theoretical relationship between acceleration measured by smartphone and IRI

Zhang, Zhen; Zhang, Hongliang; Xu, Shengyuan; Lv, Wenjiang

doi:10.1080/10298436.2021.1881783

Cited by 22 publications

(6 citation statements)

References 30 publications

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“…Shohel et al [23] proposed a method to determine the IRI of a pavement surface using conventional vehicles and smartphones by employing the grey box model algorithm and the quarter-car vehicle model. The results demonstrate that smartphones can be used to determine pavement IRI with reasonable accuracy An algorithm was developed by Zhang et al [24] to calculate the International Roughness Index (IRI) using acceleration values obtained from smartphone sensors. This algorithm also identified physical parameters associated with a quarter-scale vehicle model and established a relationship between acceleration, IRI, and profile elevation.…”

Section: Related Workmentioning

confidence: 90%

Design and Implementation of a Vibration-Based Real-Time Internet of Things Framework for Road Condition Monitoring

AbdelRaheem,

Hassan,

Alyousify

et al. 2023

IEEE Open J. Veh. Technol.

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This paper presents a study on the design and implementation of a real-time Internet of Things (IoT) framework for monitoring road conditions. The proposed system incorporates an accelerometer sensor attached to the rear axle of a vehicle to capture vertical acceleration signals resulting from the interaction between the vehicle suspension system and the road surface. These acceleration signals are utilized to determine vertical displacements, which are then used to calculate the International Roughness Index (IRI) as an indicator of road quality. The system comprises a hardware node for acquiring acceleration signals and a remote monitoring system for calculating and visually representing the IRI on a road map. Our results demonstrate that the proposed system is effective in automatically assessing road conditions, with an error in the calculated IRI of less than 3.5%INDEX TERMS Internet of Things (IoT), road condition assessment, cellular internet, IRI.

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Section: Related Workmentioning

confidence: 90%

Design and Implementation of a Vibration-Based Real-Time Internet of Things Framework for Road Condition Monitoring

AbdelRaheem,

Hassan,

Alyousify

et al. 2023

IEEE Open J. Veh. Technol.

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“…Based on the data type, different performance measurements were developed to monitor pavement conditions over the years. For instance, as Table 1 summarizes, accelerometer data, which can measure vehicle vertical fluctuations, were employed to assess the overall pavement roughness implicitly, and they were validated to be effective when compared with the International Roughness Index (IRI) [11,12,33,34]. Video collected for pavement surface was used to extract the pavement distress like longitudinal and transverse cracking, rutting, and potholes [15,29].…”

Section: Pavement Performance Measurementsmentioning

confidence: 99%

Estimating Pavement Condition by Leveraging Crowdsourced Data

Gu,

Khojastehpour,

Jia

et al. 2024

Remote Sensing

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Monitoring pavement conditions is critical to pavement management and maintenance. Traditionally, pavement distress is mainly identified via accelerometers, videos, and laser scanning. However, the geographical coverage and temporal frequency are constrained by the limited amount of equipment and labor, which sometimes may delay road maintenance. By contrast, crowdsourced data, in a manner of crowdsensing, can provide real-time and valuable roadway information for extensive coverage. This study exploited crowdsourced Waze pothole and weather reports for pavement condition evaluation. Two surrogate measures are proposed, namely, the Pothole Report Density (PRD) and the Weather Report Density (WRD). They are compared with the Pavement Quality Index (PQI), which is calculated using laser truck data from the Tennessee Department of Transportation (TDOT). A geographically weighted random forest (GWRF) model was developed to capture the complicated relationships between the proposed measures and PQI. The results show that the PRD is highly correlated with the PQI, and the correlation also varies across the routes. It is also found to be the second most important factor (i.e., followed by pavement age) affecting the PQI values. Although Waze weather reports contribute to PQI values, their impact is significantly smaller compared to that of pothole reports. This paper demonstrates that surrogate pavement condition measures aggregated by crowdsourced data could be integrated into the state decision-making process by establishing nuanced relationships between the surrogated performance measures and the state pavement condition indices. The endeavor of this study also has the potential to enhance the granularity of pavement condition evaluation.

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“…The effects of smartphone type on pavement roughness measurement have been widely studied, with multiple studies reporting a significant impact [56,85,90,[93][94][95]. For example, Zhang et al [92] reported a relative error of up to 11%, which they considered tolerable.…”

Section: Acceleration-based Response-type Approachesmentioning

confidence: 99%

Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review

Fares

Zayed

2023

Remote Sensing

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Roughness is widely used as a primary measure of pavement condition. It is also the key indicator of the riding quality and serviceability of roads. The high demand for roughness data has bolstered the evolution of roughness measurement techniques. This study systematically investigated the various trends in pavement roughness measurement techniques within the industry and research community in the past five decades. In this study, the Scopus and TRID databases were utilized. In industry, it was revealed that laser inertial profilers prevailed over response-type methods that were popular until the 1990s. Three-dimensional triangulation is increasingly used in the automated systems developed and used by major vendors in the USA, Canada, and Australia. Among the research community, a boom of research focusing on roughness measurement has been evident in the past few years. The increasing interest in exploring new measurement methods has been fueled by crowdsourcing, the effort to develop cheaper techniques, and the growing demand for collecting roughness data by new industries. The use of crowdsourcing tools, unmanned aerial vehicles (UAVs), and synthetic aperture radar (SAR) images is expected to receive increasing attention from the research community. However, the use of 3D systems is likely to continue gaining momentum in the industry.

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Pavement roughness evaluation method based on the theoretical relationship between acceleration measured by smartphone and IRI

Cited by 22 publications

References 30 publications

Design and Implementation of a Vibration-Based Real-Time Internet of Things Framework for Road Condition Monitoring

Design and Implementation of a Vibration-Based Real-Time Internet of Things Framework for Road Condition Monitoring

Estimating Pavement Condition by Leveraging Crowdsourced Data

Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review

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