Article
citation information:
Sangsrichan, C., Sungtrisearn,
P., Pichayapan, P., Nitayaprapha,
T. Weighting transit-oriented development indicators for regional railway
stations in Thailand using the Spherical Fuzzy Analytic Hierarchy Process. Scientific Journal of Silesian University of
Technology. Series Transport. 2025, 128,
237-249. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2025.128.13
Chaiwat Sangsrichan[1],
Patcharida Sungtrisearn[2],
Preda Pichayapan[3],
Thanasit Nitayaprapha[4]
WEIGHTING
TRANSIT-ORIENTED DEVELOPMENT INDICATORS FOR REGIONAL RAILWAY STATIONS IN
THAILAND USING THE SPHERICAL FUZZY ANALYTIC HIERARCHY PROCESS
Summary. This research develops
an integrated assessment framework for evaluating Transit-Oriented Development
(TOD) potential around regional railway stations in Thailand using the
Spherical Fuzzy Analytic Hierarchy Process (SFAHP). An extensive literature
review was conducted to identify and analyze seven
main factors and 24 sub-indicators from previous TOD studies across different
railway station types. Expert evaluations were systematically incorporated to
determine the relative importance of these factors within Thailand's specific
context. The results indicate that density (20.1%) and diversity (18.1%) are
the most critical factors, followed by transit (15.1%), design (14.7%),
destination accessibility (11.7%), economic development (10.8%), and distance
to transit (9.5%). Among the sub-indicators, land use diversity, population
density, and level of mixed land use emerged as the most influential elements.
The SFAHP methodology effectively addressed the uncertainty and complexity in
expert judgments, resulting in a more robust evaluation system than traditional
methods. This assessment framework offers a valuable tool for policymakers,
urban planners, and developers to prioritize investment and development efforts
in Thailand's expanding regional rail network. The findings provide significant
implications for integrating transportation and land use planning to achieve
sustainable urban development in Thailand's regional context, ultimately
supporting the country's national strategic goals for infrastructure
development.
Keywords: transit-oriented development, regional railway stations, spherical
fuzzy AHP, sustainable suburban development, TOD indicators
1. INTRODUCTION
Transit-oriented development (TOD) is a globally
recognized urban planning approach aimed at creating sustainable communities centered around public transportation hubs. This concept
emphasizes the development of mixed-use, high-density, and pedestrian-friendly
environments within walking distance of transit stations [1]. In Thailand, the
application of TOD principles to regional rail networks has gained significant
importance as the country continues to expand its railway infrastructure under
the 20-Year National Strategy (2018-2037) and Thailand's Transport
Infrastructure Development Strategy, which seeks to position rail as Thailand's
primary transportation network [2].
Thailand's regional rail system examined in this
research consists of four main corridors spanning approximately 2,680 kilometers with 92 stations. The Northern Line connects
Bangkok to Chiang Mai Station through key economic centers
such as Lopburi, Nakhon Sawan, and Phitsanulok. The
Northeastern Line extends from Bangkok to Nong Khai, while the Eastern Line
links Bangkok to Aranyaprathet, facilitating cross-border movement with
Cambodia. The Southern Line runs from Bangkok to Hat Yai Junction, serving as a
vital connection for goods transport and tourism between the central and
southern regions [3].
Despite the growing interest in implementing TOD
around regional railway stations in Thailand, there are considerable challenges
in assessing the development potential of these areas. Notably, existing TOD
indicator frameworks were developed primarily for urban environments in more
developed economies. They may not adequately reflect the unique socioeconomic,
physical, and cultural characteristics of Thailand's regional contexts.
Additionally, conventional assessment approaches often lack systematic methodologies
for weighting indicators according to their relative importance in the Thai
setting, potentially resulting in evaluations that fail to capture the true
development potential of these areas [4].
This research addresses these limitations by
developing an integrated framework for evaluating Transit-Oriented Development
(TOD) potential around regional railway stations in Thailand, utilizing the
Spherical Fuzzy Analytic Hierarchy Process (SFAHP). This methodology offers
distinct advantages over traditional approaches through its ability to
incorporate uncertainty and complex expert judgments via three-dimensional
membership functions [5]. The SFAHP enables the systematic determination of the
relative importance weights for both of main factors and sub-criteria while
accounting for the inherent complexity and variability in expert assessments
when evaluating TOD indicators specific to Thailand's context [6].
The anticipated outcomes of this research have
significant implications for urban and transportation planning in Thailand. By
establishing a tailored set of weighted indicators that reflect Thailand's
specific development needs and regional characteristics, this work will provide
planners, policymakers, and developers with a more accurate tool for evaluating
TOD potential around regional railway stations. Furthermore, by ranking station
areas according to their development potential, this research will inform
strategic investment decisions and guide the implementation of TOD initiatives
based on each area's unique attributes and readiness. These contributions will
ultimately support the sustainable development of regional centers
throughout Thailand, improving the quality of life for residents in alignment
with the country's broader national development objectives.
2. Literature Review
2.1.
Literature Review of TOD Factors and Indicators
The
literature review reveals comprehensive insights into Transit-Oriented
Development (TOD) factors and indicators from previous research, as shown in
Table 1, which presents seven main factors: Density (D1), Diversity (D2),
Design (D3), Distance to transit (D4), Destination accessibility (D5), Transit
(TS), and Economic development (EC), categorized by railway station types
including central, regional main, sub-regional, and other related stations.
Table 2 provides further details on the 24 sub-indicators under these main
factors that researchers have employed to evaluate TOD potential across
different types of railway stations, illustrating which indicators have been
prioritized in various studies and contexts. Table 3 provides in-depth
information on the definitions and measurement methods of these sub-indicators
from previous research, which is invaluable for developing an appropriate
assessment framework for Thailand's context. This includes measures such as
population density, land use diversity, quality of street and pedestrian
design, as well as economic indicators and transit connectivity. This
systematic categorization demonstrates the evolution of TOD assessment
approaches and provides a solid foundation for selecting and adapting
indicators that are most relevant to regional railway stations in Thailand.
Tab.
1
Study of Main TOD Factors from Previous
Research Categorized by Railway Station Types
|
No. |
Main factors |
Central railway station |
Regional Main railway station |
Sub-Regional railway station |
Other related |
||||||||
|
[7] |
[8] |
[9] |
[10] |
[11] |
[12] |
[13] |
[14] |
[15] |
[16] |
[17] |
[18] |
||
|
D1 |
Density |
● |
● |
● |
● |
● |
● |
● |
● |
● |
● |
● |
● |
|
D2 |
Diversity |
● |
● |
● |
● |
● |
|
● |
● |
● |
● |
● |
● |
|
D3 |
Design |
● |
● |
● |
● |
● |
● |
● |
● |
|
|
● |
● |
|
D4 |
Distance
to transit |
● |
|
● |
|
● |
|
|
|
|
● |
|
● |
|
D5 |
Destination accessibility |
● |
|
● |
● |
● |
|
● |
|
|
|
|
● |
|
TS |
Transit |
● |
|
● |
|
● |
|
● |
|
● |
● |
|
|
|
EC |
Economic development |
● |
|
● |
|
● |
● |
|
|
● |
● |
|
|
Tab.
2
Study of TOD Sub-Indicators
Categorized by Railway Station Types
|
No. |
Sub-factors (Indicators) |
Regional Main railway station |
Sub-Regional railway station |
Other related |
|||||
|
[11] |
[12] |
[13] |
[14] |
[15] |
[16] |
[17] |
[18] |
||
|
D11 |
Population
density |
● |
● |
● |
● |
● |
● |
● |
● |
|
D12 |
Commercial density |
● |
● |
|
|
● |
● |
|
|
|
D13 |
Employment/Job
density |
|
● |
● |
|
● |
● |
● |
● |
|
D14 |
Business density |
● |
|
|
● |
|
|
|
● |
|
D21 |
Land use diversity |
● |
● |
● |
● |
|
|
|
● |
|
D22 |
Mixed land use |
● |
|
|
|
● |
● |
|
● |
|
D23 |
Level of mixed land use |
|
|
|
|
● |
● |
|
|
|
D31 |
Intersection density |
|
● |
|
● |
|
|
● |
● |
|
D32 |
Walkable/Cyclable infrastructure |
|
● |
|
● |
|
|
● |
● |
|
D33 |
Street
network characteristics |
|
|
● |
|
|
|
● |
|
|
D34 |
Station
design elements |
|
|
● |
|
|
|
● |
|
|
D41 |
Accessibility
to station |
|
|
|
|
● |
● |
|
|
|
D42 |
Walking Distance to transit facilities |
|
|
● |
|
|
|
|
● |
|
D43 |
Distance
to bus stops |
|
|
● |
|
|
|
|
● |
|
D51 |
Access to job opportunities |
● |
|
|
|
|
|
|
● |
|
D52 |
Access to services and amenities |
|
|
|
|
|
|
● |
● |
|
D53 |
Connectivity
to destinations |
|
|
|
● |
|
|
|
● |
|
TS1 |
Passenger
volume |
|
|
● |
|
● |
● |
|
|
|
TS2 |
Safety
and amenities |
|
|
|
|
● |
● |
|
|
|
TS3 |
Intermodal
connectivity |
|
|
|
|
● |
● |
|
● |
|
TS4 |
Parking facilities |
|
|
|
|
● |
● |
|
● |
|
EC1 |
Business measures |
● |
|
|
|
● |
● |
|
|
|
EC2 |
Employment
measures |
|
● |
|
|
● |
● |
|
|
Tab.
3
Study of Measurement Methods
and Definitions of TOD Sub-Indicators
|
No. |
Sub-factors (Indicators) |
Definition |
Measurement |
|
D11 |
Population
density |
Population density per square kilometer |
[11]: Higher residential and commercial densities
are required for more efficient public transport. [13]: Population per square kilometer
(Person/km²). [15]: Population/sq km. [16]: Minimum 1500 persons/km²/Local Authority. |
|
D12 |
Commercial density |
Commercial activity density per square kilometer |
[11]: Higher commercial densities support more
efficient public transport. [15]: Commercial activity/sq
km. [16]: Minimum 20% from TOD zone. |
|
D13 |
Employment/ Job density |
Job density per square kilometer |
[12]: Employment density as a key factor for
Transit-Oriented Development (TOD). [15]: Jobs total/sq km. [16]: Minimum 20% from TOD zone. |
|
D14 |
Business density |
Number of business establishments per unit area |
[11]: The higher number of business establishments
represents a higher level of economic development and, hence, higher TOD
levels. [14]: Concentration of businesses in TOD area. |
|
D21 |
Land use diversity |
Diversity of land uses measured using indexes like
Shannon-Wiener |
[11]: Higher diversity of land use reduces vehicular
trips and enhances the liveliness and safety of a place where people
socialize. [13]: Measured using Shannon-Wiener Index. |
|
D22 |
Mixed land use |
Degree of mixed land uses with respect to
residential use |
[11]: Higher mixedness of land uses (w.r.t
residential land use) encourages a higher degree of walking and cycling for
non-work trips. [15]: Measured using dissimilarity index, activity center mixture, and commercial intensities. [16]: A minimum of 100% of the TOD zone can be
developed. |
|
D23 |
Level of mixed land use |
Percentage of mixed land use in TOD area |
[15]: Mixed land use of housing and others. [16]: A minimum of 50% of the TOD area is mixed land
use. |
|
D31 |
Intersection density |
Number of road intersections per unit area |
[12]: Density of road intersections. [14]: Number of intersections per unit area. |
|
D32 |
Walkable/ Cyclable infrastructure |
Length of infrastructure suitable for walking and
cycling |
[12]: Total length of road fit for walking and
cycling. [14]: Length of bicycle and pedestrian networks. |
|
D33 |
Street network characteristics |
Road length, block face length, sidewalks, lights,
etc. |
[13]: Road length per catchment (km). [17]: Block face length, proportion of blocks with
sidewalks, planting strips, overhead lights, flat terrain (< 5% slope),
quadrilateral shape. |
|
D34 |
Station design elements |
Features like number of exits, lighting,
accessibility |
[13]: Number of exits per railway station. [17]: Distance between overhead lights (feet). |
|
D41 |
Accessibility
to station |
Spatial readiness and population with access to
transit node |
[15]: Spatial readiness and total population that
can afford the transit node. [16]: Minimum Distance is 400/800 m from station. |
|
D42 |
Walking Distance to transit facilities |
Distance to transit facilities based on walkable
principles |
[13]: Based on general TOD principles regarding
walkable Distance to transit facilities. |
|
D43 |
Distance to bus stops |
Proximity and number of bus stops per catchment area |
[13]: Bus stops per unit catchment area. |
|
D51 |
Access to job opportunities |
Access to jobs within walkable distance of transit
node |
[11]: Access to job opportunities within a walkable
Distance of a transit node. [17]: Accessibility index to all jobs (via auto). |
|
D52 |
Access to services and amenities |
Access to retail, services, recreation within
walkable distance |
[17]: Access to sales and services jobs (via walk),
per developed acre rates of retail stores, activity centers,
parks, and recreational sites. |
|
D53 |
Connectivity
to destinations |
Connectivity between transit node and key
destinations |
[14]: Connectivity between transit node and key
destinations in the area. |
|
TS1 |
Passenger
volume |
Passenger capacity during peak and non-peak hours |
[15]: Total passenger/transport capacity during peak
hour and outside peak hour. [16]: 300 passengers (2 trips) during peak hours;
100 persons (1 trip) during non-peak hours. [13]: Average daily commuters per station (Person). |
|
TS2 |
Safety
and amenities |
Safety features and passenger amenities at stations |
[15]: Waiting and vehicle safety; station amenities
(shelter, seating, shops, lighting); information panels; accessibility
features. [16]: Security (CCTV, guards); facilities (seating,
toilets, cafeteria, ventilation); information displays (boards, LED signs,
directions). |
|
TS3 |
Intermodal
connectivity |
Connection between routes and transport modes |
[15]: Connection between routes and modes of
connectivity. [16]: Minimum one mode of public transportation
connected with the station; minimum one route connected to the station. |
|
TS4 |
Parking facilities |
Ratio of users to parking spaces, bicycle parking,
etc. |
[15]: User and space ratio. [16]: Existing parking for cars, bicycles, and
specific groups (such as disabled persons). |
|
EC1 |
Business measures |
Number of businesses and level of economic
development |
[11]: The number of business establishments
represents a higher level of economic development. [15]: Total business/sq
km. [16]: Minimum 20% from Local Authority jurisdiction. |
|
EC2 |
Employment
measures |
Tax earnings, investment, employment levels |
[12]: Tax earnings of municipalities. [15]: Total investment. [16]: Minimum RM 100 million/year (Majlis Perbandaran); Minimum RM 50 million/year (Majlis Daerah);
Minimum 30% of land use are industry and commercial. |
2.2.
Methodology of Sphere-Based Fuzzy Multi-Criteria Decision Analysis
Spherical
fuzzy sets (SFS) provide a valuable framework for addressing uncertainty and
complexity in expert judgments for multi-criteria decision analysis. This
methodology was applied to assess the Transit-Oriented Development (TOD)
potential around regional railway stations in Thailand. Table 4 in the
research presents the linguistic scale for pairwise comparisons in the
Spherical Fuzzy Analytic Hierarchy Process, defining membership (μ),
non-membership (ν),
and hesitancy (π)
degrees for each importance level, along with corresponding Score Indices (SI)
for converting linguistic judgments into numerical values for computation [6].
The scale ranges from "Absolutely Higher Importance" (AHI) with
values (0.90, 0.10, 0.00) and a score of 9, to "Absolutely Lower
Importance" (ALI) with values (0.10, 0.90, 0.00) and a score of 0.11.
Tab.
4
Spherical fuzzy linguistic
scale for criteria pairwise comparisons
|
Linguistic Term |
(μ,
ν, π) |
Score Index (SI) |
|
Higher Importance (AHI) |
0.90, 0.10, 0.00 |
9 |
|
Very High Importance (VHI) |
0.85, 0.15, 0.04 |
8 |
|
0.80, 0.20, 0.10 |
7 |
|
|
0.75, 0.25, 0.14 |
6 |
|
|
High Importance (HI) |
0.70, 0.30, 0.20 |
5 |
|
0.65, 0.35, 0.23 |
4 |
|
|
Slightly Higher Importance (SHI) |
0.60, 0.40, 0.30 |
3 |
|
0.55, 0.45, 0.30 |
2 |
|
|
Equally Important (EI) |
0.50, 0.40, 0.40 |
1 |
|
0.45, 0.55, 0.30 |
0.5 |
|
|
Slightly Lower Importance (SLI) |
0.40, 0.60, 0.30 |
0.33 |
|
0.35, 0.65, 0.23 |
0.25 |
|
|
Lower Importance (LI) |
0.30, 0.70, 0.20 |
0.20 |
|
0.25, 0.75, 0.14 |
0.17 |
|
|
Very Low Importance (VLI) |
0.20, 0.80, 0.10 |
0.14 |
|
0.15, 0.85, 0.04 |
0.13 |
|
|
Lower Importance (ALI) |
0.10, 0.90, 0.00 |
0.11 |
For
converting linguistic judgments to numerical values in pairwise comparisons,
the research used the following formulas:
(1)
for AMI; VHI;
HI; SMI; and EI
(2)
for EI; SLI;
LI; VLI; and ALI.
The
research followed a systematic approach to determine the relative importance of
criteria and sub-criteria through the following steps:
1. Expert evaluations were collected using the linguistic terms
presented in Table 4.
2. Pairwise comparison matrices were constructed for main factors
(Table 5) and sub-indicators (Table 6-12).
3. For each comparison, the membership (μ),
non-membership (ν),
and hesitancy (π)
values were recorded.
4. The score (Sw̃sj)
was calculated for each factor or sub-factor.
5. Finally, normalized weights (w̄s)
were determined.
This
SFAHP methodology effectively addressed the uncertainty and complexity in
expert judgments, resulting in a more robust evaluation system than traditional
methods for evaluating TOD potential around regional railway stations in
Thailand.
3. Analysis Results
Tables
5 through 12 present the results of pairwise comparisons of various factors for
evaluating TOD potential around regional railway stations using the Spherical
Fuzzy Analytic Hierarchy Process (SFAHP). As shown in Table 5, the comparison
of seven main factors reveals that Density (D1) has the highest importance
weight (0.201), followed by Diversity (D2) at 0.181 and Transit (TS) at 0.151.
Tables 6-12 display the pairwise comparisons of sub-indicators under each main
factor: Table 6 shows comparisons of density sub-indicators (D11-D14) with
population density (D11) having the highest weight (0.318); Table 7 presents
diversity sub-indicators (D21-D23) with land use diversity (D21) receiving the
highest weight (0.356); and Tables 8-12 show similar results for the
sub-indicators of design, Distance to transit, destination accessibility,
transit, and economic development factors, respectively. Each table provides
the membership (μ),
non-membership (ν),
and hesitancy (π)
values of the spherical fuzzy sets, along with the score (Sw̃sj)
and normalized weights (w̄s)
resulting from the aggregation of three experts' opinions. This comprehensive
analysis yields reliable importance weights that systematically reflect the
complexity of decision-making in TOD assessment, accounting for uncertainty and
expert judgment in a structured mathematical framework.
Tab.
5
Aggregated pairwise
comparisons of main factors
|
D1 |
D2 |
D3 |
D4 |
D5 |
TS |
EC |
w̃s |
Sw̃sj |
w̄s |
|
|
D1 |
(0.50, |
(0.62, 0.39, 0.23) |
(0.68, 0.32, 0.18) |
(0.77, 0.24, 0.12) |
(0.66, 0.35, 0.20) |
(0.58, 0.42, 0.27) |
(0.71, 0.31, 0.15) |
(0.65, 0.35, 0.22) |
17.43 |
0.201 |
|
D2 |
(0.43, 0.57, 0.25) |
(0.50, 0.40, 0.40) |
(0.63, 0.37, 0.22) |
(0.74, 0.27, 0.14) |
(0.69, 0.33, 0.15) |
(0.54, 0.46, 0.30) |
(0.64, 0.37, 0.19) |
(0.60, 0.40, 0.24) |
15.72 |
0.181 |
|
D3 |
(0.32, 0.68, 0.19) |
(0.37, 0.63, 0.23) |
(0.50, 0.40, 0.40) |
(0.70, 0.31, 0.18) |
(0.62, 0.39, 0.20) |
(0.57, 0.44, 0.25) |
(0.61, 0.42, 0.22) |
(0.53, 0.46, 0.24) |
12.68 |
0.147 |
|
D4 |
(0.24, 0.76, 0.12) |
(0.29, 0.72, 0.16) |
(0.33, 0.69, 0.21) |
(0.50, 0.40, 0.40) |
(0.46, 0.55, 0.28) |
(0.40, 0.61, 0.24) |
(0.47, 0.58, 0.27) |
(0.38, 0.63, 0.25) |
8.25 |
0.095 |
|
D5 |
(0.38, 0.63, 0.20) |
(0.32, 0.69, 0.18) |
(0.41, 0.60, 0.22) |
(0.56, 0.45, 0.29) |
(0.50, 0.40, 0.40) |
(0.45, 0.58, 0.26) |
(0.54, 0.49, 0.23) |
(0.45, 0.55, 0.26) |
10.12 |
0.117 |
|
TS |
(0.44, 0.58, 0.29) |
(0.49, 0.53, 0.32) |
(0.43, 0.58, 0.27) |
(0.61, 0.40, 0.24) |
(0.58, 0.44, 0.26) |
(0.50, 0.40, 0.40) |
(0.59, 0.42, 0.20) |
(0.52, 0.49, 0.28) |
13.05 |
0.151 |
|
EC |
(0.31, 0.71, 0.16) |
(0.38, 0.64, 0.20) |
(0.41, 0.62, 0.24) |
(0.53, 0.49, 0.28) |
(0.48, 0.52, 0.25) |
(0.43, 0.60, 0.21) |
(0.50, 0.40, 0.40) |
(0.43, 0.59, 0.25) |
9.36 |
0.108 |
Tab.
6
Aggregated pairwise
comparisons of density sub-factors
|
D11 |
D12 |
D13 |
D14 |
w̃s |
Sw̃sj |
w̄s |
|||||||||||
|
D11 |
0.50 |
0.40 |
0.40 |
0.64 |
0.36 |
0.22 |
0.71 |
0.29 |
0.16 |
0.68 |
0.32 |
0.18 |
0.63 |
0.35 |
0.24 |
16.92 |
0.318 |
|
D12 |
0.38 |
0.63 |
0.24 |
0.50 |
0.40 |
0.40 |
0.58 |
0.42 |
0.26 |
0.61 |
0.39 |
0.20 |
0.52 |
0.46 |
0.28 |
13.47 |
0.253 |
|
D13 |
0.30 |
0.71 |
0.17 |
0.44 |
0.57 |
0.28 |
0.50 |
0.40 |
0.40 |
0.57 |
0.44 |
0.27 |
0.45 |
0.53 |
0.27 |
11.21 |
0.210 |
|
D14 |
0.35 |
0.66 |
0.19 |
0.42 |
0.60 |
0.22 |
0.45 |
0.56 |
0.29 |
0.50 |
0.40 |
0.40 |
0.43 |
0.55 |
0.28 |
11.68 |
0.219 |
Tab.
7
Aggregated pairwise
comparisons of diversity sub-factors
|
D21 |
D22 |
D23 |
w̃s |
Sw̃sj |
w̄s |
|||||||||
|
D21 |
0.50 |
0.40 |
0.40 |
0.62 |
0.38 |
0.23 |
0.58 |
0.42 |
0.25 |
0.57 |
0.40 |
0.29 |
14.85 |
0.356 |
|
D22 |
0.40 |
0.61 |
0.25 |
0.50 |
0.40 |
0.40 |
0.54 |
0.46 |
0.29 |
0.48 |
0.49 |
0.31 |
12.35 |
0.296 |
|
D23 |
0.44 |
0.57 |
0.27 |
0.47 |
0.53 |
0.31 |
0.50 |
0.40 |
0.40 |
0.47 |
0.50 |
0.33 |
14.49 |
0.348 |
Tab.
8
Aggregated pairwise
comparisons of design sub-factors
|
D31 |
D32 |
D33 |
D34 |
w̃s |
Sw̃sj |
w̄s |
|||||||||||
|
D31 |
0.50 |
0.40 |
0.40 |
0.47 |
0.53 |
0.28 |
0.55 |
0.45 |
0.26 |
0.62 |
0.38 |
0.23 |
0.54 |
0.44 |
0.29 |
13.76 |
0.271 |
|
D32 |
0.54 |
0.46 |
0.29 |
0.50 |
0.40 |
0.40 |
0.59 |
0.41 |
0.24 |
0.67 |
0.33 |
0.19 |
0.58 |
0.40 |
0.28 |
15.32 |
0.302 |
|
D33 |
0.47 |
0.53 |
0.27 |
0.42 |
0.58 |
0.26 |
0.50 |
0.40 |
0.40 |
0.56 |
0.44 |
0.27 |
0.49 |
0.49 |
0.30 |
12.48 |
0.246 |
|
D34 |
0.39 |
0.62 |
0.24 |
0.35 |
0.66 |
0.22 |
0.45 |
0.55 |
0.28 |
0.50 |
0.40 |
0.40 |
0.42 |
0.56 |
0.29 |
9.21 |
0.181 |
Tab.
9
Aggregated pairwise
comparisons of Distance to transit sub-factors
|
D41 |
D42 |
D43 |
w̃s |
Sw̃sj |
w̄s |
|||||||||
|
D41 |
0.50 |
0.40 |
0.40 |
0.59 |
0.41 |
0.24 |
0.63 |
0.37 |
0.22 |
0.57 |
0.39 |
0.29 |
15.32 |
0.372 |
|
D42 |
0.42 |
0.59 |
0.25 |
0.50 |
0.40 |
0.40 |
0.56 |
0.44 |
0.27 |
0.49 |
0.48 |
0.31 |
13.15 |
0.319 |
|
D43 |
0.38 |
0.63 |
0.23 |
0.45 |
0.55 |
0.29 |
0.50 |
0.40 |
0.40 |
0.44 |
0.53 |
0.31 |
12.74 |
0.309 |
Tab.
10
Aggregated pairwise
comparisons of destination accessibility sub-factors
|
D51 |
D52 |
D53 |
w̃s |
Sw̃sj |
w̄s |
|||||||||
|
D51 |
0.50 |
0.40 |
0.40 |
0.48 |
0.52 |
0.29 |
0.54 |
0.46 |
0.27 |
0.51 |
0.46 |
0.32 |
13.28 |
0.333 |
|
D52 |
0.53 |
0.47 |
0.30 |
0.50 |
0.40 |
0.40 |
0.57 |
0.43 |
0.25 |
0.53 |
0.43 |
0.32 |
14.12 |
0.354 |
|
D53 |
0.47 |
0.53 |
0.28 |
0.44 |
0.56 |
0.26 |
0.50 |
0.40 |
0.40 |
0.47 |
0.50 |
0.33 |
12.52 |
0.313 |
Tab.
11
Aggregated pairwise
comparisons of transit sub-factors
|
TS1 |
TS2 |
TS3 |
TS4 |
w̃s |
Sw̃sj |
w̄s |
|||||||||||
|
TS1 |
0.50 |
0.40 |
0.40 |
0.57 |
0.43 |
0.25 |
0.61 |
0.39 |
0.23 |
0.66 |
0.34 |
0.19 |
0.59 |
0.39 |
0.27 |
15.53 |
0.294 |
|
TS2 |
0.44 |
0.56 |
0.26 |
0.50 |
0.40 |
0.40 |
0.55 |
0.45 |
0.27 |
0.59 |
0.41 |
0.24 |
0.52 |
0.46 |
0.29 |
13.25 |
0.251 |
|
TS3 |
0.40 |
0.60 |
0.24 |
0.46 |
0.54 |
0.28 |
0.50 |
0.40 |
0.40 |
0.54 |
0.46 |
0.29 |
0.48 |
0.50 |
0.30 |
12.11 |
0.229 |
|
TS4 |
0.35 |
0.65 |
0.21 |
0.42 |
0.58 |
0.25 |
0.47 |
0.53 |
0.30 |
0.50 |
0.40 |
0.40 |
0.44 |
0.54 |
0.29 |
11.95 |
0.226 |
Tab.
12
Aggregated pairwise
comparisons of economic development sub-factors
|
EC1 |
EC2 |
w̃s |
Sw̃sj |
w̄s |
|||||||
|
EC1 |
0.50 |
0.40 |
0.40 |
0.53 |
0.47 |
0.29 |
0.52 |
0.44 |
0.35 |
13.65 |
0.531 |
|
EC2 |
0.48 |
0.52 |
0.30 |
0.50 |
0.40 |
0.40 |
0.49 |
0.46 |
0.35 |
12.04 |
0.469 |
Table
13 presents the importance weights of primary factors and sub-indicators for
evaluating the potential of Transit-Oriented Development around regional
railway stations, which is the final result of the Spherical Fuzzy Analytic
Hierarchy Process (SFAHP) analysis. As shown in Table 13, Density (D1) has the
highest importance weight (0.201), followed by Diversity (D2) at 0.181, Transit
(TS) at 0.151, Design (D3) at 0.147, Destination accessibility (D5) at 0.117,
Economic development (EC) at 0.108, and Distance to transit (D4) at 0.095,
respectively. Furthermore, this table displays the importance weights of all 24
sub-indicators, categorized into local weights (comparative importance within
the same group of sub-indicators) and global weights (calculated by multiplying
the local weight of a sub-indicator by the weight of its parent main factor).
The top five sub-indicators with the highest global weights are Land use
diversity (D21) at 0.064, Population density (D11) at 0.064, Level of mixed
land use (D23) at 0.063, Business measures (EC1) at 0.057, and Mixed land use
(D22) at 0.054. These weights are crucial for applying the assessment framework
to evaluate and prioritize areas surrounding regional railway stations in
Thailand, providing a systematic approach to identify locations with the
highest TOD development potential based on a comprehensive set of indicators
that have been weighted according to their relative importance in the Thai
context.
Tab.
13
The weights of main and
sub-criteria
|
Main criteria |
Local weights of main criteria |
Sub-factors |
Local weights of sub-factors |
Global weights of sub-factors |
|
Density (D1) |
0.201 |
D11 |
0.318 |
0.064 |
|
D12 |
0.253 |
0.051 |
||
|
D13 |
0.210 |
0.042 |
||
|
D14 |
0.219 |
0.044 |
||
|
Diversity (D2) |
0.181 |
D21 |
0.356 |
0.064 |
|
D22 |
0.296 |
0.054 |
||
|
D23 |
0.348 |
0.063 |
||
|
Design (D3) |
0.147 |
D31 |
0.271 |
0.040 |
|
D32 |
0.302 |
0.044 |
||
|
D33 |
0.246 |
0.036 |
||
|
D34 |
0.181 |
0.027 |
||
|
Distance to transit (D4) |
0.095 |
D41 |
0.372 |
0.035 |
|
D42 |
0.319 |
0.030 |
||
|
D43 |
0.309 |
0.029 |
||
|
Destination accessibility (D5) |
0.117 |
D51 |
0.333 |
0.039 |
|
D52 |
0.354 |
0.041 |
||
|
D53 |
0.313 |
0.037 |
||
|
Transit (TS) |
0.151 |
TS1 |
0.294 |
0.044 |
|
TS2 |
0.251 |
0.038 |
||
|
TS3 |
0.229 |
0.035 |
||
|
TS4 |
0.226 |
0.034 |
||
|
Economic development
(EC) |
0.108 |
EC1 |
0.531 |
0.057 |
|
EC2 |
0.469 |
0.051 |
||
|
SUM. |
1.000 |
|
7.000 |
1.000 |
5. Conclusion
This
research has successfully developed an integrated assessment framework for
evaluating Transit-Oriented Development (TOD) potential around regional railway
stations in Thailand using the Spherical Fuzzy Analytic Hierarchy Process
(SFAHP). Through a comprehensive analysis of TOD factors and indicators from
previous research, combined with expert evaluations, we have established that
density (20.1%) and diversity (18.1%) are the most critical factors affecting
TOD potential, followed by transit (15.1%), design (14.7%), destination
accessibility (11.7%), economic development (10.8%), and distance to transit
(9.5%). At the sub-indicator level, land use diversity (6.4%), population
density (6.4%), and level of mixed land use (6.3%) emerged as the most significant
elements for successful TOD implementation in Thailand's regional context. This
prioritization differs notably from traditional TOD models developed in more
urbanized contexts, reflecting Thailand's unique development patterns,
socioeconomic conditions, and transportation needs.
The
SFAHP methodology has proven particularly valuable for this assessment, as it
effectively handles the uncertainty and subjectivity inherent in expert
judgments, providing a more nuanced representation of decision-making processes
than traditional methods. The weighted indicator system developed in this study
offers several practical applications: for policymakers and urban planners, it
provides a systematic tool to prioritize investment and development efforts;
for transportation agencies, it provides guidance for integrating land use and
transit planning more effectively; and for developers, it indicates which
locations and specific aspects of development should be prioritized to create
successful TOD projects. As Thailand continues to expand its regional rail
network under the 20-Year National Strategy, this assessment framework will be
instrumental in guiding sustainable urban development patterns around transit
nodes, thereby maximizing the return on investment in rail infrastructure while
creating more livable and sustainable communities
throughout the country.
Future
research should focus on validating this framework through case studies of
specific regional railway stations, developing implementation guidelines for
various station typologies, and adapting the framework as Thailand's
transportation network and urban areas evolve. Additionally, incorporating
emerging factors such as climate resilience, innovative city technologies, and
post-pandemic spatial requirements would further enhance the applicability of
the TOD assessment framework in addressing future challenges and opportunities
in Thailand's regional development context.
References
1.
Peter
Newman, Jeffrey
Kenworthy. 2015. The End of Automobile Dependence: How Cities are Moving
Beyond Car-Based Planning. Washington DC: Island Press. ISBN: 978-1-61091-613-4. DOI:
https://doi.org/10.5822/978-1-61091-613-4.
2.
Office of Transport and Traffic
Policy and Planning. 2020. The 20-year Transportation Development Strategy
(2018-2037). Thailand: Ministry of Transport.
3.
State Railway of Thailand. 2022. Annual Report
2012. Thailand: State Railway of Thailand.
4.
Office of Transport and Traffic Policy and
Planning. 2021. Study on urban development with transportation
infrastructure systems (TOD) (Final report). Thailand: Ministry of
Transport.
5.
Kutlu Gündoğdu Fatma, Cengiz Kahraman. 2019.
"Spherical fuzzy sets and spherical fuzzy TOPSIS method". Journal
of Intelligent & Fuzzy Systems 36(1): 337-352. ISSN: 1064-1246. DOI:
10.3233/JIFS-181401.
6.
Kutlu Gündoğdu Fatma, Cengiz Kahraman. 2020.
"A novel spherical fuzzy analytic hierarchy process and its renewable
energy application". Soft Computing 24(6): 4607-4621. ISSN: 1432-7643. DOI:
10.1007/s00500-019-04222-w.
7.
Institute for Transportation and Development
Policy (ITDP). 2017. TOD Standard (3rd ed.). New York: ITDP. Available
at: https://www.itdp.org/publication/tod-standard/.
8.
Kyaw Nyunt Khin Thiri, Natachai Wongchavalidkul.
2020. "Evaluation of Relationships Between Ridership Demand and
Transit-Oriented Development (TOD) Indicators Focused on Land Use Density,
Diversity, and Accessibility: A Case Study of Existing Metro Stations in
Bangkok". Urban Rail Transit 6(1): 56-70. ISSN: 2199-6687. DOI: 10.1007/s40864-019-00122-2.
9.
Kumar P. Phani, Manoranjan
Parida, Ch. Ravi Sekhar. 2020. "Developing Context
Sensitive Planning Criteria for Transit Oriented Development (TOD): A
Fuzzy-Group Decision Approach". Transportation Research Procedia
48: 2421-2434. ISSN: 2352-1457. DOI:
10.1016/j.trpro.2020.08.278.
10. Uddin Md. Anwar, Tahsin
Tamanna, Saima Adiba, Sadib Bin Kabir. 2023. "Revolutionizing TOD planning
in a developing country: An objective-weighted framework for measuring nodal
TOD index". Journal of Advanced Transportation 2023: 9911133. ISSN: 0197-6729. DOI:
10.1155/2023/9911133.
11. Singh Yamini Jain, Pedram
Fard, Mark Zuidgeest, Mark Brussel, Martin van Maarseveen. 2014.
"Measuring transit oriented development: a spatial multi criteria
assessment approach for the City Region Arnhem and Nijmegen". Journal
of Transport Geography 35: 130-143. ISSN: 0966-6923. DOI:
10.1016/j.jtrangeo.2014.01.014.
12. Singh Y.J. 2015. „Measuring
transit-oriented development (TOD) at regional and local scales: a planning
support tool”. PhD thesis, Enschede, Netherlands: University of
Twente, Faculty of Geo-Information Science and Earth Observation (ITC).
13. Guo Jing, Fumihiko Nakamura,
Qiang Li, Yuan Zhou. 2018. "Efficiency assessment of transit-oriented
development by data envelopment analysis: Case study on the Den-en Toshi Line
in Japan". Journal of Advanced Transportation 2018: 6701484. ISSN: 0197-6729. DOI:
10.1155/2018/6701484.
14. Huang Runjie, Anna Grigolon,
Mafalda Madureira, Mark Brussel. 2018. "Measuring transit-oriented
development (TOD) network complementarity based on TOD node typology". Journal
of Transport and Land Use 11(1): 305-324. ISSN: 1938-7849. DOI: 10.5198/jtlu.2018.1110.
15. Ahmad Mohamad Sabri, Robiah
Suratman. 2020. "Critical review on suburban transit orientation
development". Planning Malaysia: Journal of the Malaysian Institute of
Planners 18(4): 365-374. ISSN: 1675-6215. DOI:
10.21837/pm.v18i14.838.
16. Ahmad Sabri, Mohamad Fadhli
Rashid, Robiah Suratman. 2022. "Readiness of Transit-Oriented Development
(TOD) Concept Implementation in Perak's Suburban Areas". Planning
Malaysia Journal 20(1): 155-165. ISSN: 1675-6215. DOI: 10.21837/pm.v20i20.1086.
17. Cervero Robert, Kara
Kockelman. 1997. "Travel demand and the 3Ds: Density, diversity, and
design". Transportation Research Part D: Transport and Environment
2(3): 199-219. ISSN: 1361-9209. DOI: 10.1016/S1361-9209(97)00009-6.
18. Xia Zhengwei, Weiyao Feng,
Hongshi Cao, Ye Zhang. 2024. "Understanding the Influence of Built
Environment Indicators on Transit-Oriented Development Performance According to
the Literature from 2000 to 2023". Sustainability 16(21): 9165.
ISSN: 2071-1050. DOI:
10.3390/su16219165.
Received 30.05.2025; accepted in revised form 19.08.2025
Scientific Journal of Silesian
University of Technology. Series Transport is licensed under a Creative
Commons Attribution 4.0 International License