Laser-based system for highway pavement texture measurement

Xiangyang, Mu; Lin, Li; Tang, Nan; Liu, Ce; Xiuhan, Jiang

doi:10.1109/itsc.2003.1252746

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…The last data source is the laser. Laser trucks employ laser scanning technology to detect surface irregularities and deterioration, which can precisely identify cracks, potholes, rutting, and texture depth [22][23][24]. Laser data acquisition often involves manual inspection or maneuver and can be labor-intensive and subjective.…”

Section: Pavement Data Sourcementioning

confidence: 99%

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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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Section: Pavement Data Sourcementioning

confidence: 99%

“…Infrequent scanning, low spatial coverage, computational and storage demanding Laser Salameh and Tsai [22], Xiangyang, Lin [23], Hildebrand, Rasmussen [24], Tsai and Li [31],…”

Section: Pavement Cracking Rutting Roughnessmentioning

confidence: 99%

Estimating Pavement Condition by Leveraging Crowdsourced Data

Gu,

Khojastehpour,

Jia

et al. 2024

Remote Sensing

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“…The sampling frequency is another important indicator of the texture measuring capability of the laser. One published study indicates that the laser sampling frequency should be great enough to handle the signal variations caused by the background fluctuations (12). Some researchers indicated that the sampling frequency of the laser should meet the requirement by the Nyquist sampling theorem (13).…”

Section: Frequency Of Pavement Texture Wavementioning

confidence: 99%

Safety Enhancement of the INDOT Network Pavement Friction Testing Prgram: Macrotexture and Microtexture Testing Using Laser Sensors

Li¹,

Noureldin²,

Zhu³

2010

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“…Recent advances are reported by Dunford (2008Dunford ( , 2010 and Dunford et al (2008) where skid resistance is predicted from parameters directly extracted from images of the road surface. Many approaches use two-dimensional or three-dimensional topographical data of the surface collected by means of laser displacement sensors or laser profile scanners (Xiangyang et al 2003, Kebrle and Walker 2007, Xie et al 2008, Meegoda 2009, Cigada et al 2010, Goubert et al 2010. Topographical data can also be generated by stereoscopic imaging.…”

Section: Introductionmentioning

confidence: 99%

A contribution to non-contact skid resistance measurement

Ueckermann

Wang

Oeser

et al. 2014

International Journal of Pavement Engineering

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Over the past decades a wide range of routine measurement devices has been developed, all of them measuring the friction force between a rubber wheel and the (wetted) road surface. At the same time, many efforts have been undertaken to predict skid resistance solely from texture measurements. This paper presents a concept of contactless skid resistance measurement which is based on optical texture measurement and consists of two components: (1) measurement of the pavement texture by means of an optical measuring system and (2) calculation of skid resistance based on the measured texture by means of a rubber friction model. The basic assumptions and equations underlying the theoretical approach are presented. Two skid resistance-measuring devices were chosen to prove the theoretical approach: one laboratory device called Wehner/Schulze machine which corresponds to a locked-wheel braking test and another, the ViaFriction w device of ViaTech AS which measures the skid resistance under controlled longitudinal slip and corresponds to ABS braking conditions. The results are very promising although in the case of the ViaFriction w device only a few surfaces could be tested. A close relation between measured and predicted friction coefficients could be found. Thus, a strong indication can be provided that skid resistance could be measured without contact in the future.

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Laser-based system for highway pavement texture measurement

Cited by 7 publications

References 4 publications

Estimating Pavement Condition by Leveraging Crowdsourced Data

Estimating Pavement Condition by Leveraging Crowdsourced Data

Safety Enhancement of the INDOT Network Pavement Friction Testing Prgram: Macrotexture and Microtexture Testing Using Laser Sensors

A contribution to non-contact skid resistance measurement

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