A Comprehensive Assessment of Highway Inventory Data Collection Methods

Jalayer, Mohammad; Zhou, Huaguo; Gong, Jie; Hu, Shunfu; Grinter, Mark

doi:10.5399/osu/jtrf.53.2.4219

Cited by 12 publications

(15 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A MLS observes roadway and nearby objects in the form of their dense coordinates at high speed (Yen et al, 2011) which makes it possible to construct a detailed 3D model of the road environment (Lehtomäki et al, 2010). A MLS achieve the highest data quality and completeness among the traditional roadway data collection methods (Jalayer et al, 2014). MLS data are used for accurate road geometry computation over existing conventional methods due to significant savings in time and human resources while yielding high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Road Surface Detection From Mobile Lidar Data

Yadav

Lohani

Singh

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. The accurate three-dimensional road surface information is highly useful for health assessment and maintenance of roads. It is basic information for further analysis in several applications including road surface settlement, pavement condition assessment and slope collapse. Mobile LiDAR system (MLS) is frequently used now a days to collect detail road surface and its surrounding information in terms three-dimensional (3D) point cloud. Extraction of road surface from volumetric point cloud data is still in infancy stage because of heavy data processing requirement and the complexity in the road environment. The extraction of roads especially rural road, where road-curb is not present is very tedious job especially in Indian roadway settings. Only a few studies are available, and none for Indian roads, in the literature for rural road detection. The limitations of existing studies are in terms of their lower accuracy, very slow speed of data processing and detection of other objects having similar characteristics as the road surface. A fast and accurate method is proposed for LiDAR data points of road surface detection, keeping in mind the essence of road surface extraction especially for Indian rural roads. The Mobile LiDAR data in XYZI format is used as input in the proposed method. First square gridding is performed and ground points are roughly extracted. Then planar surface detection using mathematical framework of principal component analysis (PCA) is performed and further road surface points are detected using similarity in intensity and height difference of road surface pointe in their neighbourhood.A case study was performed on the MLS data points captured along wide-street (two-lane road without curb) of 156m length along rural roadway site in the outskirt of Bengaluru city (South-West of India). The proposed algorithm was implemented on the MLS data of test site and its performance was evaluated it terms of recall, precision and overall accuracy that were 95.27%, 98.85% and 94.23%, respectively. The algorithm was found computationally time efficient. A 7.6 million MLS data points of size 27.1MB from test site were processed in 24 minutes using the available computational resources. The proposed method is found to work even for worst case scenarios, i.e., complex road environments and rural roads, where road boundary is not clear and generally merged with road-side features.

show abstract

Section: Introductionmentioning

confidence: 99%

Road Surface Detection From Mobile Lidar Data

Yadav

Lohani

Singh

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Laser scanning technology ( 14 ) can provide highly accurate survey data, which can be utilized to extract horizontal and vertical alignment of a curve. However, its cost is higher than that of other approaches – it was reported that the cost per mile to obtain the required highway inventory dataset for photo/video log, satellite/aerial imagery, and GPS data logger were $72, $107, and $700, respectively, while the mobile LiDAR was $915 ( 15 ). In addition, users must be familiarized with operating instructions.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification and Calculation of Horizontal Curves for Low-Volume Roadways Using Smartphone Sensors

Wood

Zhang

2018

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Horizontal curves are a contributing factor to the number of observed roadway crashes. Identifying locations and geometric characteristics of horizontal curves plays a crucial role in crash prediction and prevention. However, most states in the USA face a challenge in maintaining detailed and high-quality roadway inventory databases for low-volume rural roads due to the labor-intensive and time-consuming nature of collecting and maintaining the data. This paper proposes a low-cost mobile road inventory system for two-lane horizontal curves based on off-the-shelf smartphones. The proposed system is capable of accurately detecting horizontal curves by exploiting a K-means machine learning technique. Butterworth low-pass filtering is applied to reduce sensor noise. Extended Kalman filtering is adopted to improve the GPS accuracy. Chord method-based radius computation and superelevation estimation are introduced to achieve accurate and robust results despite the low-frequency GPS and noisy sensor signals obtained from smartphones. This study implements this method using an Android-based smartphone and tests 21 horizontal curves in South Dakota. The results demonstrate that the proposed system achieves high curve identification accuracy as well as high accuracy for calculating curve radius and superelevation.

show abstract

“…There are also vision-based applications for road surface condition monitoring and inventories. These systems use camera(s) [11] mounted on vehicles and other devices ( [11]; [6]; [7], [12] to capture videos that are used to assess the condition of roads. The vision-based approach, rely solely on video processing analysis such as texture extraction, algorithms [13], comparison of captured images, photogrammetry analysing programs [14], and other machine learning approach (Saeed et al, 2020) to depict road surface distresses.…”

Section: Introductionmentioning

confidence: 99%

Mapping of Road Surface Condition features for Unpaved Roads Through the use of Remotely Sensed Imagery from Unmanned Aerial Vehicle

Oppong-Adu

2023

Preprint

View full text Add to dashboard Cite

Road surface condition evaluation is a critical component for the operation and maintenance of road infrastructure. The situation is more heightened for unpaved roads as they lack protective hard surfaces such as concrete or asphalt. New methods are now emerging for the identification and extraction of road surface condition parameters from remotely sensed imagery. Accordingly, this study illustrates an approach to identify and extract road surface condition parameters from remotely sensed imageries obtained from unpaved roads with unique environmental characteristics. An Unmanned Aerial Vehicle (UAV) was used to capture images on unpaved roads where they were pre-processed, and analysed by building a digital elevation model (DEM) and orthomosaic. The processed imagery was validated with a conventional field measurement of road defects. The number of road defects identified and their severity was similar for both approaches. The defects counts were similar for all except one defect which vary for both approaches. The predictions of the extent of defect severity identified as percent count were exact – vegetation encroachment had the highest 100% and depression had the lowest of 25%. These findings illustrate the potential use of remotely sensed imagery from UAV to identify, extract, and measure road surface condition parameters for unpaved roads. The results can facilitate the monitoring operation of road agencies in the country towards low-cost and effective maintenance of roads.

show abstract

A Comprehensive Assessment of Highway Inventory Data Collection Methods

Cited by 12 publications

References 15 publications

Road Surface Detection From Mobile Lidar Data

Road Surface Detection From Mobile Lidar Data

Identification and Calculation of Horizontal Curves for Low-Volume Roadways Using Smartphone Sensors

Mapping of Road Surface Condition features for Unpaved Roads Through the use of Remotely Sensed Imagery from Unmanned Aerial Vehicle

Contact Info

Product

Resources

About