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         Geoplanning 
   Vol 7, No 2, 2020, 113-130                                                                                                                                                          Journal of Geomatics and Planning 

                                                                                                 E-ISSN: 2355-6544 
http://ejournal.undip.ac.id/index.php/geoplanning  

doi: 10.14710/geoplanning.7.2.113-130 

Application of Spatial Multi-Criteria Analysis and Least-Cost Path on The 
Highway Route Planning (Case Study of Bawen – Yogyakarta Highway)  

B. Marjuki a*, I. Rudiartob 

a Center of Data Processing – Ministry of Public Works and Housing, Indonesia 
b Urban and Regional Planning & Development Department – Diponegoro University, Indonesia 

Abstract: Infrastructure planning ideally considers the geotechnical aspects and 
physical conditions of the infrastructure development location and must be able to 
support regional development. One kind of spatial analysis technique, which has the 
capabilities to integrate various regional characteristics associated with its suitability for 
a particular use, is spatial multi-criteria analysis. By using Bawen - Yogyakarta Toll Road 
Plan as a case study, this research is intended to apply route planning that takes into 
account regional characteristics, through the involvement of Spatial Multi-Criteria 
Analysis, Analytic Hierarchy Process, and Least Cost Path analysis. The analysis results 
then compared with the government preferred route to see its advantages and 
disadvantages.  

Study results show that the generated route from the analysis has several 
advantages over the government preferred route while also having some shortcomings. 
The advantages of route analysis results compared to government preference routes 
include: better able to avoid earthquake and landslide-prone areas, better support to the 
preservation of protected areas, has more areas with flat to gentle topography, and have 
smaller additional construction cost as the consequences of the intersection with existing 
roads, rivers, and railways, In terms of affected land-use, generated route also has 
minimum negative impacts on the sustainability of agricultural land in the study area. 

The shortcomings of the analysis result are: not yet able to avoid flood and volcanic 
eruptions-prone areas as well as government’s preferences route, higher land acquisition 
cost estimation, and less support for industrial and tourism activities in the research 
area. Improvement of analysis methods, data, and cost assessment strategy is needed 
to obtain better results and more appropriate modeling and analysis, in order to support 
regional infrastructure planning and development. 

 
Copyright © 2020 GJGP-UNDIP  

This open access article is distributed under a  
Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license. 

How to cite (APA 6th Style): 
 Marjuki, B., & Rudiarto, I. (2020). Spatial Multi-Criteria Analysis and Least-Cost Path on The Highway Route Planning: A Case Study of Bawen – 
Yogyakarta Highway, Indonesia. Geoplanning: Journal of Geomatics and Planning, 7(2), 113-130. doi: 10.14710/geoplanning.7.2.113-130 

 

1. INTRODUCTION 

Regional disparities between the western region and eastern region of Indonesia become the primary 
focus in the infrastructure development planning of the Indonesian Government. In this regard, the challenge 
of contemporary Indonesian infrastructure development is on how to reduce disparities and balance growth 
and development. Having to achieve this expectation, the Strategic Plan of the Ministry of Public Works and 
People's Housing 2015-2019 mandates that infrastructure development must integrate on the inter-regional, 
inter-sectoral, and inter-governmental levels. This mandate is realized in the form of the Strategic 
Development Area concept (SDA), which becomes the basis of infrastructure development in Indonesia. 
Ministry of Public Works divides the Indonesian territories into 35 SDA's, where every SDA has a specific 
planning policy according to the potential and problems that exist in each region. 

One of the planned SDA in Java Island is the Integrated Growth Center Yogyakarta-Solo-Semarang SDA 
(Strategic Plan Ministry of Public Works and Housing 2015-2019). Various infrastructure is planned to build 
in this SDA, where is one of them is Bawen – Yogyakarta Toll Road. Given that SDA-based infrastructure 

Article Info: 
Received: 7 April 2018 
in revised form: Janury 2019 
Accepted: January 2020 
Available Online: 1 November 2020 
 

Keywords:  
Spatial Multi-Criteria Analysis, Route 
Planning, AHP, Least Cost Path, Toll 
Road. 
 

*Corresponding Author: 
B. Marjuki 
Center of Data Processing – Ministry 
of Public Works and Housing, 
Jakarta, Indonesia 
Email: b_marjuki@pu.go.id  

OPEN ACCESS 

http://ejournal.undip.ac.id/index.php/geoplanning
https://doi.org/10.14710/geoplanning.7.2.113-130


 
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development must be followed, the determination of the Bawen - Yogyakarta Toll Road route should take 
into account those integration aspects. Road construction should be integrated with regional development 
plans undertaken by provinces and districts traversed by the planned toll road. Also, toll road existence must 
also be able to support the existence of economic, social, and tourism activities in the region.  

The realization of integrated road planning requires formulation, analysis, and evaluation techniques 
that can involve these considerations. Multi-Criteria Evaluation is one of the analysis and evaluation 
techniques that can be used for that purpose. The role of Multi-Criteria evaluation in decision making has a 
long history, ranging from the traditional form (e.g., conventional mediation) to the form of modern 
automated programming through the help of computers and information technology (Köksalan, Wallenius, 
& Zionts, 2013). This development also includes spatial and regional planning through the development of 
Spatial Multi-Criteria Analysis (Malczewski, 2006). In spatial multi-criteria analysis (SMCA), regional indicators 
become the criteria used as the basis for determining the toll road route. Through analysis of the involved 
criteria, toll road alternative scenarios can be made, followed by an evaluation to determine which are the 
best scenarios, in the context of integration of infrastructure development and regional development. 

However, the factors and criteria involved in SMCA need to be formulated in advance of hierarchical 
urgency, since the importance of each factor is different from one to another. The urgency of each involved 
factor can be formulated using experts judgment techniques. Various techniques have been developed to 
determine the urgency of factors from the experts, but one of the most commonly used is the Analytic 
Hierarchy Process (AHP), developed by Wind & Saaty (1980). AHP can produce weight values and scores of 
involved factors and criteria that can be inputted in SMCA to find alternative solutions to the problems faced, 
followed by an evaluation to determine which one is the best. SMCA and AHP integration for site selection 
application has been conducted by many authors either in transportation route planning (Atkinson et al., 
2005; Beukes, Vanderschuren, & Zuidgeest, 2011; Abdi et al., 2009; Keshkamat, Looijen, & Zuidgeest, 2009; 
Effat & Hassan, 2013) or other fields of study (Sánchez-Lozano & Bernal-Conesa, 2017; Mishra, Deep, & 
Choudhary, 2015; Bunruamkaew & Murayam, 2011). Thus, through a combination of SMCA and AHP, a toll 
road route plan that reflects not only the information integration of the involved criteria but also the 
experiences and preferences of the stakeholders involved in toll road infrastructure development can be 
obtained. By looking at the gap, this study combines SMCA with AHP as a tool to see an effective and efficient 
evacuation route based on Least Cost Path analysis. 

 

2. DATA AND METHODS 

2.1. Study Area 

The study is conducted at several subdistricts in Central Java Province and Yogyakarta Special Region, 
Indonesia, which likely will be traversed by Bawen - Yogyakarta Toll Road. These sub-districts are within the 
administrative area of Semarang Regency, Temanggung Regency, Magelang Regency, and Magelang City, 
which are part of the Central Java Province. As for Special Region of Yogyakarta, the study area covers some 
sub-districts of Sleman Regency and Kulonprogo Regency (Table and Figure 1). 

 

 

 

 

 

 

 

 

 

 



 
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Table and Figure 1. Study Area 

Regency/District Sub-District 

Semarang Bergas 
Bawen 
Bandungan 

Jambu 
Banyubiru 
Tuntang 

Sumowono 
Ambarawa 
Pringapus 

Temanggung Kaloran 
Temanggung 

Kranggan 
Pringsurat 

Tembarak 
Selopampang 

Magelang Secang 
Grabag 
Windusari 
Kaliangkrik 
Bandongan 
Ngablak 
Ngluwar 

Tegalrejo 
Candimulyo 
Sawangan 
Tempuran 
Mertoyudan 
Mungkid 
Muntilan 

Pakis 
Borobudur 
Salam 
Sawangan 
Srumbung 
Dukun 

Magelang City North 
Magelang  

South Magelang  Central 
Magelang 

Sleman Tempel 
Turi 
Sleman 

Pakem 
Ngaglik 
Mlati 

Minggir 
Seyegan 

Kulon Progo Kalibawang   

 

 

2.2. Route Planning Criteria 

Criteria in SMCA is the basis for determining the evaluated object is to meet the requirements or not. 
Criteria, sub-criteria, and alternatives criteria for toll road route planning in this study are derived from a 
literature review of similar research (Atkinson et al., 2005; Effat & Hassan, 2013; Kushari, Mulyono, & 
Hendratno, 2015; and government regulations. The results of the criteria formulation are presented in Table 
2. 

Table 2. Proposed Toll Road Route Planning Criteria (Analysis, 2018) 

 
Factors Criteria Sub Criteria 

Geotechnics Soil • Soil Texture 
Topography • Slope 
Geology • Rock Type 

Environmental Natural Protected Area • Natural Protected Areas in Existing Spatial Plan 
Cultural Protected Area • Distance from Archeological Cultural Heritage 

Social Land-use • Existing Land-use 
Land Value • Estimation of current Land Value 

Regional Activity System Regional Activity System • Distance from Industrial Areas 

• Distance from Urban Growth Centers 

• Distance from Tourism Destinations 
Safety and Additional 
Construction Cost 

Disaster Hazards • Landslide-Prone Areas 

• Flood-Prone Areas 

• Earthquake-Prone Areas 

• Volcanic Eruption Prone Areas 
Road Safety  • Slope direction (aspect) 
Additional Construction Cost • Intersection with the road network 

• Intersection with the railway network 

• Intersection with the river network 

 

2.3 Data Sources 

Various geospatial data has been collected and processed (i.e., vector data digitization, map projection 
conversion, vector to raster conversion) into one spatial database (geodatabase) to meet the minimum 
requirements for analysis. Data used in this study came from primary sources through field surveys and 
secondary data obtained from various agencies and institutions (Table 3). 

 



 
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Table 3. Data Sources (Analysis, 2018) 

 
Data Type Data Sources Type of Data 

Land-use Visual at Interpretation of 1.5 meters Orthorectified SPOT-6/7 Satellite 
Imagery acquired 2016 to 2017 (National Geospatial Agency – National 
Institute of Aeronautics and Space) 

Secondary 
Data 

Topography and Slope 8 meters IFSAR DEM generated from TERRASAR-X SAR Imagery (National 
Geospatial Agency – National Institute of Aeronautics and Space) 

Secondary 
Data 

Natural Protected Areas Existing Regency Spatial Plan (Regency Regional Planning Board) Secondary 
Data 

Cultural Protected Areas Temples and Archeological Site Mapping Resulted from Degroot & J Klokke 
(2010) study.  

Secondary 
Data 

Volcanic Eruption Hazard Volcanic Eruption Hazard Map from National Geological Agency Secondary 
Data 

Landslide Hazard Landslide Hazard Map from National Geological Agency Secondary 
Data 

Flood Hazard Flood Prone Map from Existing Regency Spatial Plan (Regency Regional 
Planning Board) 

Secondary 
Data 

Earthquake Hazard National Earthquake Hazard Map 2017 (Ministry of Public Works and Housing) Secondary 
Data 

Soil Texture 250 meters of global soil texture data from SOILGRIDS (ISRIC – World Soil 
Information) 

Secondary 
Data 

Land Value Land Value Map published in 2015 by the National Land Agency  Secondary 
Data 

Rock Type Remote Sensing based Geologic Map at 1:50.000 Scale (National Geological 
Agency) 

Secondary 
Data 

Industrial, Tourism and 
Growth Center location 

Field Survey, Existing Regency Spatial Plan Primary and 
Secondary 
Data 

Road Network National, Provincial, and Regency Road Network Map published in 2015 by the 
Ministry of Public Works and Local Government. 

Secondary 
Data 

Railway Network National Topographic Map at Scale 1: 50.000 Published in 1915 by Nederland 
Indie Topographic Survey Agency, verified by field survey because the railway 
network in the study area has been closed at 1976, though there is a 
government plan to reactivate it in the future. 

Primary and 
Secondary 
Data 

River Network National Topographic Map at Scale 1:25.000, SPOT-6/7 Satellite Imagery 
Visual Interpretation 

Secondary 
Data 

Government’s preferred 
Route Plan of Bawen – 
Yogyakarta Toll Road  

Latest official Report of Bawen – Yogyakarta Toll Road Development (Ministry 
of Public Works and Housing) 

Secondary 
Data 

 

2.4 Data Standardization and Normalization 

Determination of the optimal toll road route based on SMCA involves a series of criteria, sub-criteria, 
and alternatives that have a different scale and value measurement. These scale and value differences will 
affect the accuracy of the analysis results if it is not standardized into standard value (Drobne & Lisec, 2009). 
Therefore, before the analysis, any alternatives of sub-criteria were assigned and reclassified into a relative 
scoring scheme (from 1 to 9). The assumption used is, the higher the score of the alternative criteria, the 
more it is not suitable as a toll road route. Standardization parameters and references are presented in Table 
4. 

 

 

 

 
 
 
 

 



 
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Table 4. Cost Value Standardization 
 

Factor Criteria Sub-criteria Alternative 
Cost 

Value 
Cost Classification 

References 

Geotechnics Soil Soil Texture 0 -30% Sandy Soil 9 Natural Breaks 
Classification 30 -80% Sandy Soil 5 

80 -100% Sandy Soil 1 

Geology Rock Type Quartenary Deposits (alluvium, 
kolluvium), breccia 

1 Expert Judgment 

Extrusive Igneous and Sediment Rock 
(sandstone, tuff) 

5 

Intrusive Igneous Rock (andesite, Granite, 
Dyorite) 

9 

Environment Natural 
Protected Area 

Natural Protected 
Area Type 

Non-Conservation Area 1 Modified from 
Kushari et al. (2015) 

and Expert Judgment 
Conservation Forest, National Park  9 

Cultural 
Protected Area 

Distance from 
Temples 

0 - 500 meters 9 Expert Judgment 
> 500 meters 1 

Social Land-use Land-use Type Shrubs, Bare land, Grassland 1 Modified from 
Kushari et al. 

(2015)and Expert 
Judgment 

Mixed Garden, 3 
Rice Fields, Poultry, Plantation 5 
Sparse Settlements, Cemetery,  7 
Industrial Area, Economic Services, 
Historical Places, Dense Settlements, Lake 

9 

Land Value Land Value 7,000 - 180,000 Rupiahs 1 Natural Breaks 
Classification 180,000 - 2,100,000 Rupiahs 5 

> 2,100,000 Rupiahs 9 
Regional Activity 
System 

Regional 
Activity System 

Distance from 
Industrial Area 

0 - 5 kilometers 1 Equal Interval 
Classification 5 - 10 kilometers 5 

> 10 kilometers 9 
Distance from 
Urban Growth 
Centers 

0 - 5 kilometers 1 
5 - 10 kilometers 5 
> 10 kilometers 9 

Distance from 
Tourism Locations 

0 - 5 kilometers 1 
5 - 10 kilometers 5 
> 10 kilometers 9 

Road Safety and 
Additional 
Construction Cost 

Additional 
Construction 
Cost 

Intersection with 
Road Network 

Non Road Area 1 Expert Judgment 

Local Road 3 
Regency Road 5 
Provincial Road 7 
National Road 9 

Intersection with 
Railway Network 

Non-Railway Area 1 
Railway Area 9 

Intersection with 
River Network 

First River Order  1 Expert Judgment 
Second to Third River Order 3 
Fourth River Order  5 
Fifth River Order  7 
Sixth to Seventh River Order 9 

Natural Hazards Landslide Hazards Non Hazard Area 1 Construction 
Standards Number. 
007/BM/2009, and 
Expert Judgment 

Low Hazard Area 3 
Moderate Hazard Area 5 
High Hazard Area 9 

Flood Hazard Non Hazard Area 1 

Low Hazard Area 3 
Moderate Hazard Area 5 
High Hazard Area 9 

Earthquake Hazard PGA 0 - 0.25 g  1 
PGA 0.25 - 0.3 g 3 
PGA 0.3 - 0.4 g 5 

Volcanic Eruption 
Hazard 

Non Hazard Area 1 
Hazard Zone I 3 
Hazard Zone II 5 
Hazard Zone III 9 

 

 

 

 



 
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2.5 AHP Based Weighting 

Criteria and sub-criteria weighting in this study were conducted using the Analytic Hierarchy Process 
(AHP) method developed by Wind & Saaty (1980). In this study, the AHP analysis was conducted on two 
levels: first, AHP at the sub-criteria level (applied to sub-criteria incorporated in disaster risk criterion, 
regional activity system criterion, and additional construction cost criterion), and AHP at the criteria level 
(Figure 2). Topographic factors are not involved in AHP and SMCA because topographic factors will be used 
as a horizontal and vertical factor according to the modeling approach used, which is an anisotropic approach 
(to be described in subsequent chapters).  

 

 

Figure 2. Hierarchy of Criteria (Analysis, 2018) 

 

As input from AHP is the result of a questionnaire survey and interview with three experts from the 
Ministry of Public Works and Housing and one transportation practitioners. The result of the questionnaire 
was then arranged in the pairwise comparison matrix. From the obtained pairwise comparison matrix, the 
priority vector and its consistency index can be calculated. The value of the obtained priority vector is then 
used as the weight value of the criteria if the consistency index is less than 0.1. The final weight value from 
the obtained weight of each respondent then is calculated using the geometric mean formula.    

 

2.6 Spatial Multi-criteria Analysis (SMCA) 

SMCA is implemented in Geographic Information System (GIS) through Map Algebra operation based on 
the raster data structure. SMCA requires the weight value of each criterion and sub-criteria to determine 
which criteria are more important to determine the most optimal toll road route. This weight value is derived 
from the AHP process described in the previous sub-chapter. The analytic method used to determine the 
best route of the toll road plan in this study is using the least-cost path (LCP) method. LCP is a technique to 
determine the shortest cost distance from one location to another based on a raster surface data called 
Accumulated Cost Surface (Douglas, 1994). Douglas (1994) defines Accumulated Cost Surface (ACS) as a 
dasymmetric representation in the form of a grid model of the Earth's surface, which refers to how much 
resources to spend or how much of the frictions must be passed over the model of the earth's surface. In this 
study, ACS is derived from the cumulative cost raster (called Cumulative Cost Surface/CCS) obtained from 
SMCA. CCS was obtained from the map overlay result of various baseline data (see Table 3) that have been 



 
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given standardized score values and weight values in accordance with the AHP results. Several SMCA 
algorithms have been developed to obtain CCS. One of them is the Weighted Linear Combination (WLC) 
technique, which has been implemented in most of Geographic Information System software available on 
the market and used for this study.  

ACS calculation within the GIS can be based on two approaches, namely the isotropic approach and 
anisotropic approach (Yu, Lee, & Munro-Stasiuk, 2003). The Isotropic Approach is an approach in which the 
movement of calculation of cost accumulation in all directions is considered equal, while the anisotropic 
approach considering the movement of cost accumulation in all directions (both horizontal and vertical) has 
different implications to cost accumulation. In the context of road planning, some authors such as Collischonn 
& Pilar (2000) and Yu et al. (2003) argue that the anisotropic approach is more appropriate than the isotropic 
one. This opinion departs from the fact that less step and sinuous slopes are preferred than slopes that are 
short but steep. Either In isotropic or anisotropic approach, ACS calculations require CCS data obtained from 
WLC operation result. In the anisotropic approach, the difference is that in addition to requiring CCS 
information, anisotropic operations also require two other input data, which are a horizontal and vertical 
factor. The horizontal factor determines the horizontal friction, which will increase the cost of the horizontal 
movement direction (0 to 360 degrees), while the vertical factor determines the vertical friction, which will 
increase the cost in upward vertical movement (0 to 90 degrees), or downward vertical movement (0 to -90 
degrees).  

Implementation of the horizontal and vertical factor to extracting the ACS using anisotropic approach 
can be done by determining the horizontal factor and vertical factor tables first. This horizontal and vertical 
factor table will be used as the basis to determine the cost value of the friction in horizontal movement (slope 
direction) and vertical movement (slope gradient). For this study, horizontal friction is considered in the 
analysis to facilitate the selection of road routes that avoid sunrise and sunset direction, so when the toll 
road is in the operational stage, the road users will not experience visibility problems due to glare disruption. 
Whereas, vertical friction is a representation of the increased cost due to terrain slope gradient changes, 
either during the road construction stage or road operational stage. The horizontal and vertical factor tables 
used in this study developed from the result of Yu et al. (2003) study and presented in Table 5. The INF value 
in the vertical factor table indicates that the cost to pass the area with a given slope is too large, so the area 
that has that kind of slope will be impassable. An overview of the performed analysis process is presented in 
Figure 3. 

 

Table 5. Vertical and Horizontal Factor Used for Anisotropic Route Extraction (Analysis, 2018) 

 

Vertical Factor Horizontal Factor 

Slope (Degrees) Cost Value Aspect (Degrees) Cost Value 
(-45) - (-90) INF 0 - 22.5 1 

(-25) - (-45) 32 22.5 - 67.5 7 
(-15) - (-25) 9 67.5 - 112.5 9 
(-12) - (-15) 7 112.5 - 157.5 3 
(-9) - (-12) 5 157.5 - 202.5 1 
(-3) - (-9) 3 202.5 - 247.5 7 

0 - (-3) 1 247.5 - 292.5 9 

0 - 3 1 292.5 - 337.5 3 
3 - 9 3 337.5 - 360 1 

9 - 12 5 
12 - 15 7 
15 - 25 9 
25 - 45 32 
16 - 90 INF 

 



 
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Figure 3. Optimal Toll Road Route Analysis Workflow (Analysis, 2018) 

 

2.7 Comparison and Evaluation of the Analysis Results 

Route comparison and evaluation in this study is done to find out to which extent the routes generated 
from SMCA and LCP is better than the government's preferred route. Comparison and evaluation are 
performed by looking to some indicators that affect: (1) ease of construction; (2) risk in the operational phase 
of the toll road; and (3) integration and support to regional development. Details of the comparison and 
evaluation indicators are presented in Table 6. 

 

Table 6. Evaluation Indicators (Analysis, 2018) 

 

Factors Criteria Comparison and Evaluation Indicators 

Ease of Construction and 
Level of Investment Cost 

Land-use - The extent of land-use type passed by the route 
Topography - The extent of the slope passed by the route  
Protected Area - The extent of the protected area passed by the route 
Land Value - Estimation of land acquisition cost along the route 
Existing Transportation 
Network 

- Number of the intersection with the road 
- Number of the intersection with railway 

Sungai - Number of the intersection with river 
Road Geometry - Route Length 

Risk in Operational Phase of 
Toll Road 

Road Safety - The extent of terrain aspect passed by the route 
Natural Hazards - The extent of Earthquake prone area passed by the route 

- The extent of volcanic eruption prone area passed by the 
route 

- The extent of Landslide prone area passed by the route 
- The extent of the flood-prone area passed by the route  

Integration with Regional 
Development 

Integration with Regional 
Development 

- Proximity to Industrial Areas 
- Proximity to Urban Growth Centers 
- Proximity to Tourism Destinations 
- Proximity to Archeological Protected Sites 

 

 



 
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3. RESULTS AND DISCUSSION 

3.1. Criteria Standardization and AHP Results 

Standardization of alternative criteria value in this study was conducted by reference to three sources 
of information, namely: (1) existing similar research and literature, (2) government rules and laws about road 
management, and (3) discussions with experts which be done simultaneously with AHP survey. For criteria 
mapped in discrete geographic objects (vector data), standardized cost value assignment is performed by 
converting object information (which is nominal data) to value (ratio data), in accordance with literature and 
expert recommendations, whereas cost value assignment for criteria mapped in continuous geographic fields 
is performed by classifying those values into standardized values. Complete visualization of the 
standardization result for each sub-criterion is presented in Figure 4. 

 

 
 

Figure 4. Standardization Results (Analysis, 2018) 

 

 



 
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The AHP processing in this study was conducted after the compilation of the questionnaire by experts 
has been completed. The AHP is performed to obtain the value of the eigenvector used as the weighting 
value of the criterion. The confidence level of the eigenvector value in AHP itself is measured from the 
Consistency Index (CI) obtained from the analysis. Based on the obtained result, the CI obtained for each 
respondent indicates a value below 0.1, which can be interpreted that the AHP analysis for each respondent 
is consistent. Thus the obtained eigenvector can be used as the criteria weight value. Eigenvalues (weights) 
calculation result of the criteria along with its consistency index value is presented in Table 7. 

 

Table 7. AHP Results (Analysis, 2018) 

 

Criteria 
Ministry of Public Works Transportation Expert Geometric 

Mean 
Weights 

Normalized 
Geometric 

Mean Expert 1 Expert 2 Expert 3 Expert 4 

Geology 0.1949 0.1231 0.0481 0.0817 0.10 0.11 
Soil 0.1839 0.1122 0.0350 0.0376 0.07 0.08 
Natural Protected Area 0.1675 0.1496 0.1529 0.1596 0.16 0.18 
Cultural Protected Area 0.1653 0.1415 0.1529 0.1596 0.15 0.17 
Land-use 0.1341 0.1065 0.1529 0.1298 0.13 0.14 
Land Value 0.0191 0.0445 0.0171 0.0210 0.02 0.03 
Regional Activity System 0.0228 0.0201 0.1529 0.1386 0.06 0.06 
Natural Hazards 0.0691 0.2787 0.1529 0.2523 0.17 0.18 
Additional Construction Cost 0.0434 0.0239 0.1354 0.0199 0.04 0.05 
Total Weights 1 1 1 1 0.90 1.00 
Consistency Index 0.07 0.08 0.09 0.07    

 

From the results of the AHP analysis described above, the final criteria and sub-criteria weights can be 
determined. In this study, the final weights computed from the normalized weights resulted from the 
geometric mean operation. Before inclusion as input for CCS generation, the decimal format's final weight 
values are converted to a percentage by multiplying it with 100 value. Conversion to percentage is applied to 
avoid the reduction effect of the final cost value (due to the multiplication operation of cost value with weight 
value). As for the final weight value of sub-criteria is determined from the division of the concerned criteria, 
in accordance with the proportion of the weight value of each sub-criterion. The final weight value of criteria 
and sub-criteria that has been converted to the percentage scale is presented in Table 8. 

 

Table 8. Criteria and Sub-criteria Final Weights (Analysis, 2018) 

 

Criteria Sub Criteria Criteria Weights Sub-Criteria Weights 

Geology 11  
Soil 8  
Natural Protected Area 18  
Cultural Protected Area 17  
Land-use 14  
Land Value 3  
Natural Hazards 18 

 

  
  
  
  

Volcanic Eruption 
 

5.61 

Landslide 
 

6.80 
Earthquake 

 
4.53 

Flood 
 

1.45 
Regional Activity System 6 

 

  
  
  

Distance from Urban Growth Center 
 

2.21 
Distance from Industrial Area 

 
3.49 

Distance from Tourism Destination 
 

0.52 
Additional Construction Cost 5 

 

  
  
  

Intersection with Railway Network 
 

1.23 
Intersection with Road Network 

 
1.85 

Intersection with River Network 
 

1.47 



 
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The result of consensus drawing from several experts using AHP and geometric mean in this study 
favored criteria of natural hazards, protected area existence, land use, and geological condition. Thus, the 
LCP analysis will be more sensitive to the above criteria than other criteria. This sensitivity is apparent after 
comparison with the government’s preferred route, where the LCP generated route cannot identify feasible 
and effective routes when viewed from regional activity system criterion and the land value criterion. The 
route generated from the LCP analysis ideally should be sensitive to the regional activity system criterion 
because the Bawen - Yogyakarta Toll Road is expected to support regional development and tourism activities 
in the study area. LCP generated route ideally should also be sensitive to the variation of land value over the 
study area because it will be related to the role of minimizing the complexity of land acquisition as one of the 
leading problems of toll road development in Indonesia. 

The relative value assignment strategy of cost (cost score) also plays a vital role in addition to the 
weighting strategy of the criteria itself. This statement can be understood from the results of the route 
comparison on the additional construction cost criterion. The weight value of the additional construction 
cost criterion is relatively smaller than the other criteria, but the route obtained from the LCP analysis 
generated intersection number with rivers and roads less than the government's preferred route. Thus, the 
cost value assignment strategy for the additional construction cost criterion is sufficient.  

The level details of the data used are one of the aspects that also need to be considered further. For 
example, this study uses the Land Use Map at Scale 1: 10,000, where is in this data, the built-up land-use is 
still visualized as area (e.g., residential area or industrial area). This data may not be detail and useful enough 
as the input in SMCA and LCP application for route planning. As can be seen from the analysis results, the 
route generated from LCP analysis has not shown adequate sensitivity compared to the government’s 
preferred route for the residential area, although the criterion weight value is quite high. This insensitivity 
happens because the score assignment of the residential area has been done based on its relative density, in 
accordance with the level of the details of those data. If detailed information that can be used as a basis for 
determining cost score in a more precise way is available (such as the number of buildings per block), cost 
models and LCP analyzes may be more sensitive to the variation in the cost value of each criterion, and then 
can have significant implication to the improvement of the analysis and modeling. 

The use of anisotropic approach (ArcGIS path distance algorithm) in this research is capable of 
generating a toll road route that has the cost as low as possible. As shown by the comparison results of 
topographic aspects in Table 8, the route of the LCP analysis can find areas with the topography as flat as 
possible, to minimize the cost when compared with the government's preferred route. Nevertheless, the 
results obtained are still can be debated further, especially in the analysis results around Bawen Sub District, 
Ambarawa City, and Rawa Pening Lake Area. The anisotropic approach, which considers slope gradient as a 
vertical cost factor, tends to favor flat topography with the slope of a small slope as the area with the best 
suitability for toll road routes. However, in the case of Bawen - Yogyakarta toll road, the government study 
route has chosen to avoid Rawa Pening Lake area, although topographically, the Rawa Pening Lake area is 
entirely appropriate. The government may want to avoid the complexity of construction costs associated 
with the soil engineering treatment in the Rawa Pening Lake area, which tends to be soft and has low 
engineering capacity to support road infrastructure. This fact has not been well anticipated by the model 
generated from this study since the soil criterion in this study is based only on the percentage of the sandy 
texture of the soil and has not considered other soil characteristics (due to lack of data), such as effective soil 
depth.  

If we refer to similar studies, among others by Ismail & Jusoff (2009), Beukes et al. (2011), and Chandio 
et al. (2012), these studies found that multi-criteria spatial analysis and LCP can provide effective and efficient 
route analysis results in terms of distance and time spent to travel, as well as minimal risk in terms of 
construction and operational cost. However, the criteria involved in those studies were not as much as the 
criteria used in this study. Consideration of more criteria will make the cost model have more complicated 
behavior, and there is a possibility to produce route analysis results that do not meet some of the criteria 
under consideration (as can be seen from the results of this study), although it is also there is the possibility 
that complex model will better represent the complexity of the cost conditions in the field. 

 



 
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The results of this study indicate the complexity of those mentioned complex behavior of the cost model 
above, which results in the non-fulfillment of some of the criteria. This finding confirms similar studies using 
relatively numerous criteria, as did by Keshkamat et al. (2009) and Kushari et al. (2015). Both studies also 
confirm the dynamic LCP analysis results as a result of involving more criteria in the preparation of the cost 
model, which resulted in some evaluation indicators are not fulfillment. 

Apart from the disadvantages of the analysis results, the modeling methods applied in this study along 
with the results that have been obtained indicate the potential that the analytical methods and techniques 
that have been applied can be utilized and developed further to support the transportation route planning, 
that can be more sensitive to various criteria, not only physical criteria but also to the sustainability of 
environmental functions, besides supporting the regional development. 

 

3.2. Criteria Standardization and AHP Results 

CCS model generated based on input: (1) result of standardization and classification of criteria and sub-
criteria; and (2 weight value of each criterion and sub-criterion. Model design and execution is performed 
within the ArcGIS Desktop 10.6 software using weighted sum geoprocessing operations. The weighted sum 
operations are carried out at two levels. First, at the sub-criteria level for disaster risk criteria, regional activity 
system criteria and additional construction cost criteria, and second, at criteria level that integrates the cost 
values of the geology, soil, protected areas, land use, land values, regional activity system, natural hazards, 
and additional construction costs criteria. 

 

 

 

Figure 5. CCS and ACS Results (Analysis, 2018) 

 

 

 



 
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The ACS model is constructed using input data consisting of: (1) initial location of road plan; (2) DEM of 
research area; (3) CCS model that has been produced; (4) horizontal factor table; and (5) vertical factor table. 
Processes performed within the ArcGIS path distance algorithm (presented in Figure 5 along with CCS result), 
which can be described as follows: 

1. Based on the initial location of the route plan and specified DEM, the algorithm calculates the surface 
distance from the initial location to all research areas.  

2. Aspect is derived from the specified DEM, followed by a cost value assignment for each mapped 
direction angle in every pixel with reference to the horizontal factor table. This sub-dataset is called 
horizontal cost.  

3. Slope is derived from the specified DEM, followed by a cost value assignment for each mapped slope 
angle in every pixel with reference to the vertical factor table. This sub-dataset is called vertical cost.  

4. CCS Cost Model is then multiplied by the horizontal cost. 

5. Through the iteration process, the accumulated sum of the cost of each pixel from the origin pixel then 
is summed, and the summation result of every two pixels is multiplied by surface distance value and 
vertical cost value between the evaluated pixels.  

 

 

 

Figure 6. LCP Routing Result (red line) Compared to Government’s Preferred Route (Green Line) 

 

 



 
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Least cost path (LCP) analysis is an optimal path determination analysis technique based on pixel-pixel 
searches across the study area, using criteria: (1) the shortest distance from the origin location to the 
destination location; along with (2) the lowest accumulation of cost. LCP requires input data consisting of (1) 
the result of the ACS model, (2) backlink data obtained from ACS generation, and (3) the destination point, 
which is the end location of the path to be extracted. The backlink data is a raster data indicating the direction 
of movement of the ACS data (quantified by the pixel value of backlink data ranging from 1 to 8 to represent 
the eight directions of possible horizontal movement) from the origin point to the destination point. The 
result of LCP analysis that has been done is presented in Figure 6. 

 

3.3. Comparison and Evaluation Results 

Obtained Toll Road Route from LCP analysis then compared and evaluated versus government's 
preferred route. To properly evaluate the affected areas, the generated route from LCP analysis and the 
government's preferred route has been buffered 80 meters either on the right side or to the left side of the 
route. Buffering of 80 meters is assumed to be sufficient to cover the necessary road space (own road space, 
road benefit space, and road monitoring space). The results of the performed evaluation are presented in 
Table 9. 

 

Table 9. Comparison and Evaluation Results (Analysis, 2018) 

 

Criteria Sub Criteria Alternative Criteria 
Route Generated 

from LCP 
Government’s 

Preferred Route 

Land Use Land-use 
Traversed by 
Route 

Settlements  166. 59 Ha  128.1 Ha 
Industry and Services  3.6 Ha  2.4 Ha 
Government Facilities  0.2 Ha  0 Ha 
Forests  0 Ha  0 Ha 
Plantations  16 Ha  35.4 Ha 
Agricultural Areas  694.9 Ha  751. 2 Ha 
Mixed Gardens  216. 2 Ha  298.1 Ha 

Bare Lands  0.7 Ha  3.2 Ha 
Slope Route Slope 0 - 3 % 114. 41 Ha  95. 24 Ha 

3 - 9 % 514. 28 Ha  386. 1 Ha 
9 - 25 % 449. 21 Ha  620. 9 Ha 
> 25 % 31. 76 Ha  123. 3 Ha 

Protected 
Area 

Protected 
Areas 
Traversed by 
Route 

Natural Protected Areas  0 Ha  0 Ha 
Cultural Protected Areas 
(300-meter radius from 
Temples) 

 111.3 Ha  122 Ha 

Land Value Land 
Acquisition 
Estimation 

Land Value 4,188,176,199,328 
IDR 

 3,581,766,068,528 
IDR 

Intersection 
with Existing 
Transportation 
Network 

Intersection 
with 
National. 
Provincial 
and 
Regencies 
Road 

National Roads  6 Intersections  5 Intersections 
Provincial Roads  4 Intersections  5 Intersections 
Regencies Roads  27 Intersections  30 Intersections 
Local Roads  102 Intersections  112 Intersections 

Intersection 
with Railway 

Railway  4 Intersections  2 Intersections 

First River Order   45 Intersections  46 Intersections 



 
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Criteria Sub Criteria Alternative Criteria 
Route Generated 

from LCP 
Government’s 

Preferred Route 
Intersection 
With River 
Network 

Intersection 
with River 

Second to Third River Order  47 Intersections  49 Intersections 
Fourth River Order  12 Intersections 9 Intersections 
Fifth River Order   0 Intersection  8 Intersections 
Sixth to Seventh River Order  0 Intersection  0 Intersection 

Road 
Geometry 

Route Length Total Route Length 69 Km + 408 m  76 Km + 720 m 

Risk on Toll 
Road 
Operational 
Phase 

Route 
Direction 

North. South  30.45 km  24. 36 km 
North West. South East 13.35 km  17. 52 km 
South West. North East 19.39 km  26. 37 km 
West. East 6.22 km  8.46 km 

Volcanic 
Eruption 
Hazard 

Hazard Zone 1  0 Ha  0 Ha 
Hazard Zone II  0 Ha  0 Ha 
Hazard Zone III  14.99 Ha  8.5 Ha 

Landslide 
Hazard 

Hazard Zone 1  702.12 Ha  835 Ha 
Hazard Zone II  89.27 Ha  179.9 Ha 
Hazard Zone III  18.67 Ha  20.8 Ha 

Flood Hazard Flood Prone Area  12.9 Ha  0 Ha 
Earthquake 
Hazard 

Peak Ground Acceleration 
0.25g -0.3g 

 63.9 Ha  79.7 Ha 

Peak Ground Acceleration 
0.3g - 0.4g 

 945.1 Ha  1034.6 Ha 

Peak Ground Acceleration > 
0.4g 

 100.1 Ha  111.2 Ha 

Integration 
with Regional 
Development 

Accessibility 
from 
Industrial 
Area 

Proximity to Industrial Areas 73 Industry 
Locations 

 113 Industry 
Locations 

Accessibility 
from Urban 
Growth 
Centers 

Proximity to Urban Growth 
Centers 

10 Growth 
Centers 

(Ambarawa. 
Grabag. 

Temanggung. 
Kranggan. Secang. 

Muntilan. Salam. 
Tempel. Sleman. 

Pakem) 

 8 Growth Centers 
(Temanggung. 

Kranggan. Secang. 
Kota Magelang. 

Mertoyudan. 
Mungkid. 

Borobudur. Dekso) 

Accessibility 
from 
Tourism 
Destinations 

Proximity to Tourism 
Destinations 

129 Tourism 
Destinations 

 142 Tourism 
Destinations 

(includes 
Borobudur 

Temple) 
Accessibility 
from Cagar 
Budaya 

Proximity to Temples and 
Archeological Sites 

57 Archeological 
Sites 

 68 Archeological 
Sites 

Note: Cells marked with green shade indicate the superior result 

 

 

 

Development and implementation of transportation infrastructure. either in central or local 
governments can consider the results obtained from this study to conduct further evaluation of the Bawen 



 
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Toll Road - Yogyakarta route plan. as well as adopting the method to similar infrastructure projects. Route 
obtained from the study can also be integrated with government studies on road segments that are perceived 
as having not met the disaster threat criteria. environmental criteria. support for food security. and 
construction costs criteria.  

The method of determining the trace used in this study. which includes SMCA. AHP. and anisotropic LCP 
analysis. is recommended to be incorporated in government's transportation planning activities. especially 
at the initial planning stage of the feasibility study. In addition to being able to provide route analysis through 
an automated process along with tools and evaluation techniques. this method can be extended to scenario-
based route modeling. so various alternative scenarios can be proposed and further evaluated. which are 
most appropriate and feasible. Formalization of methods can be done in the form of legislation. technical 
guidance. or Standard of Procedures (SOP). 

A comparison between isotropic and anisotropic approaches in determining the optimal route also 
needs to be studied and simulated further in future studies. This assessment is needed to ascertain which 
approach is the best in transportation infrastructure route planning. Assessments can be conducted in 
different regions with different regional characteristics to see how each approach performs. The testing of 
different LCP algorithms also needs to be applied. LCP analysis used in this research is using LCP Djikstra 
algorithm (Dijkstra. 1959) with a queen pattern (eight directions of movement). On the other hand. various 
LCP algorithms have been developed. for example. A* (Hart. Nilsson. & Raphael. 1968). Best First Search. A* 
Manhattan Heuristics. A* Diagonal Shortcut Heuristics. or LCP algorithms dedicated explicitly to road-based 
route planning such as SmartTerrain (Yu et al.. 2003) and Baek's Cut and Fill (Baek & Choi. 2017). The above 
LCP approaches and algorithms can be an alternative to determine transportation routes that may generate 
better results. Knight movement patterns (24 directions of movement) that have been implemented in the 
GRASS GIS software can also be studied further along with those various algorithms. 

The development of a cost model based on actual cost is also recommended. Incorporation of actual 
cost into the cost model will have a strategic value. among others is: (1) projecting the real cost-benefit 
condition of the implementation of infrastructure development; (2) can be valuable information in preparing 
infrastructure development budget plans; (3) represents the condition of the cost that is closer to the actual 
conditions in the field. Real cost information for each criterion can be simulated using current cost standards. 
or based on experience/budget realization reports from toll road or other infrastructure projects that have 
been implemented. 

 

4. CONCLUSION 

Based on the obtained results. we can conclude that the utilization of an anisotropic approach in the toll 
road route planning can give the result of route analysis. which can minimize the cost. The cost model is 
getting more complex along with the addition of more criteria and may negate one criterion with other 
criteria. resulting in the insensitivity of route analysis to specific criteria. particularly on criteria that have low 
weight and/or criteria that use generalized data. Nevertheless. these findings are not fixed because there is 
a possibility that the cost model can be more sensitive to low weight criteria. as long as it is applied a proper 
cost assessment strategy and/or represents the real cost condition in the field. 

The existence of criteria that have real cost values such as land value (which used in this study) can be 
useful to estimate the project budgets and costs. which is essential in the planning stage of infrastructure 
development programs. in order to obtain appropriate planning products.The obtained route from this study 
is also shorter about 8 kilometers compared to government's preferred route. allowing road users to save a 
certain amount of resources when the toll road has been operational. These findings indicate that there are 
alternative routes to government's preferred route. which is environmentally friendly and helpful to support 
food security sustainability.    

 

This study still has some limitations related to the implementation of SMCA. LCP. and AHP analysis to 
support the route planning of the Bawen - Yogyakarta Toll Road. There are several criteria for toll road 
planning that still use a relatively subjective value assessment and have not been involved in research. both 



 
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in terms of road planning regulations. toll road planning feasibility standards. and conditions and complexity 
of problems in the field. This research is carried out on a regional scale and uses publicly available data with 
different quality in terms of scale and updates.  

 

4. ACKNOWLEDGMENTS 

The authors would like to acknowledge the Ministry of Public Works and Housing of Republic Indonesia who 
funded the research under the Karyasiswa Kedinasan Scholarship Program.  

 

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