A 3-D VR MODEL FOR OPTIMAL ALIGNMENTSEARCH SYSTEM OF HIGHWAY DESIGN (OHPASS)
USING ASTER GDEM
Motoya YAMASAKI 1, Masaharu INOUE2, Takayuki FUKASE3,
Ichizuru ISHIMOTO4, Ippei IWAMOTO5, Shunta SHIMIZU6¹Professor, Faculty of Regional Environment Science, Tokyo University of Agriculture, 1-1-1,
Sakuragaoka, setagaya-ku, Tokyo, Japan, m3yamasa@nodai.ac.jp²Road Group, NEWJEC Inc., 2-3-20, Honjo-Higashi, Kita-ku, Osaka, Japan³Road Group, NEWJEC Inc., 2-3-20, Honjo-Higashi, Kita-ku, Osaka, Japan
4

Nippon Koei Co., Ltd. Overseas Consulting Administration

5

Nippon Koei Co., Ltd. Overseas Consulting Administration

6

FORUM8 Co., Ltd. VR Support Group Deputy-manager

ABSTRACTThis study developed a function in OHPASS, Optimal Highway Path Automatic Search System, toallow it to use ASTER Global Digital Elevation Model (ASTER GDEM) that covers all the
topographic data in the world. In addition, we have constructed a system that enables output files fromOHPASS to be viewed and evaluated as a three-dimensional (3D) virtual reality (VR) model. The
system can be used for landscape evaluation and road structure evaluation in road alignment planning,
and for consensus building with concerned parties throughout the world using OHPASS.
KEY WORDS
astergdem, road design, virtual reality, genetic algorithms
1. INTRODUCTION
Construction of ideal highways needs satisfying various demands such as reduction of construction
cost, restrictions on the road structure, and conservation of natural environments and good highway
landscape. Many studies have been performed on the development of automated models for
optimizing highway alignments design.
First development of an automatic model for highway alignment optimization required grasping main
costs, developing efficient algorithms, and linking it with the actual Geographic Information System(GIS). "Main costs" usually refer to the expenditures each of which occupies relatively higher
percentage of the total cost. The model for highway alignment optimization (Jong, 1998; Jha, 2000)
optimized highway alignments by minimizing the total cost.
It is also suggested that the following characteristics are essential for an excellent model for highwayalignment optimization (Jong, 1998):[3](1) Examines all the main costs and variable costs,(2) Formulates all the important restrictions,(3) Generates realistic alignments,(4) Capable of treating alignments accompanied with backward bending,(5) Optimizes horizontal and vertical alignments at the same time,(6) Finds the globally or most globally appropriate solution,(7) Has efficient solution algorithms,(8) Has a continuous search space,(9) Considers the costs for intersections, interchanges, bridges, and tunnels,(10) Automatically avoids inaccessible areas, and(11) Compatible with GIS.
For optimizing highway alignments, a number of conventional optimization methods have been usedsuch as the variational method, the dynamic planning method, numerical search, the alignment
planning method, and network optimization (Howard et al, 1968; Thomson and Sykes, 1998; Shawand Howard, 1981, 1982; OECD, 1973; Turner and Miles, 1971; Turner, 1978; Athanassoulis andCalogero, 1973; Parker, 1977; Trietsch and Handler, 1985; Trietsch, 1987a, b; Hogan, 1973; Nicholson
et al., 1976).[2][3][4] However, most of these methods lacked one or more characteristics of the
highway alignment optimization model indicated above.
Genetic algorithms [3][4] have proven to be effective for optimizing highway alignments, particularly
as the optimization of horizontal (planar) and vertical alignments can be performed effectively at thesame time. This is performed by searching for a better solution through continuous generation as well
as by utilizing the whole search space without stopping at a local solution. This algorithm enables
optimization of both horizontal and vertical alignments at the same time. It is possible to generate aflat and continuous alignment (e.g. not a corridor but an accurate linear shape), while directly linking
the generated alignments to an actual GIS map.
The Optimal Highway Path Automatic Search System (OHPASS) has been created for the purpose of
performing quantitative evaluation on a large number of alignments that are unable to be examined byconventional road design methods for highway alignment plans [3][4][5]. This study reports a new
approach that visualizes an optimized alignment by linking ASTER Global Digital Elevation Model(ASTER GDEM) that covers all the topographic data on earth to OHPASS, as well as by linking 3DCAD to a VR system at the same time (i.e. constructing a system for highway alignment optimizationand landscape simulation.)
2. OVERVIEW OF ASTER GDEM
(1) Effectiveness of using ASTER GDEM data
ASTER Global Digital Elevation Model (GDEM) is an easy-to-use, highly accurate DEM that covers
all the land on earth, available to all users irrespective of size or location of their target areas. Anyone
can use the ASTER GDEM without difficulty to display a bird's-eye-view map or run a flight
simulation, realizing visually sophisticated maps. By utilizing the ASTER GDEM as a platform,
institutions specialized in disasters, hydrology, energy, environmental monitoring etc. can perform ahigher degree of analysis.
(2) Specifications of ASTER GDEM data
Released in 2009, ASTER GDEM is Global DEM for all the land area covered by ASTER. Accuracy
is enhanced to 30m per pixel by using multiple ASTER images over the same area. Table 1 shows the
specification of data.

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Table 1 - Specifications of ASTER GEDMASTER GDEM

SRTM3*GTOPO30

Data source

ASTER

Space shuttle radar

From organizations the world overin possession of DEM data

Generation anddistribution

METI/NASA

NASA/USGSUSGS (US Geological Survey)

Posting interval

30m

90m

1000m

DEM coverage

83 degrees north 83 degrees south

60 degrees north 56 degrees south

GlobalFigure 1 - System configuration

3. LINKING 3D-CAD, ASTER GDEM, AND A VR SYSTEM(1) System configuration
We have developed a mechanism for creating 3D highway shapes and producing drawings with a data
input and creation function in OHPASS. Figure 1 shows the system configuration diagram.
This system has been developed based on AutoCAD Civil 3D (Civil 3D), a civil engineering CAD
software. The functions developed include a function to create 3D terrains and control points that are
input data using Civil 3D, and a function to incorporate the calculated results into Civil 3D.
Civil 3D has functions that include loading DM, creating 3D representations of terrain, road design(horizontal design, vertical design, and cross-sectional design). In addition, we link the system with
UC-win/Road (produced by FORUM8 Co., Ltd.), one of the 3D-model VR systems. UC-win/Road iscapable of providing various real-time presentations with CG after generating three-dimensional VR
spaces, having been utilized for landscape studies, design consultation, and project briefings. It also
has a data exchange function with Civil 3D used in this study, enabling linkage experiments without
developing special software.

3(2) Terrain data conversion and the establishment of control points (OHPASS Input Interface) :In order to efficiently create input data for OHPASS, we developed an interface for converting terrain
data, setting up control points, and then passing them into OHPASS.
a) Terrain data conversion function
In OHPASS, terrain is mesh datum. A 2m mesh is required for calculations, while a 20m mesh isrequired for display. For this reason, we have developed a function to create both 2m and 20m meshfiles from a 3D surface model created with Civil 3D. While Civil 3D normally provides an API(Application Program Interface) to export the elevation data of points that are specified in the 3D
surface, we have increased the speed and efficiency of this process (e.g. by skipping unnecessary
elevations when the file size for terrain data is too large.)b) Control point set-up function
In OHPASS, control points are expressed using lines (for railroads, rivers etc.) or areas (for houses,
prohibited planar areas, etc.). We have developed a function to add the information in OHPASS
control points to the lines (unclosed polylines) or polygons (closed polylines) created by the standard
CAD functions of Civil 3D.
(3) Loading optimal results (OHPASS Output Interface)
We have developed a function that loads the examined results of alignments obtained in OHPASS anddisplays them as horizontal and vertical drawings. As a feature of OHPASS, it is possible to examine
two or more alignments by changing design conditions; this time, a function for selecting and loadingan alignment has been added so that loading can be performed with multiple results.

4. DEMONSTRATION EXPERIMENT 1 (USING ASTER GDEM)(1) Purposes and Procedures of the Experiment
The benefits of using ASTER GDEM are the labor saving effect in creating terrain data andestablishing control points. The labor saving effect is evaluated on the assumption that it is comparedwith a case where a terrain datum is provided simply as a two dimensional CAD data.
a) Labor saving in the process of creating 3D terrain data
Since the contour lines include digital elevation data, time to create a new three-dimensional data is
saved. In case height information other than contour lines is required, it is necessary to add the height
information separately. However, ASTER GDEM, which is the target of this system, is based on amesh of 30m/pixel; thus it is assumed that adequate design accuracy can be obtained in threedimensional representation only by contour line information.
b) Labor saving in the process of establishing control points
We set up control points assuming that ASTER GDEM data are classified into layers by map element
type and loaded into the CAD system.
Figure 2 shows the procedure of the experiment.
1. Road design experts list up evaluation items to evaluate control points and design results to consider
in designing roads.
2. Optimal design calculation is conducted for the actual object.
3. The results are summarized and evaluated.

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Figure 2 - Procedure for optimal highway design using ASTER GDEM
In 2.(a) in Figure 2, as it was expected to be difficult to select an object for which a topographic map
had been organized with ASTER GDEM out of the past design objects, we selected an object for
which a topographic map was organized as CAD data, assuming that the ASTER GDEM loading
function implemented in this system would be simulated. In (b), we loaded the topographic map forthe object selected in (a) into AutoCAD Civil 3D, and edited layers etc. so that it would be in a state ofbeing loaded with ASTER GDEM loading function, in order to simulate the ASTER GDEM loading
function. Then, we created 3D terrains and control points using the functions developed in this study.
In (c), we specified geometric conditions and earthwork conditions etc., and performed calculation for
optimal highway design. In (d), we loaded the result of the optimal design calculation and created a
3D highway model of a road and its drawings.
(2) Subject
The object selected as an experimental subject was a highway outline design of a partial section of anArterial High-Standard Highway. Its overview is as follows:(a) Designed daily traffic volume (H32): 16,300-21,800 vehicles/day(b) Highway standard: 1st class, 2nd grade(c) Design velocity: V=100km/h(d) Design length: L=21.8km(e) Structure width: W=20.5m, Four lanes on completion(f) Design classification: highway outline design (B)(g) Design year: 2003(h) Planned as a completed section(i) Design work has been performed with a plan of S=1/2500
In this demonstration, using the entire section (total length L=21.8km) of the above design object
would cause a large load to be applied to the computer. Therefore, aiming at easiness data handlingand saving calculation time, we extracted a section of about 4km as the subject of the experiment.
(3) Creating 3D-mesh terrain and establishing control points(a) Loading CAD data
Since the CAD data for the subject was in DWG format, which is the same as the standard format for
3D CAD (Civil 3D) used in the experiment, loading was performed in success without any problem.
(b) Editing CAD data to simulate loading of ASTER GDEM
We edited the loaded data in process (a) so that they would be just similar to the loaded ASTER
GDEM. ASTER GDEM loading function in Civil 3D classifies the data into layers according to theirmap element type while loading. This approach is considered common for loading ASTER GDEM
data into a CAD system. Since this study simulates the state after loading, the data was divided intolayers by map element type after loading CAD data.
In the original CAD data, only the contours had height information. We decided not to provide
additional height information because it was assumed that the height information in the ASTER
GDEM output data would be contours only, and that the height given by contour lines provides
adequate accuracy for the highway design of the subject.

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(c) Establishing control points
Control points were established using the control point function explained in the above section "3.
LINKING 3D-CAD, ASTER GDEM, AND A VR SYSTEM"(4) Results of the optimal alignment search
The optimal road alignment calculation system searches for the best alignment within a width of 200m
from left to right of the initial alignment. In this study, we searched for three routes: Plan A, B, and C.
Plan A - Northern route: This route passes through a northern area and is optimized with the actual
design as the original alignment. It is used to compare the actual design (initial alignment) to the
optimal calculation results.
Plan B - Central route: This route makes a southern detour around a natural woodland (environmental
protection area) that coexists with a nearby population.
Plan C - Southern route: This route passes through the mountainous area in the south, avoids northernareas that are prone to landslides or considered environmental protection areas.
Figure 3 shows the schematic searching ranges for each plan. The upper drawing of Figure 3 shows
the schematic searching ranges while the lower one shows the optimal routes searched through genetic
algorithm (GA).

Figure3 - Search range of each route
Table 3 shows a cost comparison. Each of these three plans satisfies the geometric conditions. Plan A
is the result of optimization by searching within a range 200m wide on both sides along the initialalignment and combining this with a cost analysis. As expected, when comparing the construction
cost of the original plan to that of Plan A, Plan A costs less. However, there are still some problems.
One problem is that the alignment of the actual design was partially a cross section of a dual
carriageway, which was approximated by a single lane. Also, since a section of 4km was extracted outof the total length of 21.8km for the actual design, the volume of earth was not perfectly calculated.

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Table 3 - Comparison of construction costs
Item
Actualdesign RouteA RouteB RouteC
Earthwork
4,150
4,190
4,270
3,500
230+260+240+300 560+400 400+460 640+380
BridgeTunnel
0
0
0
900
Total
5,180
5,150
5,130
5,420
24,954 20,529 23,277 16,219
Project Road (earth work)
Bridge53,923 49,224 51,970 46,343
costs(1,000,000
Tunnel000 39,396
yen)
Others17,263 17,144 17,632 14,274
Total
96,140 86,897 92,879 116,232
Alignment Minimum curve radius (m)
550
500
550
element Minimum curve length (m)
230
259
308
Minimum transition curve (m)
550
500
550
Minimum transition curve length (m)807371
Steepest vertical gradient (%)
3.8
4
Steepest vertical curve radius (m)
2486
12092
7446
Steepest vertical curve length (m)
154
472
268
Length(m)

In terms of costs, Plan A, is the least expensive and will be the best option among these three. Also,
because Plan A is the optimal result of the initial plan alignment, it verifies the validity of the actual
design.
(5) Results of using DM and Problems
This system provides very simple functions, selecting a CAD element and then specifying its type ofcontrol point. A CAD element refers to an object, such as a line or a symbol, which describes a feature
on a drawing. Even this simple function automatically contributes to reduction of the operator's
burden in establishing control points on the structured data (e.g. separated into layers) compared withsimple CAD data. Though time measurement etc. was not performed, the following processes became
possible, for instance:(1) Showing the road layers only, selecting them as a group, and setting them up as control points.
(2) Hiding the elements that have no relations with control points in order to improve visibility.
In addition, through functional development in the future, it will be possible to automatically convert alayer to a control point. In other words, when the map elements are drawn on designated layers, theseelements on each layer can be converted together into control points as a group.
On the other hand, some problems that inhibit those effective uses were also found. As a matter ofcourse, some of the control points in highway design are not defined in ASTER GDEM. These
problems will be dealt with by revising the specifications of ASTER GDEM in the long term, and by
judging control points through visual inspection and generating data through manual operation of
CAD etc. in the short term.
5. DEMONSTRATION 2: LANDSCAPE EVALUATION THROUGH LINKAGE WITH A VRSYSTEM
Although roadside landscape is one of the most important considerations for road design, OHPASS
does not have a function to evaluate it. In this experiment, a person will judge the landscape using
computer graphics (CG). We have linked OHPASS with a VR system and examined its uses andeffects. The VR system configuration is described in 3.(1).
(1) Automatic generation of VR spaces
Terrain data are loaded from 3D data created within Civil 3D via a LandXML format file. Next,
horizontal alignment data for highways (linear coordinates and parameters), vertical alignments(intersection points, height, VCL, etc.), and highway cross sections (width of road surface, slope

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gradient / bench spacing, etc.) are loaded. A three-dimensional space is created by combining all thesedata.
3D geometries are not loaded directly from 3D CAD data. Instead, VR software is used to load thecross-section data and build its design geometries. The geometry created by the VR software does notdiffer from geometry created with 3D CAD software.
When creating a 3D VR space, we also used textures to represent details such as lane markings, roadsurface textures, and cutting / banking, which is not expressed in the process of 3D modeling. Figure 4
shows the link between the VR and Civil 3D systems.

Figure 4 - Linkage of CAD and VR system

Figure 5 - Screen Example for presentation

Figure 6 - Screen Example for presentation with UC-win/Road(left: before alteration of the alignment, right: after alteration of the alignment)(2) Evaluation and discussion
We made a presentation showing an animation with real-time CG. Figures 4 and 5 show thepresentation screens. After presentation and examination the following results were achieved:
We made evaluations of the experimental results using the 3D VR screens automatically generated by

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UC-win/Road. Figure 6 shows the VR screens comparing views before and after alteration of thealignment. On the left screen, a cutting face develops close to the present slope face. On the rightscreen, on the other hand, development of a cutting face is avoided by placing it at a distance of about
20m from the slope. After presentation and examination, the following results were achieved about the3D highway-landscape design system.
a) Landscape evaluation from a driver’s perspective
The VR simulation can be utilized for examination and evaluation of safety issues such as driver
viewing distance and visibility with different alignments. The geometry of cutting and banking androad structure has only a limited effect on drivers. For example, the sense of apprehension that a drivermight feel when passing through a steeply cut terrain area can be evaluated to a certain extent.
However, for examining the geometry of tunnel entrances, separate model creation apart from thedescribed workflow is necessary for evaluation in the VR space.
b) Effects produced by substantial reduction in time for image generation
Streamlining the image creation process (by loading ASTER GDEM in OHPASS to transferring it into
3D CAD and the VR system) can substantially reduce the working hours that would be required forimage generation using a conventional method.
Table 4 shows the time required for these demonstration experiments. In this case demonstrations 1
and 2 are considered consecutive. Demonstration 1 was performed to search for an optimal alignment
with OHPASS using the data converted into the specifications of ASTER GDEM. Demonstration 2
used the results produced by OHPASS and linked these with a VR system. In comparing the timerequired to generate alternative plans: (a) is equal. Yet this system makes it possible to generatealternative plans and preview them with the VR simulation in about 60 minutes, the total working timeof steps (b), (c), and (d). For instance, when deciding between an embankment or an elevated structure,
after calculating the alignments, the following procedure makes it possible to compare both plans in ashort amount of time taking into account construction costs or volume of earthwork.
Table 4 - A comparison of working times
Working timerequired for theconventional
methodWorking timerequired forthis system

Creating 3D terrains from DM andestablishing control points

240min240min(b) Optimal design calculation with OHPASS

420min10min

3D representation of the(c)calculation results from OHPASS

180min30min(d) Creating data with a VR system
Total

240min1,080min

20min
300minWork contents(a)(1) Embankment geometry generation with a series of operations up to the VR system(2) Establishment of an area to be made into an elevated structure as a control point for a "compulsory
elevated section" using the function of OHPASS(3) (b) Optimal design calculation using OHPASS --> (c) three-dimensional representation of thecalculation results from OHPASS(4) (d) Data creation using a VR system and comparison with the result of (1)CONCLUSIONSThis study developed and added a function for using ASTER GDEM, highly accurate DEM covering
all the land on earth, to Optimal Highway Path Automatic Search System (OHPASS). In specific, by
creating 3D terrain from ASTER GDEM and using the control point set-up function, a foundation for

9utilizing ASTER GDEM has been created. In addition, by enabling the optimal design calculation
results to be loaded into 3D CAD, graphical representations of the design results have been linked
with a VR system.
Using the developed system, we performed simulated demonstrations using ASTER GDEM. It was
concluded that use of ASTER GDEM is effective for time saving in creating three-dimensional terrain
models of the present conditions, which are the essential data to input into the optimal design system.
Next, we conducted a linkage experiment with a VR system. It was confirmed that use of ASTER
GDEM as input data linked with the optimal highway alignment design system, 3D CAD, and VR
system is effective in evaluating a landscape speedily or evaluating various landscapes in a shortamount of time. We expect that a link between OHPASS and a VR system can be used effectively forlandscape evaluation and road structure evaluation during highway alignment design stages, as well as
for consensus building and presenting proposals.
Future issues include how to add height information other than contour lines to planimetric features inusing ASTER GDEM, creation of graphics required as control points, and support for items that are
not considered in OHPASS.

REFERENCES[1] MotoyaYAMASAKI, Teietsu HONGO, Hiroshi IMAMURA, Kazuo HIYANE, Jun LIO, Tomoyuki
YATABE, and Seiichi KAGAYA (2001). Basis study in optimal vertical design with digital terrain
data and genetic algorithms. Infrastructure Planning, Collected papers, Vol.18，pp.139-148.
[2] Motoya YAMASAKI, Teietsu HONGO, Kazuo. HIYANE, and Tomoyuki YATABE (2004). Studyof alignment models for optimizing highway alignments. Collected papers of Japan Society of Civil
Engineers, No.758／IV-63, pp.57-69.
[3] J.C. JONG (1998). Optimizing highway alignment with genetic algorithms. Ph.D. dissertation,
University of Maryland, College Park, MD.
[4] J.C. JONG and Paul SCHONFELD (2003). An Evolutionary model for simultaneously optimizing
three-dimensional highway alignments. Transportation Research, Part B.
[5] AASHOTO,1977. A Manual on User Benefit Analysis of and Bus Transit Improvements. American
Association of state Highway and Transportation Officials, Washington, Dc.
[6] Jha,MK.,2000. A geographic information systems-based model for highway design optimization.
Ph.D. dissertation,University of Maryland, College Park, MD.
[7] M. Yamasaki, T. Hongo, K. Hiyane, J. Lio and T. Yatabe: Genetic Algorithms Based Optimizationof Three-Dimensional Alignments for Highways, Proceedings of the Third International Conference
on Engineering Computational Technology, 2002.
[8] M. Yamasaki, T. Hongo, K. Hiyane, J. Lio and T. Yatabe: Genetic Algorithms Based Optimizationof Three-Dimensional Alignments for Highways, Proceedings of the Third International Conference
on Engineering Computational Technology, 2002.

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