Roadway Measurement System Evaluation

Hunt, John; Vandervalk, Anita; Snyder, David

doi:10.17226/14523

Cited by 4 publications

(5 citation statements)

References 1 publication

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“…The cross slope was then measured along each travel lane and compared with field surveyed cross slope data. The deviations between LiDAR-derived cross slopes and field measurements were less than 0.19%, which met SHRP2 (33) and suggested cross slope accuracies for mobile measurements ( 6 0.2%) and demonstrated that mobile LiDAR is a reliable method for cross slope verification.…”

Section: Beck Et Al (supporting

confidence: 56%

“…For both data collection methods (E2E method and 0.2ft_int point extraction), the statistical hypothesis to test is whether the mean of the deviation between LiDAR-derived cross slopes and field surveying measurement is less than 0.2%, and is performed according to LMM models using JMP statistical discovery software ( 40 ). According to SHRP2 ( 33 ) guide specifications, a slope tolerance value of ±0.2% of the design value would be acceptable for final measurement after project completion.…”

Section: Evaluation Of Resultsmentioning

confidence: 99%

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Extracting Highway Cross Slopes From Airborne and Mobile LiDAR Point Clouds

Shams

Sarasua

Russell

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Adequate water pavement surface drainage on highways is crucial in minimizing the potential of hydroplaning. Highway cross slope has a significant effect of draining water laterally from the pavement surface. Currently, field surveying techniques and other manual measurement methods are used to collect cross slope data on a limited basis in most states, despite these methods being labor intensive and exposing personnel to traffic. Furthermore, field surveying techniques cannot provide continuous data and can only be conducted at sample-based locations. This study conducted a technical evaluation of the effectiveness of airborne LiDAR (light detection and ranging) scanning and mobile terrestrial LiDAR scanning systems in measuring pavement cross slopes. Cross slope data were extracted from the LiDAR point cloud at five selected test sections using two different methods: (i) end-to-end method using elevations only from the pavement edge lines to generate the cross slope; and (ii) 0.2 ft interval point extraction along the cross-section and using a fitted linear regression line as the basis for the cross slope. Cross slopes were also measured at test section locations using conventional surveying methods and compared with LiDAR-extracted cross slopes. Results demonstrate that LiDAR methods are reliable for collecting accurate pavement cross slopes and should be considered for the purpose of cross slope verification on a braod scale such as statewide to address cross slope and pavement surface drainage issues proactively.

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Section: Beck Et Al (supporting

confidence: 56%

Section: Evaluation Of Resultsmentioning

confidence: 99%

Extracting Highway Cross Slopes From Airborne and Mobile LiDAR Point Clouds

Shams

Sarasua

Russell

et al. 2022

Transportation Research Record: Journal of the Transportation R

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“…For a highway section with a typical cross-slope of 2.08%, an allowable minimum cross-slope would be 1.73%. Using the SHRP 2 suggested slope acceptable measurement error ± 0.2% ( 10 ), which is greater than the average MLS measurement error of ± 0.19% found in this research, a cross-slope of 1.53% can potentially be considered acceptable when incorporating a ± 0.2% error. According to Figure 5, a cross-slope of 1.53% corresponds to a water depth of 0.147 inches which has a low potential for hydroplaning for vehicles traveling at highway speeds for rain fall intensities less than 2 in./h.…”

Section: Cross-slope Sensitivity Analysismentioning

confidence: 53%

“…The comparison between unadjusted LiDAR data and field surveying varies from 0% to 0.24%. With regards to SHRP2 guide specification a slope tolerance value of ± 0.2% of the design value would be acceptable for final measurement after project completion ( 10 ). The LiDAR derived point clouds on Sections 1 and 2 were adjusted using IMU measurements and through post-processing with ground control points, however, the Section 3 point cloud was adjusted only with the integrated IMU data.…”

Section: Evaluation Of Resultsmentioning

confidence: 99%

Highway Cross-Slope Measurement using Mobile LiDAR

Shams

Sarasua

Famili

et al. 2018

Transportation Research Record

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Ensuring adequate pavement cross-slope on highways can improve driver safety by reducing the potential for ponding to occur or vehicles to hydroplane. Mobile laser scanning (MLS) systems provide a rapid, continuous, and cost-effective means of collecting accurate 3D coordinate data along a corridor in the form of a point cloud. This study provides an evaluation of MLS systems in terms of the accuracy and precision of collected cross-slope data and documentation of procedures needed to calibrate, collect, and process this data. Mobile light detection and ranging (LiDAR) data were collected by five different vendors on three roadway sections. The results indicate the difference between ground control adjusted and unadjusted LiDAR derived cross-slopes, and field surveying measurements less than 0.19% at a 95% confidence level. The unadjusted LiDAR data incorporated corrections from an integrated inertial measurement unit and high-accuracy real-time kinematic GPS, however it was not post-processed adjusted with ground control points. This level of accuracy meets suggested cross-slope accuracies for mobile measurements (±0.2%) and demonstrates that mobile LiDAR is a reliable method for cross-slope verification. Performing cross-slope verification can ensure existing pavement meets minimum cross-slope requirements, and conversely is useful in identifying roadway sections that do not meet minimum standards, which is more desirable than through crash reconnaissance where hydroplaning was evident. Adoption of MLS would enable the South Carolina Department of Transportation (SCDOT) to address cross-slope issues through efficient and accurate data collection methods.

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“…For the iPhone, the cross-slope difference ranged from 0.02% to 0.69%, with an average difference of 0.19%. The acceptable accuracy for cross-slope differences, as defined by relevant literature and technical guidelines, is generally considered to be 0.2% Hunt et al, 2013). Based on the obtained results, it can be concluded that the average difference values obtained by all three methods were close to this acceptable limit.…”

Section: Accuracy Assessment Of Point Cloudsmentioning

confidence: 67%

Road Infrastructure Mapping by Using Iphone 14 Pro: An Accuracy Assessment

Suleymanoglu

Tamimi

Yilmaz

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Vital aspects of transportation networks, such as the extraction of road information and analysis of road conditions, have become increasingly important research topics as they outline the foundation of many applications such as high-precision mapping, infrastructure planning and maintenance, intelligent transportation, or road design analysis. Therefore, regularly obtaining accurate high-density point cloud data of infrastructures supports many transportation-based applications and provides up-to-date information for smart cities or digital twins. Low-cost smartphone platforms equipped with a variety of sensors provide new and powerful data acquisition capabilities that can be exploited in the geospatial field. For example, mobile phones are now capable of collecting valuable data to generate accurate models to support digital reconstruction of infrastructures. These platforms can provide simple and effective data acquisition, while offering useful geospatial data that can be an alternative to traditional measurement techniques. However, the sensor performance with respect to spatial accuracy of point clouds generated in different applications have not yet been fully investigated. Thus, this paper evaluates the feasibility of using the point clouds generated by the built-in camera and LiDAR sensors integrated into iPhone 14 Pro for extracting road-related information. Additionally, the use of the viDoc RTK Rover on the iPhone 14 Pro increases the platform positioning accuracy, consequently improving the georeferencing accuracy of the point clouds. To validate the performance of the point clouds obtained by the iPhone 14 Pro, a reference dataset of the road features was obtained by measuring with a single-point RTK-GNSS receiver, receiving corrections from the Turkish CORS network (TUSAGA-Aktif) which provides two to three centimetres of accuracy. In addition, reference point cloud data over the same area was obtained from different platforms such as Mobile LiDAR and UAS, and the road features were extracted from these dataset and performance validated. The data acquired by the iPhone 14 Pro was processed and evaluated with respect to the reference datasets. The advantages and disadvantages of using iPhone 14 Pro are analysed in detail and the findings are reported.

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Roadway Measurement System Evaluation

Cited by 4 publications

References 1 publication

Extracting Highway Cross Slopes From Airborne and Mobile LiDAR Point Clouds

Extracting Highway Cross Slopes From Airborne and Mobile LiDAR Point Clouds

Highway Cross-Slope Measurement using Mobile LiDAR

Road Infrastructure Mapping by Using Iphone 14 Pro: An Accuracy Assessment

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