Lidar for Road Inventory

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Introduction Grade and cross-slope of the roadway influence the operational characteristics of vehicles. Heavy vehicles operation is mainly impacted by the roadway grade, which affects their stopping and passing sight distance. Emission characteristics are also influenced by the roadway grade. Cross-slope of the road segments influences the drainage across the road pavement and can affect vehicle movement along the roads. Presently available road inventory databases do not accurately measure the existing grade for it to be useful in analying the effect on the issues described above. !he grade and cross-slope values are usually grouped into intervals for ease of storage and analysis. !he grade and cross-slope of the roadway can change with the use of the facility mainly due to settling of the pavement and structural failures. !he grade and crossslope in a facility could also be different from the design specifications specifications and hence to assess the functionality and the safety of a facility the current grade and cross-slope are important elements of analysis. Cross slope is often a compromise between the need for a relatively steep cross slope for drainage and a relatively flat cross slope for driver comfort. "#$ %n %owa, two-lane highways usually have a cross slope of &.'(. !he outside lanes in multi-lane facilities usually have a cross slope of ).'(. !he cross-slope is varied to allow the water to drain across the pavement. !he grade along the roadway is necessary to avoid stagnation of water hence a minimum grade of '.* ( is re+uired along road sections. urface models created from ight etection and /anging 0%1/2 can be used to determine the artifacts defining the roadway condition which include the cross-slope, grade and surface roughness. !his form of remote sensing can be crucial in rapid data collection and analysis for developing a timely maintenance and inventorying procedure. !he ob3ective of this research is to estimate the roadway characteristics from %1/ data by developing regression models relating the elevation changes with the grade and the cross-slope of the road segment. Presently, the ma3or concern is to correctly identify the points defining the pavement surface from the point cloud for accurate analysis. Use of LIDAR data /oad inventorying is a difficult and prolonged process re+uiring on-site presence. !he tas4 of management of these widely spread networ4s becomes even more challenging due to the weather conditions, which ma4e it difficult for on-site measure measurements. ments. 5n-site surveys are time consuming and are a safety ris4, as data collectors have to be very close to the vehicles using the facility. urface models created from %1/ data are useful in visualiing the entire study area and can potentially minimie ground survey for road inventorying.

Collecting Road Inventory using LIDAR surface models  Data Description: %1/ data and #&- inch resolution orthophotos were collected for the %owa highway # corridor in 5ctober  &''#. %n addition to the %1/ dataset and #&-inch orthophotos, a set of 6-inch resolution orthophotos was available with the 5!. 1 commercial vendor for the pro3ect collected data. !he density of %1/ points in the dataset was # every &7 s+uare feet. !he vendor also provided the gridded E8 of the area with 9 feet postings.  1 G% street database was also available from the 5ffice of !ransportatio !ransportation n ata, ivision of Planning and Programming Program ming at the %owa 5!. G%8 G%8 dataset contains roadway characteristics for all public roadways in the state of %owa, such as lane width, grade, traffic volume, surface and shoulder type 0C!/E final report, 8ay &''#2. Methodology: !en test segments were selected along the %owa # corridor as shown in figure #. even straight segments were selected which avoided horiontal or vertical curves. !wo segments were chosen along locations with a vertical and horiontal curve and one segment was chosen along a vertical curve. :igure & shows the location of the road segments selected for the analysis.

 

Figure 1: Location of the test segments along Iowa Highway 1

east s+uares regression was used to calculate grade and cross-slope. ;sing regression analysis, a plane was fit to the dataset in +uestion. %t was theoried that grade and cross-slope could be determined by fitting a plane to %1/ data from roadway sections with constant grade and cross-slope 0lane-groups2. 1s a result, each &-lane roadway segment was defined by two planes delineated by the center of the roadway crown and the edge of pavement. houlder sections were evaluated separately, since cross slopes are fre+uently differe diff erent nt than the roadway roadway cross slop slope. e. Con Conse+u se+uentl ently y four section sections s were analyed analyed on each each roadway roadway segment. !he %1/ data consisted of a randomly spaced point cloud with average point density of # point per &7 s+uare feet. %n order to satisfy the minimum number of %1/ points for assuming a normally distributed datas dataset et for least least s+ s+uar uare e regr regress ession ion analy analysis sis,, each each sectio section n was was #& #&' ' feet feet in le leng ngth. th. !h !his is leng length th was determined by considering the average density of %1/ points throughout the corridor and the lane width allowing ade+uate number of points to be used for regression analysis. ength

<

)'

=

08inimum

width

>

density2

!he number )' is the minimum data points re+uired for assuming normal distribution. 0/ule of thumb2 onger  sections would be preferred as the number of points for regression analysis will increase, but the increased length of the segment may be unsuitable as only monotonously increasing or decreasing sections can be estimated by using linear regression. Delineation of Road Sections: !he lateral extent of each roadway section boundaries of each roadway section 0& lane sections and & shoulder sections for each segment2 were necessary to determine which of the %1/ data points corresponded to a particular section. %n order to define lane and shoulder regions, the location of the edge of pavement, centerline, and edge of shoulder was necessary. !he four individual sections for each segment included? northbound shoulder 0@2 northbound pavement 0@P2 southbound pavement 0P2 southbound shoulder 02 • •

• •

/oadway boundaries were defined using each of three different datasets including the 6-inch resolution orthophotos, #&-inch resolution orthophotos and a surface model. !he surface terrain model was created from the %1/ !heofpoint cloud randomly spaced %1/ for %owaaHighway # corridor, had an average pointdata. density # point perfrom &7 s+uare feet, was used to develop triangular irregular which networ4 0!%@2 using the patial 1nalyst module in 1rcAiew ).&B. !he surface model was tested since it is a direct product that would be available with any %1/ data collection effort. 1erial images are fre+uently ta4en in

 

con3unction with %1/ data collection and can be planned to meet desired resolution re+uirements for final orthophotos, but add to the cost and re+uire additional processing. !herefore, the ability to use the surface model alone to determine roadway boundaries would be the ideal situation. Each of the three datasets was used individually to create polygons that defined each region of the roadway segment. !he polygons were then used to select the %1/ points that corresponded to each section by polygon overlay to be used in the regression analysis. :igure ) illustrates the use of each dataset to create boundary polygons.

Figure 3: Roadway delineation from: a) 6-inch Orthohoto !) 1"-inch Orthohoto c) #I$ from LI%&R

ith the surface terrain model, the outer edge of the shoulder was the only feature that could be visually determined. 1s shown in :igure )0c2, only a rough outline representing the entire roadway was available using the !%@. !he outer of the shoulders established asarea the outer edges outline. !he centerline of the road wasedges determined by finding were the midpoint of the enclosed by of thethe outer edges of the shoulder. %nformation relating to the lane width and the shoulder width were +ueried from G%8 database for each of the #' test segments. 5nce a centerline was established, pavement edges were determined by adding the lane width for each section from the G%8. !he centerline and pavement edges defined the polygon for the northbound pavement and southbound pavement sections. %n the #&-inch resolution orthophotos, the shoulder edges could be determined for all #' segments, but the centerline could not be consistently identified in the images. hen the centerline was not readily identifiable, it was estimated by finding the midpoint from the delineated outer edge of the shoulders. Each of the four roadway sections was determined for each segment using the #&-inch resolution orthophotos. !he edge of pavement, centerline, and edge of shoulder were clearly visible for each of the #' segments in the 6-inch resolution orthophotos. Conse+uently for the 6-inch orthophotos, the boundaries of each of the four sections 0@, @P, P and 2 were defined using the images alone.

Figure ': (omarison of road segments deried !y using the three !aselayers

 

Utility of Remote Sensing and GIS: /oad inventorying is a difficult and time-consuming process re+uiring on-site presence. !he tas4 of management of these widely spread networ4s becomes even more challenging due to the weather conditions, which ma4e it difficult for on-site measurements. ata collection by remote sensing has made it possible to !he results from the regression analysis from the %1/ points identified interactively will be compared to the results by using the G%8 metadata for pavement and shoulder width while using the centerlines derived interactively. Regression Analysis !here exists a very high correlation between the points in the %1/ point cloud even though in absolute terms the %1/ measurements have #9 inches of /8E. ")$ !he correlation between the %1/ points is suitable for determination of cross-slope and grade as these are relative measures of the elevation at different points on the roadway. !he elevation values are regressed with distance from the centerline and the distance along the segment is used for analysis. !he co-efficients of these independent variables give the cross-slope and the grade.

Figure *: Regression &nalysis

!he form of the regression e+uation was? D < b## F b&& F e where? D < elevation b# < coefficient for cross-slope # < perpendicular distance from centerline b& < coefficient for grade & < distance along the roadway :or validation, a slope meter will be used on site to measure the cross-slope and the grade in the road sections analyed. Results !he results from regression analysis vary with the points considered for estimation of the cross-slope and grade. %dentification of the points defining the pavement and shoulder is crucial for accurate analysis as otherwise the results would be s4ewed. 1 coo4boo4 has been prepared for performing the steps for roadway identification and regression analysis. !he residuals for grade and cross-slope estimated using the surface model to create polygons for egment  are plotted in :ig 6 and 7. 1s shown, the residuals are within the /8E value for vertical accuracy of '.* feet as denoted by the vendor. !his indicates that the relative measurement between points maybe less that the absolute error so that the use of regression analysis is viable. !he goodness of fit of the estimated plane with the %1/ points is shown in !able * for all four roadway and shoulder sections for each segment. a!le ": Regression results #R$% &or 'ach Section Calculated Using Surface Model to Define (oundaries# Scenario

%$Segment Section

A

B

C

D

E

F

G

H

I

J

 

 Northbound Shoulder Shoulder 0.454 0.5! 0." 0.!#$ 0.!$ 0.!!5 0.4! 0.!!$ 0.%&% 0.%5$  Northbound '()ement 0.554 0.5&" 0.#

0.!%

0.!!# 0.!!$ 0.40 0.!!& 0.%!% 0.%%

Southbound '()ement 0.5$& 0.&4# 0.4#! 0.4"% 0.!! 0.!!4 0.$#

0.!! 0.5&" 0.%4$

Southbound Shoulder 0.&&! 0.5$% 0.# 0.!4% 0.!!# 0.!!# 0.0$ 0.!! 0.&&4 0.&# 5f the *' observations,  are less than '.9. :or only two of the sections 0I shoulder for egment G and @I Pavement for egment C2 were the results poor enough that they indicate that a plane could not be fit to the data. !his could be due to the errors induced while selecting %1/ points defining the roadway regions or due to abrupt changes in the grade and cross-slope in the road sections or systematic errors in the %1/ data. However, for the ma3ority of the sections, the /& values were greater than '.J 0&' sections2 and #9 of those had an /& over '..!herefore for most of the sections, a plane could be fit to the data fairly well. Issues in Analysis: !wo issues were found to complicate the use of %1/ to extract grade and cross-slope. :irst, in order to provide a confident estimate of gradient using regression planes, a minimal number 0)'2 of points are re+uired. However, due to the relative sparseness of %1/ points falling on paved lanes, it is necessary to use long segments. However, as pavement within long sections can vary in gradient, it is desirable to minimie their length for analysis. 1 reasonable compromise was reached at #'' feet 0)' meters2. econd, partly because spatial accuracy and precision of the %1/ derived surface model is limited, and partly because we lac4 brea4lines at the edge of the paved surface, data are suspect near the edges and crown of the road. !o overcome this problem, we define slightly smaller rectangles of road surface, rectangles for which we have higher confidence in representing actual grade and cross slope measurements 0as defined in scenarios # and & of the following table2. Scope for future )or*: •





 1ll the sections sections of the roadway roadway can be estimated estimated by u using sing regr regression ession analysis analysis if the centerline centerline is clearly defined. Irea4lines would be used with the !%@ for better identification of the roadway edges. !he length of road segment that can be used for analysis and visualiation has to be determined for optimal accuracy. :or visualiation, A/8 can be used to view the output of the regression analysis.

References:   References: •



Effects of /oadway Geometrics on ;rban Pavement rainage, 5ffice of esign, %owa 5!. Eaglescan ocumentation for %1/ data collection.

 

Mo!ile Laser Scanning and +ideo Capture

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Applications Safety Fence Assess Assessment ment

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Topographic Mapping

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Surface Profiling & Drainage

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Roads and ,ther Infrastructure Data  1 digital digital elevation model 0E82 0E82 lets us get at at drainage, drainage, a power powerful ful piece of infrastructure infrastructure data data that can be be extracted from i1/.hat else is thereK e could identify all the roads in the sample area.e could see the pavement area and, with suitable hyperspectral scanners, probably measure road surface and condition as well.Iut could we extract accurate ) road centerlines from the i1/ data 3ust as we can extract drainage lines from the E8K  1t the % conference conference in 8elbourn 8elbourne e % found that that 8erric4 now 8erric4 now has an 1ustralian connection via 8erric4 8ars i1/ of Irisbane, Lueensland, 1ustralia. 1t the stand % had some time to Mchew the fatM with Gary 5utlaw, 8erric4Ns ;-based AP for business development, and had a loo4 at their 81/ software.!he conversation confirmed what % suspected - it is possible to extract a ) road centerline from the same i1/ data my forester friend was extracting products specific to forestry. Having a ), accurate road centerline dataset would be of immense value to the state of !asmania.!ransportation logistics could be computed from real road slope distances rather than the planimetric distances currently being used.!ransportation models could ta4e into account the hilly nature of !asmania, and model actual truc4 behavior 0e.g., slow speeds while ascending a hill or descending under airbra4es2.!o these data we could then add statutory road speeds, traffic lighting, one-way attribution, etc., to get a truly useful dataset that would drive economic efficiency.

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