scholarly journals AN ASSESSMENT OF THE EFFECT OF PAVEMENT SURFACE CONDITION ON PERFORMANCE OF SIGNALISED INTERSECTIONS

2018 ◽  
Author(s):  
NASREEN HUSSEIN ◽  
RAYYA HASSAN ◽  
MICHAEL FAHEY
Keyword(s):  
Surface Condition ◽  
Pavement Surface

scholarly journals Analysis of Nonsevere Crashes on Two- and Four-Lane Urban and Rural Highways: Effects of Wet Pavement Surface Condition

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Aschalew Kassu ◽  
Michael Anderson
Keyword(s):  
Negative Binomial ◽  
Surface Condition ◽  
Negative Binomial Regression ◽  
Segment Length ◽  
Mean Values ◽  
Rural Highways ◽  
Explanatory Variables ◽  
Crash Data ◽  
Pavement Surface ◽  
Binomial Regression

This study examines the effects of wet pavement surface conditions on the likelihood of occurrences of nonsevere crashes in two- and four-lane urban and rural highways in Alabama. Initially, sixteen major highways traversing across the geographic locations of the state were identified. Among these highways, the homogenous routes with equal mean values, variances, and similar distributions of the crash data were identified and combined to form crash datasets occurring on dry and wet pavements separately. The analysis began with thirteen explanatory variables covering engineering, environmental, and traffic conditions. The principal terms were statistically identified and used in a mathematical crash frequency models developed using Poisson and negative binomial regression models. The results show that the key factors influencing nonsevere crashes on wet pavement surfaces are mainly segment length, traffic volume, and posted speed limits.

Cohen’s Weighted Kappa Statistic in Quality Control–Quality Assurance Procedures: Application to Network-Level Contract Pavement Surface Condition Surveys in British Columbia, Canada

2003 ◽  
Vol 1860 (1) ◽  
pp. 103-108 ◽  
Author(s):  
Shawn Landers ◽  
Wael Bekheet ◽  
Lynne Falls
Keyword(s):  
Quality Assurance ◽  
Surface Condition ◽  
Composite Index ◽  
Kappa Statistic ◽  
Weighted Kappa ◽  
Weighted Kappa Statistic ◽  
Testing Procedures ◽  
Pavement Surface ◽  
Surface Distress ◽  
Level Of Agreement

Like many provincial and municipal agencies, the British Columbia Ministry of Transportation (BCMoT) contracts out the collection of pavement surface condition data. Because BCMoT is committed to contracts with multiple private contractors, quality assurance (QA) plays a critical role in ensuring that the data are collected accurately and repeatably from year to year. Comprehensive QA testing procedures for surface distress data have been developed and implemented since the data collection has been based on visual ratings with event boards. Control sites that are manually surveyed are used to evaluate whether the contractor is correctly applying the BCMoT pavement surface distress rating system. To date, the QA testing has been based on a composite-index–based criterion for assessing the level of agreement and supplemented with the detailed severity and density rating data. However, the use of a composite index presents some limitations related to the model formulation and weightings assigned to particular distress types. Although the detailed ratings are useful as a diagnostic tool to pinpoint discrepancies, in the disaggregated format, they are not conducive as acceptance criteria for QA testing. Not widely used in the field of engineering, Cohen’s weighted kappa statistic has been applied since the 1960s in other areas to assess the level of agreement beyond chance among raters. The statistic was therefore identified as a possible solution for improving the ministry’s QA surface distress testing process by providing an overall measure of the level of agreement between the detailed manual benchmark survey and the contractor severity and density ratings. The application is described of Cohen’s weighted kappa statistic for visual surface distress survey QA testing using the BCMoT survey and testing procedures as a case study.

scholarly journals Pengaruh Kecepatan Kendaraan Terhadap Tingkat Kerusakan Jalan Menggunakan Metode PCI (Pavement Condition Index)

2020 ◽  
Vol 17 (2) ◽  
pp. 161-171
Author(s):  
Eko Prayitno
Keyword(s):  
Surface Condition ◽  
Condition Index ◽  
Traffic Load ◽  
Pavement Condition ◽  
The Road ◽  
Pavement Surface ◽  
Maintenance Program ◽  
Periodic Maintenance ◽  
Pavement Condition Index ◽  
Fair Poor

The pavement and pavement structure are structure consisting of one or several layers of processed materials, whose the function is to support the weight of the traffic load without causing significant damage to the construction. Pavement Condition Index (Pavement Condition Index) is the level of pavement surface condition and its size in terms of the power function that refer to the conditions and damage on the pavement surface that occurs. The Pavement Conditions Index or PCI is a numerical index that has values ​​ranging from 0 to 100 with criteria excellent, very good, good, fair, poor, very poor, and failed. The field study of this research is the road section starting from STA 310 + 000 up to 320 + 000. The assessment of road conditions according to the Pavement Condition Index (PCI), where the data was collected through field surveys. The types of damage on the ivory tip of the STA 310 + 000 - 320 + 000 are patches, crocodile cracks, holes, edge cracks, loose grains, waves and elongated cracks. Obtained pavement condition index (PCI) average is 34.6 with an assessment of the condition of road damage is bad (poor). Based on the PCI value the road is included in the periodic maintenance program.

scholarly journals Methodology for remote assessment of pavement distresses from point cloud analysis

2021 ◽  
Author(s):  
Ernest Berney ◽  
Naveen Ganesh ◽  
Andrew Ward ◽  
J. Newman ◽  
John Rushing
Keyword(s):  
Point Cloud ◽  
Surface Condition ◽  
Three Dimensional ◽  
Point Clouds ◽  
Pavement Surface ◽  
Cloud Models ◽  
Rapid Generation ◽  
Point To Point ◽  
Point Cloud Analysis ◽  
Cloud Analysis

The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-based sensors. ERDC-developed algorithms automatically discretize the pavement surface into cross- and grid-based sections to identify physical surface distresses such as depressions, ruts, and cracks. Depressions can be sized from the point-to-point distances bounding each depression, and surface roughness is determined based on the point heights along a given cross section. Noted distresses are exported to a distress map file containing only the distress points and their locations for later visualization and quality control along with classification and quantification. Further research and automation into point cloud analysis is ongoing with the goal of enabling Soldiers with limited training the capability to rapidly assess pavement surface condition from a remote platform.

scholarly journals Interval Recognition Algorithm of the Pavement Surface Condition Based on Lagrange Interpolation Method

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Ren He ◽  
Liwei Zhang
Keyword(s):  
Lagrange Interpolation ◽  
Interpolation Method ◽  
Estimation Error ◽  
Surface Condition ◽  
Recognition Algorithm ◽  
Identification Algorithm ◽  
Pavement Surface ◽  
Road Condition ◽  
Road Friction ◽  
Road Friction Coefficient

The accurate recognition of road condition is one of the important factors that influence vehicle safety performance. This paper comes up with an original mathematical method of an interval recognition algorithm of the pavement surface condition based on Lagrange interpolation. The ordinate of the peak point is solved by the Lagrange interpolation method, and the pavement surface condition is deduced by the interval identification algorithm. The simulation results from six typical roads and the varied pavement surface show that besides the cobblestone pavement which is not common in the daily road, the estimation error of the initial tire-road friction coefficient by the Lagrange interpolation method is less than 2%, the pavement surface condition can be identified by interval recognition algorithm quickly and accurately, and the response time is less than 0.2 seconds.

Investigating Asphalt Pavement Surface Condition Using Drones

2020 ◽  
Vol 22 (1) ◽  
pp. 1-8
Author(s):  
Damryung Kim ◽  
Sooho Jung ◽  
Hyungil Ga ◽  
Sungho Mun
Keyword(s):  
Asphalt Pavement ◽  
Surface Condition ◽  
Pavement Surface

scholarly journals Pavement surface condition estimation based on geospatial modelling

Annals of GIS ◽  
2017 ◽  
Vol 23 (3) ◽  
pp. 167-181 ◽  
Author(s):  
Su Zhang ◽  
Christopher D. Lippitt ◽  
Susan M. Bogus
Keyword(s):  
Surface Condition ◽  
Condition Estimation ◽  
Pavement Surface ◽  
Geospatial Modelling

scholarly journals A Review on Effect of Pavement Surface Failure on Road Traffic Accident

2020 ◽  
Vol 3 (1) ◽  
pp. 14-19
Author(s):  
Debela Deme
Keyword(s):  
Road Traffic Accident ◽  
Traffic Accident ◽  
Road Traffic ◽  
Surface Condition ◽  
Related Factor ◽  
Vehicle Speed ◽  
Pavement Surface ◽  
Road Geometry ◽  
Triggering Factor ◽  
The Impact

Pavement surface condition was parameter used to define quality of road traffic system. In this review pavement surface condition inspected were pavement friction, roughness and rutting. In order to analysis the study considers all previous paper done by researcher in specified and related title. Due to data constraints and other related issues to signify the outcome this review randomly selects forty-five researches paper. As per the examination most researchers argue that affinity of pavement surface condition for the occurrence of road traffic accident was irrelevant in respect to another related factor (Vehicle speed, Road geometry, Wet pavement surface, Pavement edge and etc.). Even though; the impact was insignificant the number of people dies and injured kin with other accident triggering factor was significant. As a result; stakeholder must play substantial role to overcome road traffic accident due to lack of proper maintenance and management of pavement surface condition.

scholarly journals Impact Of Traffic Characteristics and Pavement Surface Condition on Noise Level for Rigid Pavement

2022 ◽  
Vol 961 (1) ◽  
pp. 012101
Author(s):  
Ban Ali Kamil ◽  
Hamid Athab Eedan AlJameel
Keyword(s):  
Noise Level ◽  
Surface Condition ◽  
Traffic Noise ◽  
Low Noise ◽  
Void Content ◽  
Frequency Noise ◽  
Effective Measure ◽  
Pavement Surface ◽  
Air Void ◽  
Air Void Content

Abstract The proper design of a road’s surface layer can result in pavements that are not only better in terms of ride comfort and safety, but also in terms of noise reduction. The use of low-noise pavements may be an effective measure to reduce the acoustic pollution generated by road traffic This study aims to consider the effect of changed pavement features on the noise level. Tire/pavement noise is a major contributor to traffic noise at highway speeds. The effects of pavement properties, including air-void content, gradation properties, roughness, texture, pavement surface condition are major contributors to traffic noise at highway speeds. As the overall texture and IRI, increase noise levels. The results showed that greater air void content decreases the level of high-frequency noise.

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