Pavement roughness index estimation and anomaly detection using smartphones

Yu, Qiqin; Fang, Yihai; Wix, Richard

doi:10.1016/j.autcon.2022.104409

Cited by 25 publications

(12 citation statements)

References 70 publications

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“…The measurement of vehicle parameters is usually complicated. Many existing studies assume that the vehicle parameters are known [ 2 , 7 , 11 ] or calibrated from vehicle vibrations [ 8 , 9 ]. However, Xue et al [ 10 ] and Keenahan et al [ 16 ] assume the parameters randomly first and apply a genetic algorithm (GA) to vehicle vibration data to simultaneously estimate the road profile and vehicle parameters.…”

Section: Road Unevenness Estimationmentioning

confidence: 99%

“…In addition, as a road profiler scans road surfaces slowly, the required measurement time is relatively long [ 1 ]. The latter has no such problem but is extremely expensive [ 2 ]. In developing countries such as Africa, large-scale road construction is underway, and a more efficient road evaluation method is required.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, some methods have been proposed to deal with vehicle speed fluctuations. These schemes can be classified into (1) correction coefficients, (2) regression parameter variations, and (3) high-pass filters [ 2 ]. However, as the vehicle usually stops, there is a limitation in mitigating the effect of vehicle speed using the correction factor.…”

Section: Introductionmentioning

confidence: 99%

“…Numerical simulations have been conducted to simulate vehicle vibration changes due to speed fluctuations and to evaluate the IRI from the obtained vehicle vibration with high accuracy [ 13 , 15 ]. As Yu et al [ 2 ] point out, Moghadam [ 15 ] did not investigate the responses under the condition of speed fluctuations. In addition, Wang et al [ 13 ] have not been able to consider realistic speed fluctuations yet.…”

Section: Introductionmentioning

confidence: 99%

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Practical Application of Drive-By Monitoring Technology to Road Roughness Estimation Using Buses in Service

Yamamoto

Shin

Sakuma

et al. 2023

Sensors

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The efficiency of vehicles and travel comfort are maintained by the effective management of road pavement conditions. Pavement conditions can be inspected at a low cost by drive-by monitoring technology. Drive-by monitoring technology is a method of collecting data from sensors installed on a running vehicle. This technique enables quick and low-cost inspections. However, most existing technologies assume that the vehicle runs at a constant speed. Therefore, this study devises a theoretical framework that estimates road unevenness without prior information about the vehicle’s mechanical parameters even when the running speed changes. This paper also shows the required function of sensors for this scheme. The required ability is to collect the three-axis acceleration vibration and position data simultaneously. A field experiment was performed to examine the applicability of sensors with both functions to the proposed methods. Each sensor was installed on a bus in service in this field experiment. The vehicle’s natural frequency estimated from the measured data ranges from 1 to 2 Hz, but the natural frequency estimated by the proposed method is 0.71 Hz. However, the estimated road unevenness does not change significantly with changes in the vehicle’s estimated parameters. The results found that the accuracy of road unevenness estimation seems to be acceptable with the conventional method and the new method. Future work will include improving the algorithm and accuracy verification of the schemes.

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Section: Road Unevenness Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Practical Application of Drive-By Monitoring Technology to Road Roughness Estimation Using Buses in Service

Yamamoto

Shin

Sakuma

et al. 2023

Sensors

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show abstract

“…Mu´cˇka (31) provided an overview of relations between the seat surface vibrations and the IRI. The current trends are to use smartphone accelerometers for IRI estimation (32)(33)(34)(35)(36)(37)(38)(39). Yu et al (33) presented an overview of the current literature on relationships between the IRI and acceleration measured by smartphones.…”

Section: Estimation Of the International Roughness Index From Vehicle...mentioning

confidence: 99%

Relation Between Seated Person Vibrations and the International Roughness Index

Múčka

2023

Transportation Research Record: Journal of the Transportation R

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Vertical vehicle feet/floor interface vibrations are typically not used to indirectly estimate road roughness. The relation for vehicle floor vibrations, the International Roughness Index (IRI), vehicle speed, and road roughness vertical displacement spectrum according to the ISO 8608: 2016 standard was previously derived only for the simulation of a reference quarter-car model. It was estimated using the in situ measurement of vibrations of a person sitting in a passenger car for different vehicle and road categories, vehicle speed, and road roughness. Vibrations at the feet/floor interface and seat surface of a seated passenger were evaluated with the frequency-weighted root-mean-square vertical acceleration according to the ISO 2631-1: 1997 standard. The results indicated that the simulated results for the quarter-car model may have substantially underestimated the ratio between the vehicle floor vibrations of a seated person and the IRI, but they were similar to seat surface vibrations.

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Road Roughness Detection Based on Discrete Kalman Filter Model with Driving Vibration Data Input

Wang

Miao

et al. 2023

Int. J. Pavement Res. Technol.

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Pavement roughness index estimation and anomaly detection using smartphones

Cited by 25 publications

References 70 publications

Practical Application of Drive-By Monitoring Technology to Road Roughness Estimation Using Buses in Service

Practical Application of Drive-By Monitoring Technology to Road Roughness Estimation Using Buses in Service

Relation Between Seated Person Vibrations and the International Roughness Index

Road Roughness Detection Based on Discrete Kalman Filter Model with Driving Vibration Data Input

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