Road condition evaluation using the vibration response of ordinary vehicles and synchronously recorded movies

Nagayama, Tomonori; Miyajima, Akira; Kimura, Shunya; Shimada, Yuuki; Fujino, Yozo

doi:10.1117/12.2010074

Cited by 21 publications

(16 citation statements)

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“…An original VIMS consists of accelerometer and many other component that are connected to each other via cables. A further development has introduced a smartphone gyroscope to help redefine VIMS 14) . Despite many merits, there are still some limitations that are hindering the use of it.…”

Section: Related Workmentioning

confidence: 99%

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A Model for the Estimation of Road Roughness Condition From Sensor Data Collected by Android Smartphones

Douangphachanh

Oneyama

2014

J. JSCE, Ser. D3

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Road roughness condition information is very important for the maintenance planning and management of road infrastructure. Collecting such information is time and resource consuming because it usually requires either, if not all, skillful manpower, substantial budget, and sophisticated equipment. Therefore, collecting and updating road roughness condition is viewed as a big challenge for many road authorities. The final goal of our research is to explore for a low cost and easy to use approach for the aforementioned purpose by using Android smartphones to estimate road roughness and traffic conditions. In this study we develop a model based on a relationship function of smartphone sensor data and road roughness condition. The relationship function is drawn from our experiment in Vientiane, Laos, in which we have found that the acceleration data from smartphones has a linear relationship with road roughness condition and the significant of the relationship also partly depends on speed. The most important and unique feature of the model is the ability to estimate road roughness condition using data obtained from anonymous drivers. Preliminary numerical examples and simulations using real sensor data have been carried out whereas the findings show that performance of the model and results are very promising.

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Section: Related Workmentioning

confidence: 99%

“…Despite many merits, there are still some limitations that are hindering the use of it. Main limitations include the need for 2 separate lengthy calibrations and its component setting, which is still practically inconvenient for the users 14) .…”

Section: Related Workmentioning

confidence: 99%

A Model for the Estimation of Road Roughness Condition From Sensor Data Collected by Android Smartphones

Douangphachanh

Oneyama

2014

J. JSCE, Ser. D3

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“…In civil engineering, mobile computing has been recognized as an important utility for realizing efficient information sharing, design, and construction collaboration. Nagayama et al built a video-based application with simultaneous acceleration recordings for road condition assessment [35]. By using accelerometer, GPS, and magnetometer sensors on smartphones, a nonintrusive method was introduced by Bhoraskar et al to detect city traffic and road condition [36].…”

Section: Engineering Applicationsmentioning

confidence: 99%

Mobile Imaging and Computing for Intelligent Structural Damage Inspection

Chen

2014

Advances in Civil Engineering

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Optical imaging is a commonly used technique in civil engineering for aiding the archival of damage scenes and more recently for image analysis-based damage quantification. However, the limitations are evident when applying optical imaging in the field. The most significant one is the lacking of computing and processing capability in the real time. The advancement of mobile imaging and computing technologies provides a promising opportunity to change this norm. This paper first provides a timely introduction of the state-of-the-art mobile imaging and computing technologies for the purpose of engineering application development. Further we propose a mobile imaging and computing (MIC) framework for conducting intelligent condition assessment for constructed objects, which features in situ imaging and real-time damage analysis. This framework synthesizes advanced mobile technologies with three innovative features: (i) context-enabled image collection, (ii) interactive image preprocessing, and (iii) real-time image analysis and analytics. Through performance evaluation and field experiments, this paper demonstrates the feasibility and efficiency of the proposed framework.

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“…This is the first study to relate statistics of the RIF to the precision of deterioration forecasts. Related studies use the output of inertial sensors to estimate the IRI by calibrating the acceleration responses of individual vehicles to known values (Nagayama, et al 2013), or by estimating parameters of an IRI model using neural networks and other methods (Dawkins, et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Precision Bounds of Pavement Deterioration Forecasts from Connected Vehicles

Bridgelall

2015

J. Infrastruct. Syst.

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Transportation agencies rely on models to predict when pavements will deteriorate to a condition or ride-index threshold that triggers maintenance actions. The accuracy and precision of such forecasts are directly proportional to the frequency of monitoring. Ride indices derived from connected vehicle sensor data will enable transformational gains in both the accuracy and precision of deterioration forecasts because of very high data volume and update rates. This analysis develops theoretical precision bounds for a ride index called the road impact factor and demonstrates, via a case study, its relationship with vehicle suspension parameter variances.

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Road condition evaluation using the vibration response of ordinary vehicles and synchronously recorded movies

Cited by 21 publications

References 0 publications

A Model for the Estimation of Road Roughness Condition From Sensor Data Collected by Android Smartphones

A Model for the Estimation of Road Roughness Condition From Sensor Data Collected by Android Smartphones

Mobile Imaging and Computing for Intelligent Structural Damage Inspection

Precision Bounds of Pavement Deterioration Forecasts from Connected Vehicles

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