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A Grid-Based Spatial Analysis for Detecting Supply–Demand Gaps of Public Transports: A Case Study of the Bangkok Metropolitan Region

School of Engineering and Technology, Department of Information and Communication Technologies, Asian Institute of Technology, Pathumthani 12120, Thailand
Center for Spatial Information Science, The University of Tokyo, Chiba 277-8568, Japan
School of Environment, Resources and Development, Department of Development and Sustainability, Asian Institute of Technology, Pathumthani 12120, Thailand
Author to whom correspondence should be addressed.
Sustainability 2020, 12(24), 10382;
Received: 14 September 2020 / Revised: 30 November 2020 / Accepted: 8 December 2020 / Published: 11 December 2020
(This article belongs to the Section Sustainable Transportation)


Public transport service has been promoted to reduce the problems of traffic congestion and environmental impacts due to car dependency. Several public transportation modes are available in Bangkok Metropolitan Region (BMR) such as buses, heavy rails, vans, boats, taxis, and trains while in some areas have fewer modes of public transport available. The disparity of public transport service negatively impacts social equity. This study aims to identify the gaps between public transport supply and demand and to demonstrate introduced indicators to assess the public transport performance incorporating transport capacity and equilibrium access aspects. Supply index was used to evaluate the level of service, and the demand index was applied to estimate travel needs. Furthermore, the Lorenz curves and the Gini coefficients were used to measure the equity of public transport. The results highlight that more than half of the BMR population is living in low-supply high-demand areas for public transportation. Moreover, the equitable access analysis has identified that the high-income population has better access to public transport than the low-income population. The results suggest that public transport gaps and equity indicate the inclusiveness of public transportation, as well as to the areas where to improve the public transport service. Thus, the methodology used in this study can be applied to another city or region similar to BMR.

1. Introduction

Sustainable transportation has become a popular theme in many research studies. Several of them have identified the critical challenges of the development and planning of urban transport, concerning sustainable transport and the reduction of environmental impacts [1,2]. In creating an effective transport plan, problems need to be defined to eliminate and solve issues, such as congestion resulting from increased vehicle ownership and a reduced use of public transport found to be degrading transport efficiency [3]. The problem that remains in public transport is that the distribution of public transport operates mainly in the central business district and inner suburbs whereas other modes such as buses, vans, and trains service further to the outer suburbs. Moreover, the transit system fails to meet people who rely on public transit for daily commuting, including the mobility inequality of specific social groups, especially for people with a disability [4,5]. Furthermore, people should have equal access to public transport resources, and to achieve equity in public transport accessibility is to provide public transport to those areas people need it most [6]. Providing public transport infrastructures and balancing the distribution of the public transportation networks allow people to access material assets, social connections, and opportunities for jobs, including enhanced quality of life in the freedom of movement [7]. For this reason, identifying equity of distribution of public transport networks is essential for improving and planning the public transport resources.
In addition, the growth of population and urban sprawl creates the expansion of the road capacity and consequently induces traffic growth and congestion problems [8]. For example, in India, rapid population growth has resulted in excessive demand for transportation with limited transport infrastructure resulting in a transport crisis such as exhausting commutes to jobs using either slow, overcrowded public transport or dangerous motorcycles [9]. Additionally, previous studies have shown that the congestion problem is increasing in major cities in developing countries across the world. In Bangkok, private and other vehicles move at a speed of only 10 km/h on average during the rush hours [10]. Bangkok has gained workers from rural provinces that have caused rapid urbanization in the metropolitan areas as well as traffic congestion problems due to uncontrollable population growth [11,12]. Population growth and urban sprawl, including the expansion of road capacity due to traffic growth and congestion, can lead to energy wastage and environmental problems [13]. Previous studies also discussed the issue of the undisciplined distribution of residential, work/study, and commercial areas, including inadequate public transport and infrastructure, forcing people to travel in their vehicle, and subsequently creating more traffic problems in the urban area [14].
In Thailand, the provision of an efficient public transport system was prioritized to improve the service level and accessibility for the communities. National, metropolitan, and provincial governments have endeavored to solve the transport problems by formulating a development plan to increase the use of public transportation. For example, the government of Thailand established the strategy on the improvement of supporting factors for transportation infrastructure within the framework of the National Transport Master Plan, based on the National Plans for Social and Economic Development together with transport integration and efficiency plans for promoting public transportation. Recently, the 12th national master plan for 2011–2020 included transport in the development strategies for the further development of infrastructure and logistics with the vision “On the way to sustainable transport” (OTP, 2015). The government’s intention for sustainable transport has impacted a priority project and plan set out in the 20-year strategy for developing the Thai transport system (2017–2036), which embraces the concept of green transport, transport efficiency, and inclusive transport. The goal of the strategy is to improve people’s quality of life, support for social change, diving into business and increasing competitiveness [15]. In the short term, the government’s goal was to increase the proportion of passengers using public transport systems in urban areas and the proportion of passengers in the Bangkok Metropolitan Region (BMR) using the urban transport system from 5% to 15% by 2021 [16]. Further, the Office of Transport and Transport Policy and Planning (OTP) has developed the Transport Performance Index, which contains the public transport performance indicators to assess the competitiveness of the country and is used to plan and develop suitable and effective transport and logistics [17].
However, many important questions of sustainable transport remain and are challenging for development due to the spatial distribution of public transport service and how to create a well-designed public transit network that meets the entire population’s needs [18]. For example, in the previous study for Bangkok, workers travel across the city from suburban areas to the large inner core of Bangkok, where most of the urban activity is concentrated [19]. Regarding the BMR travel mode statistic in 2017 by OTP [20], the proportion of private mode is around 69%, while the public mode is around 24%; the rest is office/school buses and walk. Some research studies for BMR have shown that increasing the share of public transport instead of private transport can lead to sustainable transport and an improvement in people’s quality of life [21,22]. According to the challenges of urban transport development and planning with a consideration of sustainable transportation concerning the reduction of environmental impacts, many countries around the world are also promoting the use of sustainable transport to enhance sustainable mobility. For example, the use of bike-sharing is one of the solutions for reducing environmental impacts [23,24,25]. Another way of promoting sustainable transport is the use of park and ride systems, which reduces the volume of the traffic and road congestion by allowing people to leave their vehicle in the parking and continue their travel by using public transport such as bus, metro, and rail system [26,27]. Additionally, many of the important issues in traffic planning are also related to the population size in which a large population leads to higher travel needs. One of the difficulties in designing an urban transportation system that meets the needs of the population is the size of the population [18,28]. In addition, the lack of existing public transport services has resulted in inefficient urban transport. Studies on transport equity have outlined the importance of the spatial gap in public transport that at least a basis for public transport services should be provided for this low density of the public transport system [29,30].
This study aims to evaluate the supply and demand gaps in public transport with a case study of the BMR and to propose indicators in assessing the performance of public transport examining transport accessibility of communities using spatial data analysis techniques. This research study also aims to measure the distribution of public transportation in BMR to describe the existing public transport services in BMR. Thus, this study’s primary purpose is to demonstrate the assessment method used to evaluate public transport supply–demand gaps, which is one of the proposed indicators for including in the transport performance index. As the existing transport performance index used for BMR has not included the supply–demand gaps for this reason, we decided to implement the method for indicating supply–demand indicators and also to show the details and results of the demonstration. This study’s main contribution is an innovative approach in assessing public transportation supply by identifying spatial service level of public transport using daily public transport service and capacity of each transport mode estimating by real service and operation. Moreover, the supply and demand gaps analysis in this study is a new approach that uses daily public transport demand estimating by the travel demand model to compare with daily public transport supply to indicate spatial gaps between public transport supply and demand. The approach is implemented based on the real situation that reflects the performance of the public transportation system in BMR.
The contributions of this paper are as follows:
  • Proposed new public transport performance indicators from the reviewed existing index and analyzed public transport service results.
  • Provided an accurate transport planning analysis of the public transport supply–demand and its gaps using the geospatial gridded model (100 m) together with public transport equity measurement.
  • The proposed method is well established with possible replication and scale to other cities.
  • Demonstrated a real case study of BMR, Thailand with the analysis results for the public transport system.
  • The paper’s contents are structured as follows. Section 2 presents a summary of the research context related to public transport measurement and supply–demand gaps analysis. Section 3 provides the details of the methodology used in this study. Section 4 provides the dataset applied in the demonstration. Section 5 then summarizes the results with existing public transport service, supply–demand gaps, and equity. Section 6 discusses the results of the key findings regarding studies on public transport. Section 7 then concludes the research study with recommended indicators for future development and assessment.

2. Related Research

Defining the scope and criteria for transport performance measurement would provide more direction for transport service planning and development [31]. In 1997, the Canadian government in the International Conference (OECD) proposed the fundamental principle for sustainable transport titled “Towards Sustainable Transportation” [32]. Based on OECD, sustainable transport can be determined by access; equity; health and safety; individual responsibility; integrated planning; pollution prevention; land and resource use; education and public participation; fuller cost accounting. Litman [33] addressed that sustainable transport indicator sets should reflect a goal and objectives which would be useful for establishing a sustainable transport plan. A large number of past research studies have identified the parameters for transport performance evaluation around the world which is mostly defined with existing transport mode, the share of public transport, service availability and reliability, service capacity, and equity [34,35,36,37,38]. Moreover, Litman has noted that access and ability to reach services is the ultimate goal of the public transport system [39].
Earlier studies by Moseley examined the quality of public transport services associated with transport accessibility and availability [40]. In the United Kingdom, the Public Transport Accessibility Level (PTAL) method has been used to evaluate the level of spatial distribution accessibility of public transport [41]; however, the PTAL approach has a very highly detailed accessibility measurement which includes walking time accessibility, service frequency, and level of service such as the average waiting time at any transport stops. With this detailed analysis, the PTAL approach concerns about the quantity of service and over-attention on ‘place-based’ gaps on the services level therefore neglects the capacity of public transport at each station [42]. Currie [29,43] has developed the approach used to measure the accessibility and availability of transport service by being performed as the transport supply index, which reflecting on the level of public transport service expressed by a number of vehicle arrival per week at each transport stops. However, the transport supply also refers to the capacity of specific transportation infrastructures and modes over a time period [44]. On the other hand, conflict often arises when the transport resources are at a limited passenger.
Within the interest of the transport efficiency concept, spatial gaps based on transport supply and demand have been studied in past research. Jiao and Dillivan [45] have quantified the gaps between transit supply and population demand using Geographic Information Systems (GIS) by overlaying supply and demand values for each census block group to understand the transit service discrepancy. The gap analysis was performed by the subtracted values between the transit-dependent for an area that referred to the needs of a particular population (demand) and the number of transits in a city (supply). In earlier researches, the public transport gaps were assessed by the different degree between public transport supply and demand [46,47]. Indicating the supply–demand gaps could provide a sufficient understanding of the preliminary public transport service plan, whether to provide or to reduce the service capacity for an area. Moreover, transport equity reflecting on the different levels of ability to receive the transport service, can be signified instead of gaps. Equity issue has been a major concern in transport assessment, as the fairness could impact to communities [48]. Many studies have applied the Lorenz curve and Gini coefficient for measuring the public transport equity in public transport supply [4,5,30]. For example, the Gini index has been used to visualize the distribution of accessibility by distinctions of personal attributes, such as location and income [49]. Including the earlier studies by S. Lee et al. [50], the Gini coefficient has been used to assess income inequity over travel time reliability in Korean society.
Amongst many past research studies on public transport services, there are limited studies on capacity, supply, and equitable accessibility in public transport accessibility in BMR. For instance, Prasertsubpakij and Nitivattananon [51] have demonstrated the equity of accessibility of metro systems in Bangkok. Apart from this, there is no existing research on public transport gaps and equity. Therefore, the originality of methodology in this research is applying multiple formulas from public transport supply and demand domains in a specific sequence combining with the Lorenz curve and Gini coefficient to evaluate the gap of public transport service in BMR.

3. Methodology

3.1. Overall Methodology

At first, the study reviewed the transport performance index (TPI) used to measure the public transport performance in the major city, including BMR. The new TPI indicators were introduced for better measurement of the public transport service focusing on public transport capacity and supply. The overall methodology presents the analysis process of proposed indicators for public transport performance measurement. In the first phase, the public transport supply and demand were evaluated using geospatial analysis. The spatial gridded system was applied to the public transport service distribution analysis. To preserve the spatial information and pattern in the grid, the Modifiable Areal Unit Problem (MAUP) was considered for the analysis of transportation supply and demand. MAUP was well-known in geography and geospatial analysis. It was applied in the study to adjust the different boundaries of the dataset when the boundary of zones used in spatial analysis changes [52]. A small grid size of 100 × 100 m was chosen as a grid framework in this study, covering all the BMR areas for retaining the details in the analysis. As the maximum accessible walking distance to public transport stops is 400 m, a 100 m grid could arrange data into a specific unit better than using a larger grid size.
The transportation data, socioeconomic data, and travel demand survey data were applied to the supply model and demand model. For the transport supply, public transport availability, accessibility, and capacity were used to illustrate the distribution of the public transport service. For the transport demand, trip production, and attraction obtained from the travel demand survey were applied to the travel demand model for estimating travel demand and display its distribution. Furthermore, public transport supply–demand gaps were indicated to identify the area for improving the public transport service. Eventually, Lorenz curves and Gini coefficients were calculated to reveal the level of equity of public transport service over the population. The analysis of public transport supply distribution through accessibility, availability, and capacity, including public transport demand distribution estimated by travel demand model, was integrated to identify public transport supply–demand gaps and its equity, which is the initial process in identifying new TPI indicators presented with the green dashed box in Figure 1. Meanwhile, the process analysis results, and intermediate outputs were presented as the results, which is the demonstration results of proposed TPI indicators presented with the pink dashed box in Figure 1. The further detailed methods can be broken down into three components:
  • Method for proposing new TPI indicators
  • Method for supply–demand gaps analysis
  • Lorenz curve and Gini coefficient

3.2. Method for Proposing New TPI Indicators

For Transport Performance Index (TPI) indicators, the information on the public transport performance was started by gathering the information revealed by the OTP journal consisting of transport policy and traffic, scholarly articles, knowledge articles, and reports on transport and traffic which are related to public transport performance. The current index used to measure the performance of public transportation in BMR was also collected from the published articles and reports studied by OTP and related organizations. Keywords filter was used to find the associated articles and reports such as TPI, transport performance measurement, public transport performance index, etc., with the relevance ordered searching results. The published year of documents was determined between 2015 to 2020 for straining the filtered results for exploring the recent indicators and index used for indicating transport performance.
The proposed indicators were innovatively developed based on past studies by Currie [29,43], which introduce the spatial public transport supply calculating by the accessible area with the passenger service capacity per number of vehicles per week. Nonetheless, the proposed technique to indicate the public transport supply for this study calculates the spatial public transport supply by an accessible area with the total vehicle capacity per day [53]. This introduced technique can perform more detailed calculations of all public transport modes, which is yet to be applied in assessing public transport supply in BMR. However, these indicators can be applied in assessing public transport performance in other cities where the public transport system condition is similar to the BMR. For example, there is an overlay distribution of public transport by mode; this approach can calculate the total public transport capacity and supply service, represented as the spatial distribution of public transport and its accessibility.
The first newly proposed indicator for public transport performance measurement in this study is the public transport supply. The supply of public transport was measured by the quantity distribution of community transport vehicles supplied by the area. In this study, Supply Index (SI) or supply value is the number of public transport service supply in one day, calculating from total operating public transportation and each transport mode’s capacity. The SI value unit is the total number of the capacity of public transportation service operating in 1 day. However, the population density is a matter factor for indicating the level of supply service. Thus, the normalized supply index by population (NSIP) is introduced to compare the supply of public transport of the different population density areas. NSIP is calculated by the summarize of the supply value in each traffic analysis zone (TAZ) divided by population number in each TAZ. This is because the city area has a larger population than the suburb area, SI values may lose meaning when used to compare public transport supply between two or more cities [54]. Thus, this is intended to give the values of the SI when compared to public transport supply between provinces or TAZs. The NSIP by the population can be calculated as the following equation:
NSIP i = SI TAZ i P i
where SI TAZ i is the supply index of TAZ i representing the number of supply trip per day; P i is the population number of TAZ i .
According to the SI approach, a progress model was developed by Currie [29,43,55]. The approach was first published by Currie [29], which shows that the supply capacity was calculated by the number of bus arrivals at each stop in a week. However, the supply capacity in this study was calculated from the number of bus arrivals at each stop in 1 day with the capacity of each type of buses. SI aimed to measure the public transport supply of TAZs using the number of public transport vehicle arrivals per week. However, this study conducted an extended supply analysis by adding the detail of the service level factor. The number of transport vehicles supplied, or service level, was improved by applying the number of transports supplied trip per day or capacity per day. The transport supplied trip was computed with a better-detailed measurement which is calculated as the number of the supplied trips by area. Therefore, the SI in this study focused on a measurement of public transport service capacity in each TAZ that can be calculated as represented in Equation (2).
SI TAZ = i = 1 n Area B i Area TAZ i × SC B i
where Area B i is the area of the accessible distance (buffered area) within TAZ i (square meter); Area TAZ i is the area of TAZ i (square meter); SC B i represents the service capacity value, the number of public transport service capacity per day in TAZ i .
Accessible distances for each transportation stop or station were calculated by spatial buffering around each stop and station to represent the public transport accessibility area. The distances to access the public transport stop that 75% to 80% of people would walk is between 400 m to 800 m [56]. However, an acceptable maximum walking distance to transports stops was found between 1000 and 1600 m [57]. In this study, the radius of the buffering area for accessible distance was decided based on past studies and the type of public transport using the following distances:
  • 400 m for heavy rail stations, boat piers, and van stations
  • 800 m for bus stops and bus rapid transit (BRT) stations
  • 1000 m for taxis
  • 1500 m for train stations
Additionally, the public transport supply was derived from public transport service capacity (SC) as represented in Equation (3). In this study, public transport service capacity was expressed by accessibility to six public transport modes consisting of heavy rail, bus, van, taxi, boat, and train. Thoroughly, the capacity of each public transport mode was measured by the average of maximum capacity per vehicle multiples by the number of operating trips in 1 day. The number of operating trips at each transport station was calculated by the average headway (minutes) separated by the duration of morning and evening peak hour and midday minutes. Peak-hours and off-peak hour operating time for public transport rely on modes of transport. Usually, the peak-hour duration is between 6:00 AM to 9:00 AM and 4:00 PM to 8:00 PM. The total capacity of public transport in one day can be defined as shown in Equation (4).
SC B i =   i = 1 n C H e a v y   R a i l + C B u s + C V a n + C T a x i + C B o a t + C T r a i n
C i   =   C V × N C × ( P e a k   h o u r   o p e r a t i n g   t i m e A v g .   p e a k   h o u r   h e a d w a y   ) + ( O f f   P e a k   h o u r   o p e r a t i n g   t i m e   A v g .   o f f   p e a k   h o u r   h e a d w a y   )
where C i represents the total capacity of public transport mode i consisting of heavy rail, bus, van, taxi, boat, and train; C V is the average of maximum capacity per vehicle; N C is the number of cars in the train which is used for only heavy rail mode and trains mode, for other modes is 1.

3.3. Method for Supply–Demand Gaps Analysis

The public transport supply–demand gaps were analyzed by geospatial analysis using a spatial overlapping function to indicate the service gaps area. The SI and NSIP were explained in the previous section. Hence, the demand index (DI) calculation and normalized travel demand by the population were used to clarify the quantity of public transport demand. Additionally, public transport supply–demand gaps analysis is also introduced and further explained in the section below.
The public transport demand aimed to evaluate the quantity of travel demand for public transport by area and population density. Back in the 1950s, the four-step travel demand modelling was first developed for estimating travel demand volume by mode [58,59]. The travel demand modelling was applied to estimate public transport demand. Accordingly, the travel demand survey and the socioeconomic data are used to evaluate the demand for public transport in this study.
The normalized demand index by population (NDIP) can be calculated by the summary of DI of public transport in each TAZ divided by the population in each zone. NDIP is also helpful and proper to compare with the NSIP. This is to adjust the measurement unit to examine the gaps between public transport supply and demand. Thus, the DI was analyzed and normalized by population, respectively. The NDIP equation is defined as follows:
NDIP i = DI TAZ i P i
where DI TAZ i is the travel demand index of TAZ i ; P i is the population number of TAZ i .
The public transport demand index applied the trip generation model from the four-step model introduced by Manheim (1979) and expanded by Florian et al. (1988). The trip production is a part of the trip generation model used to calculate the total trip productions generated in each TAZ. Thus, the DI was calculated by travel trip rate multiple with the population in TAZ. This DI can be expressed as follows:
DI TAZ = i = 1 n P i × Trip   rate i
where DI TAZ represents the travel demand index of TAZ, which reflects the number of travel demand trip per day; and P i is the population number of TAZ i ; Trip   rate i displays the proportions of household trips by purpose.
Initially, the trip rate was performed from the travel demand survey collected in 2017. The survey dataset was classified into four trip purposes: home-based work (HBW), home-based education (HBE), home-based other (HBO), and non-home-based (NHB). Additionally, the survey dataset of the household vehicle ownership was segmented into four household types: no vehicle (0VEH), motorcycle (MC), private car (MC), and multi-vehicles (MULTI). In this process, all trip purpose was considered in the demand model with the data from OVEH household only, focusing the target of public transport passenger. The trip rate calculation can be computed as follows:
Trip   rate = i = 1 n n i × TP i
where n i refers to the number of trips per person per day for the trip purpose i; TP i is the trip purpose proportion of household type i.
The public transport service gap was determined by the level of provided supply and the level of travel needs. The public transport supply–demand gap in this study was indicated by the proportion of supply and demand indices. This method has the advantage of identifying an area where supply–demand is inequity. For example, the area of low public transport supply and high public transport demand can be identified by this analysis. The public transport supply–demand gap can be calculated as follows:
Gap TAZ = i = 1 n NSIP TAZ i NDIP TAZ i
where NSIP TAZ i is the normalized supply index by the population of TAZ i ; NDIP TAZ i the normalized travel demand index by the population of TAZ i .

3.4. Lorenz Curves and Gini Coefficient

Lorenz curve was developed by Max O. Lorenz [60] for representing the inequity of the income distribution across the population in the economic field. Currently, the Lorenz curve has been applied in various subjects, from the studies of transport to environment and resources even within data distribution study [61,62,63,64,65]. An example of a Lorenz curve is shown in Figure 2, the black linear line represents a perfectly equitable public transport supply distribution; the red curve represents an inequitable public transport supply distribution. In a perfect equity scenario, the first 10% of the population has received 10% of the public transport supply, 20% of the population has received 20% of the public transport supply, and so on. An example of an inequity scenario, 80% of the population has access to 40% of the public transport supply while 20% of the population has access to 60% of the public transport supply presented as the red curve in Figure 2.
The Gini coefficient (G) was proposed by Corrado Gini in 1912 to measure income inequity of wealth inequity. The Gini coefficient calculates from the ratio between the line of equity and the Lorenz curve (area “A” in Figure 2) divided by the total area under the equity line (area A + B in Figure 2) [66]. The Lorenz curve is a graphical representation of equity distribution, whereas the Gini coefficient is a single statistical value intended to represent the overall inequity degree. G values are set between 0 and 1; 0 means perfect equity, and 1 means perfect inequity. The Lorenz curve and Gini coefficient were used in this study to analyze the equity of public transport supply in different regions across the BMR population in different socioeconomic groups. The mathematical equation of the Gini coefficient can be approximately calculated using the following formula:
G = 1 k = 1 n P k P k 1 S k S k 1
where P k is the cumulative proportion of population, for k = 0, 1, 2, 3, …, n, P 0 = 0 and P n = 1. S k   is the cumulative proportion of public transport supply, for S 0 = 0 and S n = 1.

4. Study Area and Dataset

The data used in this research are mostly supported by the government departments under the Ministry of Transport (MOT), Thailand, including the Department of Provincial Administration (DOPA), Thailand, and the analysis boundary established and provided by the government department. The data collected and used in the analysis in this study are reliable and recently updated. Using real transportation data, population density data, including recent travel demand survey data, is reasonable to reflect the reality of the public transportation system in BMR.

4.1. Study Area

The case study was performed in the Bangkok Metropolitans Region (BMR), located in central Thailand in South-East Asia. The study area covers the capital city named Bangkok and five vicinities, including Nakorn Pathom, Nonthaburi, Pathum Thani, Samut Prakan, and Samut Sakorn (Figure 3). The coverage area of the BMR is approximately 7700 square kilometers (3000 square miles) and has a population of more than 11 million—the highest population in Thailand accounting for 16.21% of the total population (The Bureau of Registration Administration, 2019). The BMR is the center of administration, transportation, education, medical, as well as commerce and development. The BMR supports many people, whether Thai citizens or foreigners in the city and its metropolitan area, who travel to and from work, access healthcare, partake in entertainment, recreation, etc. The massive daily travel is demanded, the government has constructed the public transport system to serve people composed of buses, sky trains, subways, vans, and taxis. On the other hand, road infrastructures are also still expanding and being developed to serve an increasing number of cars and reduce traffic congestion in the city. Importantly, the characteristic of most commuters’ travels from the suburbs into the city to work and various errands meanwhile public transport is mainly available in the downtown and business districts. Thus, BMR is an interesting area for the study of public transport supply and travel demand.

4.2. Dataset for Supply Index

4.2.1. GIS-Based Transportation Data

For public transport supply index calculation, the public transportation dataset is provided by government departments in accordance with transportation mode. Missing data and non-updated data were acquired and updated from Open Street Map (OSM) ( All public transport modes used in this study were prepared in the form of GIS data representing their locations and service routes. The number of serviced vehicles and transport stations in 2020 is described as the details of each public transport mode in Table 1. Moreover, the taxi data used in this study were obtained from the study results by Saurav [67]. The clustered data of taxi stay points were derived from probe GPS taxi data using trip information such as origin and destination, taxi demand information, free taxi movement, and network travel time.

4.2.2. Transportation Service Information

For public transport service times, schedule and vehicle capacity used to estimate the capacity of public transport are collected from the service schedule reports of each public transport operators. For heavy rail, the service schedules are published by Bangkok Mass Transit System Public Company Limited (BTS), Mass Rapid Transit Authority of Thailand (MRTA), SRT Electrified Train Company Limited under the SRT operating ARL. For the bus, the data were collected from the BMTA and BTS, the owner of the Bangkok bus rapid transit (BRT) system. For the boat, the service schedule was obtained from the Marine Department, Ministry of Transport (MOT). Lastly, train schedule information was collected from the State Railway of Thailand.

4.3. Dataset for Demand Index

The data required for DI calculation are shown in Table 2. The main dataset used to compute the DI was the number of travel trips per person per day which could be extracted from the demand survey. Travel demand survey was provided by OTP, Ministry of Transport. The survey was collected in 2017 from 18,833 households or about 0.3% of households in the BMR area. The dataset has been prepared and provided in 2018, illustrated by the travel demand survey zone (TDS). The number of populations was collected from The Bureau of Registration Administration (BORA) website ( For average income in each region, the data were provided by OTP, forecasted by the Extended Bangkok Urban Model (eBUM) using the TAZs. TAZ areas were defined by the number of residents based on the district and sub-district administration area. Therefore, some subdistrict areas were divided into several TAZ areas upon the density of residents. Moreover, the number of inhabitants in these areas has been surveyed and updated by population and housing census data, Gross Provincial Product (GPP) data, and travel behavior survey data.
However, the analysis zone in this study was used TAZs because it separated zones based on travel demand and population density. Further, eBUM could estimate the population number as well, whereas the population number given by BORA is more precise and complete. Thus, the population data and travel demand survey data were adjusted to the TAZ zone to analyze the DI.

5. Results

5.1. Proposed New TPI Indicators

The TPI is the index not only used to evaluate the transport performance for assessing the competitiveness of the country but also used to plan and develop suitable and effective traffic. Initially, the index has been sorted by mode of transportation classified as national level and city level, which is BMR. The total indicators used to assess the country’s transport performance are 47 indexes which are categorized into four main dimensions and eight minor dimensions. In total, nine indexes out of the total TPI index are involved in public transport in BMR. Currently, the TPI indicators associated with public transport in BMR consist of four major dimensions: supply/availability/capacity, quality of service, utilization, and safety. These indexes can be used to evaluate the performance of public transport infrastructure and performance. However, it is found that there are no indicators determined in the capacity minor dimension, as shown in Table 3. More importantly, the supply dimension is commonly calculated from the capacity of transport services or service level [44]. In this study, the missing indicator of the capacity dimension is proposed. The detailed results of the proposed indicators are given in Section 5.2, Section 5.3 and Section 5.4.
The study of the TPI index is shown that public transport in BMR contributes to the overall country’s transport performance. Moreover, this index can be used to evaluate the overall performance of public transport in BMR as well. Missing indicators for public transport services are mainly about capacity, which impacts the overall supply of public transport. The capacity dimension can be evaluated by service level estimation. The proposed indicators for TPI are involved in transport availability minor dimension and a capacity minor dimension, as presented in Table 3. For the availability dimension, the number of public transport mode availability and the proportion of area covered by public transport walking catchment are introduced. For the capacity dimension, three indicators are introduced consisting of the number of service capacity, the proportion of the number of service capacity, and population. The public transport supply index was introduced to include in supply minor dimensions. Additionally, the results also proposed an equity index, a new dimension for public transport performance measurement in BMR. For sustainable transport performance, service equity of public transport service can be evaluated by using the service equity dimension comprising the average of the public transport supply–demand gap indicator and Gini coefficients.

5.2. Supply Index

5.2.1. Public Transport Availability and Capacity

Table 4 presents the summary of public transport service availability and capacity by mode in each province. In initial data analysis, Bangkok has the highest proportion number of public transport stops accounted for about 72.25% of the total public transport stops in BMR followed by Samut Prakan and Nonthaburi provinces, accounted for 9.07% and 8.56%. It was obviously seen that only about 10% of public transport stops were provided in Pathum Thani, Nakhon Pathom, and Samut Sakhon provinces. The overall public transport service area in BMR was about 975 accounted for 12.62% of the total study area. Bangkok has the largest area covered by public transport service accounted for 36.89%, followed by Nonthaburi and Samut Sakorn provinces, which accounted for 13.28% and 11.87%, and the remaining public transport covered less than 10% in each region. Moreover, the total number of public transport service capacity in BMR was about 2.2 million trips per day. It was also pointed out that the number of public transports service capacity was served in Bangkok about 75% of the total public transport capacity; however, it was only 25% of the total service capacity served in surrounding provinces.
The spatial distribution of public transport service capacity in BMR is shown in Figure 4. The results in each 100 × 100 m grid pixel present the volume of the public transport service as the number of trips per day shading range from zero (yellow color) to maximum service capacity (blue color). The minimum service capacity is zero, and the maximum service capacity is 433 trips. The maximum density of the public transport capacity mostly distributes in central Bangkok and some parts of Nonthaburi, Pathum Thani, and Samut Prakan which are connected to Bangkok.

5.2.2. Supply Index and Normalized Supply Index

Figure 5 presents the spatial distribution of SI and NSIP of public transport in BMR shading ranges from zero supply (grey color) to the highest supply (red color). NSIP of public transport in BMR result indicates over demand area as represented in red color areas in Figure 5b. Table 5 shows the findings of the public transport supply detection using the NSIP calculation described in the methodology section. The NSIP was grouped into seven categories and compared between six provinces. The results indicate the following:
  • In total, 1.6 million residents in Bangkok or about 30% of the population receive zero supply of public transport. Approximately 50% of the population receive a low public transport supply (<20, 20–40), and about 20% of the population receive a high supply of public transport (>40).
  • In the Nonthaburi and Samut Prakan provinces, 53% and 65% of the population receive zero supply of public transport, respectively. It was indicated that 40% of residents in Nonthaburi and about 29% of residents in Samut Prakan receive low supply (<20, 20–40). The high public transport supply (>40) serves the population in Nonthaburi and Samut Prakan for 7% and 6%, respectively.
  • In the Pathum Thani, Nakhon Pathom, and Samut Sakorn provinces, approximately 84%, 83%, and 73% of the population receive zero supply of public transport, respectively. It was shown that 16% of residents in Nonthaburi and Nakhon Pathom, and 28% of the population in Samut Sakorn receive low supply (<20, 20–40). Interestingly, there has no high public transport supply (>40) that serves residents in Pathum Thani, Nakhon Pathom, and Samut Sakorn provinces.

5.3. Demand Index

Figure 6 presents the spatial distribution of DI and NDIP of public transport in BMR shading ranges from the lowest demand (yellow color) to the highest demand (brown color). It clearly illustrates that normalized demand value by population lead to a better calculation for public transport demand. The high demand for public transport is mainly distributed in the Bangkok center and urban zones in vicinities, as shown in Figure 6b. As the population number is a matter, especially Bangkok having a higher population than vicinities, many areas change after normalization by population such as dark to yellow, or yellow to dark.
Table 6 shows the results of the public transport demand estimated by trip rate and calculated by DI and NDIP methodology described in Section 3.3. The travel demand or NDIP was also grouped into seven categories and compared between six provinces for easy comparison with the supply value, which represents the same measurement unit and categories. The results indicate the following:
  • In total, 2.5 million residents in Bangkok or about 45% of the residents desire less public transport service (<20). However, 37% of Bangkok residents require moderate public transport service (20–60), while 18% of the population require high public transport service (>60).
  • A total of 70% of Nonthaburi residents and 78% of Pathum Thani residents demand less public transport service (<20). Further, 20% of the population in Nonthaburi and 16% of the population in Pathum Thani desire moderate public transport supply (20–60). For the high public transport demand (>60) were sought from 10% and 6% in Nonthaburi and Pathum Thani.
  • The populations of Nakhon Pathom, Samut Prakan, and Samut Sakhon require less public transport service (<20), and accounted for 62%, 54%, and 67% share, respectively. In total, 34% of Nakhon Pathom residents, 45% of Samut Prakan residents, and 26% of Samut Sakhon residents seek moderate public transport supply (20–60). A lot less of the population in Nakhon Pathom, Samut Prakan, and Samut Sakhon demand high public transport supply (>60), and account for 4%, 1%, and 7% share of the population, respectively.

5.4. Supply–Demand Gaps

The number of TAZs in each category of public transport supply–demand gaps was calculated using the methodology described in Section 3.3. Figure 7 illustrates the graphical distribution of public transport supply–demand gaps with different ratio level of the threshold at 10, 20, and 30 of supply–demand gaps. Although, a 10% threshold of the difference between the supply and demand indices was used to illustrate the results into three groups, as indicated in Table 7. The categories of supply–demand gaps consist of low supply and high demand (LS-HD), high supply and high demand (HS-HD) or low supply and low demand (LS-LD), and high supply and low demand (HS-LD). In Figure 7a, the spatial distribution displays that most of the results indicated as LS-HD area, which means that most of the BMR area is lacking public transport supply or service. Whereas HS-LS zones have appeared in Bangkok the most comparing with vicinities, which means that some area in Bangkok has an oversupply of public transportation. Interestingly, there are also very small areas identified as HS-HD or LS-LD area, which means that there are small areas where the supply and demand of public transport are quite equitable.
Table 7 illustrates the public transport supply–demand gaps that are categorized into three groups by household income level. This also demonstrates the household income distribution in each province. For example, it was found that there has no TAZs indicated as a high-income area in Pathum Thani and Nakhon Pathom provinces. Furthermore, all TAZs in Samut Sakhon province was indicated as a low-income household area. This is because the household income was averaged from the total household number, which may have a gap in household income distribution within a TAZ. The results reveal the following:
  • High gap areas with high public transport supply are mostly the business area along the metro lines which has many office buildings and large popular shopping centers. These areas have commuting demand for work purposes as well as other activities such as school, hospital, shopping, as well as government services. On the other hand, high gap areas with low public transport supply are mostly in residential areas, including non-business areas such as agricultural areas, livestock areas, and countryside.
  • Overall supply–demand gaps show that most of the population in BMR are placed under the LS-HD area. Moreover, most of the low-income households (<30,000) in vicinities were found in these LS-HD categories but in Bangkok and Nonthaburi were found in the middle-income household (30,000–50,000). The second proportion of the population in BMR was placed under LS-LD or HS-HD area. Lastly, the very small size of the population was placed under the HS-LD area.
  • The results display that it has more than 50% of the population proportion, which has big gaps between supply–demand for public transport. The largest gaps between the supply–demand were found in Nakhon Pathom province followed by Samut Prakan, Pathum Thani, and Samut Sakhon, which accounts for the population proportion about 88%, 72%, 71%, and 69% respectively. Additionally, it was found that the LS-HD area occurs mostly in the surrounding provinces with low-income households’ distribution except for Bangkok and Nonthaburi, where the LS-HD area was found in middle-income households more than low-income household.
  • The results also show that HS-HD or LS-LD area was indicated about 20% to 40% of the population in BMR. Nonthaburi province has the largest number of populations placed under the HS-HD or LS-LD area, and accounted for about 40% of the population followed by Samut Prakan, Pathum Thani, and Bangkok, which accounted for about 27%, 24%, and 20% of the population proportion, respectively. For Bangkok and Nonthaburi, HS-HD or LS-LD area was mostly distributed in middle-income households’ area, whereas the remaining provinces was distributed in low-income households’ area.
  • Only three provinces appeared in the HS-LD area for public transport more than 10% of the population proportion; which are Bangkok, Nonthaburi, and Samut Prakan. Bangkok has the most population distribution placed under the HS-LD area for public transportation, and accounted for about 14% of the total population. Moreover, it occurred in middle-income and high-income household’s distribution areas. For Pathum Thani, Nakhon Pathom, and Samut Sakhon provinces, less than 5% of population distribution area were indicated as an HS-LD area.

5.5. Lorenz Curves and Gini Coefficients

Using Lorenz curves could explain the analysis results of the public transport supply distribution across the population in the overall equivalence level as well as in each province and each household income category. The Lorenz curves visually display the overall degree of horizontal equity of public transport services, which is low, indicating that approximately 60% of BMR residents acquire less than 5% of total public transport service. It means that there are about 40% of BMR residents having very high access to public transport service accounted for about 95% of total public transport supply substituted as the red dashed curve in Figure 8a. Moreover, Lorenz curves show that the population in Bangkok has a smaller gap in public transport service than vicinities. In practical terms, this means that approximately 60% of Bangkok’s population shared about 10% of public transport, conversely about 40% of Bangkok’s population shared about 90% of public transport service. In the same direction, the Lorenz curves of the five vicinities display quite similar trends, which about 80% of the population in surrounding provinces have accessed about 5% to 15% of public transport supply, while 20% of the population have accessed about 85% to 95% of public transport service.
Remarkably, Lorenz curves also point out that the high-income population has smaller gaps than the middle-income population and the low-income-population. In the same way, the Lorenz curves show that the middle-income population has smaller gaps than the low-income population presented in Figure 8b. It describes that around 80% of the high-income population shares around 40% of public transport; however, approximately 20% of the high-income population shares about 60% of public transport service. Additionally, about 70% of the middle-income population shares about 15% of public transport, while 30% of the middle-income population shares 85% of public transport. Lastly, 80% of the low-income population shares only 10% of public transport, while conversely 20% of the low-income population shares 90% of public transport service.
Due to the concentration of public transport services, the overall BMR acquired a high score (G = 0.75). Significantly, Bangkok appears as the lowest Gini index score (G = 0.64), compared with vicinities that have higher Gini index scores, G is between 0.78 to 0.88, as presented in Table 8. Thus, among the ranking of Gini index scores of all provinces, Pathum Thani and Nakhon Pathom rank first and second, respectively, (G = 0.88, 0.86), which are the closest to the absolute inequity value of 1. It indicates that public transport in these two provinces are mainly clustered in a few communities’ areas while the remaining communities are not very well connected. Ranking the Gini index scores classified by household income level, the results capture that low-income households rank the fist (G = 0.83) followed by middle-income households and the high-income household, which is rank second and third respectively (G = 0.70, 0.63). According to these Gini value scores, public transport service in high-income households has a better distribution than low-income and middle-income households. On the other hand, public transport supplied in the low-income household area is poorly distributed and connected.

6. Discussion

The approach used in this study can indicate public transport supply–demand gaps and compare across geographic regions. Accurate transport planning using geospatial analysis and GIS tool respect to the gridded system could keep the analysis details more precise and accurate. The number of arrival vehicles per day and capacity per vehicle has been used to estimate the number of service levels in a particular grid. However, the approaches used in this study were adopted from the study of transport supply based on the social need [19,33]. This study could specify the details of the public transport supply using the number of operating trip supplies per day. In the same way, the DI in the study is applied by the trip generation model, a part of traditional four-step travel demand modelling that could apply to other cities for estimating travel demand. Hence, the SI and DI measurement units in this study have been regulated to be the same scale represented as the number of trips per day. The advantage of this approach is the supply–demand index could be compared certainly even using the normalized index by the population to identify gaps area, which is smaller than the administrative area. For example, the results obtained 16 TAZs out of 34 total TAZs identified as the LS-HD area in the Huai Khwang sub-district located in Bangkok, where it is a very dense place of commerce, services, and housing. By these details results could specify the more accurate area for improving public transport and could answer the research question that geospatial analysis can identify gaps of public transport supply–demand omitted by administrative based data.
Following SI results, 5.61 million population received zero supply for public transportation, which accounted for 50.73% of the total population in BMR. It presents similar results obtained from the Lorenz curve that about 50% of the BMR population sharing zero supply for public transport. Thus, applying the Lorenz curve and Gini coefficient to transport disadvantage studies could explain the level of supply–demand gaps over the population [4]. Typically, population density is a matter factor for assessing public transport supply and demand. The study results reveal that a high population number associated with high demand. Grubler has also found that demand growth due to the joint effects of population growth and household [68]. To explain the degree of equity level of public transport over the population, the study showed that the Lorenz curve and Gini coefficient could be used to display the gaps over the population as well as household income distribution.
As stated by OTP, transport infrastructure evaluation using TPI helps to determine transport strategy, policy, and infrastructure development planning, as well as contribute to a global competitive assessment [17]. Verifying the indexes enables the assessment of the existing TPI and to plan for future improvement of transport performance measurement. Hence, the index analysis results suggest including the supply index and equity index into TPI indicators for better measurement on the basis of equitable distribution of public transport and equitable access to public transport. As a result of recommended indicators, the SI and the equity of public transport accessibility are also essential for measuring transport performance, including performance improvement to compete in global standards. Likewise, this has been introduced in earlier studies by Mayer—level of service or volume-to-capacity and equitable distribution were a part of the transport performance-based planning process related to the ultimate effect of the transportation system on a community in the United States [34]. Moreover, measuring public transport performances to assess the sustainability of transport activities have included even subjective indicators such as quality of service, passenger perception, and satisfaction [21,26,27].
Underlying transport equity results, public transport could not yet address the economic gaps against the growing economy in BMR. Economic growth along with public transport services causes some people are far away from the public transit system especially, sky trains and subways networks. Public transport infrastructure and planning should be planned and served for everyone, and no one should be left behind. Reducing gaps by providing public transport accessibility for the low-income population could increase their opportunities for commuting and activities. This study suggested that the government should assist the problem that remains in supply–demand gaps, including public transport service inequity. For example, setting the future goal for improving the accessibility level of public transport and reducing its gaps to balance public transport services equity. The supply–demand gap results also benefit the government to know where to improve public transport services including transport planners or policymakers can decide which indicators threshold or measures level criteria can be used to plan for the supply–demand gap reduction for future development.

7. Conclusions

This paper aimed to propose indicators to evaluate public transport performance as well as to measure the supply–demand gaps of BMR public transport objectively. Existing public transport performance index analysis has disclosed that the capacity and supply indicators are missing. Hence, the study has introduced and analyzed proposed indicators for evaluating public transport given for the supply, availability, and capacity including new dimensions for public transport supply–demand gaps and equity which associate with public transport performance measurement. The analysis results highlight that public transport supply in BMR is mostly concentrated in Bangkok city while the service in outer Bangkok and its vicinities is very sparse of public transport services. It only has the main service bus line and train line passing through vicinities’ central business districts (CBD). The supply–demand gaps result pointed out that more than half of the residents in BMR is in low supply areas but high demand for public transport, which is mainly distributed in low-income households. Lacking public transport supply in a large area of residents caused the rich to become car-dependent while a poor who does not have enough money to pay for a car must spend a long time to commute and may lose job opportunities due to a limit of travel because of the limited public transport system. In terms of sustainable transportation, these problems affect an unstable society and led to gaps expansion between rich and poor in the future.
As per the demonstrated real case study of the public transport system in BMR, exposing the accessibility to public transport service and its gaps reveal the inequitable accessibility level of public transport in BMR. Application of Lorenz curves and Gini coefficients showed that approximately 60% of the population in BMR shares less than 5% of public transport supply, and conversely, about 40% of the population shares about 95% of public transport supply. Significantly, the Gini index results identified that the high-income population has better access to public transport than the middle-income and low-income population, while the low-income population has the lowest accessibility to public transport. The spatial mismatch of public transport results provides an angle to extract the effectiveness of the existing public transport service and its distribution.
Therefore, the proposed method used in this study is concise and well established with possible replication which is possible to scale widely and transfer the context to other cities for assessing public transport performance through service capacity, supply, and demand gaps, including for the equity in public transport accessibility. In this context, the public transport supply–demand gaps analysis described is a systematic and comprehensive approach that is straightforward to apply and execute in order to display the capacity of spatial public transport services and to identify spatial public transport supply–demand gaps. Moreover, the data required in the method are relatively common such as population data, employment data, and transport data. Thus, this approach can be applied to other cities where the data are available. If the area lacks some required data, the method can be simplified by measuring a broad transport supply and demand, such as using an analysis zone’s aggregation. Therefore, this approach is suitable for a large metropolitan city, such as BMR and also other cities such as Melbourne [29] and Guangzhou [4]. The output of this paper could be applied or lead to promoting sustainability of transportation in terms of transport efficiency management and plan. Promoting the use of public transport or mode shift from private transport to public transport can reduce environmental pollutions; hence, making public transport more efficient and accessible will result in increased use of public transport and drive the private vehicle commuters to public transport commuters.
The limitation of the study is that the supply and demand for public transport is dynamic in different periods of the day. During rush hours in the morning and evening in working days, the traffic is more crowded than during the day or night. For example, the Bang Sue sub-district has a very high demand during peak-hours, but less demand during off-peak hours. Identifying the supply or passengers carried per vehicle for public transport at a different time of the day can be implemented by specifying the period of public transport operating hours. In contrast, estimating the demand for public transport at a different time of the day requires microdata such as individual travel timetables or probe vehicle data to estimate public transport demand [67,69]. However, some public transportation modes in BMR, such as train and bus, still use paper tickets while the transit system uses electronic cards. The consolidation of the payment system for public transport will provide more detailed data to explore more details of the demand, such as specific time analysis.
In the study’s results, two out of four TAZs are indicated as a HS-LD area because this research study analyzed the total supply and demand on the basis of the daily trip which reflects on the overview of public transport supply–demand. For such a case, the results in this study would be useful to manage the public transport supply on a macro scale. Therefore, transport planners can plan to distribute peak times to accommodate the travel, and it could help to manage the volume of public transport sufficiency [70]. Using emerging technologies such as people mobility data like Call Detail Records (CDR) based mobility data and vehicle GPS probes can be used to monitor travel demand by hours for a breakthrough of this limitation [71]. Although many parts of BMR have high travel demand with limited transportation that cause congestion problems, as also reported by the TomTom traffic index, indicating that Bangkok ranked seventh in the most congested cities in Asia and ranked 11th on the world’s most congested cities in 2019 [72]. On the other hand, Bangkok Post released that work from home during the COVID-19 spread in the first half of 2020 could reduce the commuting time for a few hours a day and have 1.4 days’ extra work in a month [73]. Learning from remote work during the COVID-19 spread can help demonstrate the effects of traffic reduction [74]. It can also be a transport management strategy that deals with the challenge of reducing the traffic volume in the future as well.

Author Contributions

A.P. and H.M. conceived and designed data management and methodology. A.P., H.M., and A.W. performed experiments and the analysis. A.P., H.M., A.W., and S.M.K. contributed to the interpretation of the results. A.P. and H.M. wrote the original draft and all the authors contributed to the review and editing of the manuscript. All authors have read and agreed to the published version of the manuscript.


This research was funded by Science and Technology Research Partnership for Sustainable Development (SATREPS), Japan Science and Technology Agency (JST)/ Japan International Cooperation Agency (JICA) “Smart Transport Strategy for Thailand 4.0” (Chair: Yoshitsugu Hayashi, Chubu University, Japan).


The authors would like to extend our appreciation to the University of Tokyo for providing the research publication fund. The authors would like to provide special thanks to Bhoj Raj Ghimire for the research discussed during the beginning of this study. We would like to acknowledge the data and resource support from Saurav Ranjit from the University of Tokyo, including transport data provided by the Office of Transport and Traffic Policy and Planning (OTP). Moreover, we would like to extend our gratitude to Masanobu Kii and Varameth Vichiensan for conscientious guidance and support. Sincere thanks to Supanut Juisoei, Threerapat Pukird, and Threerapong Wanwonstudent from the transportation department, AIT who provided insight and suggestions. Lastly, we would like to give very special thanks to Niphon Lapanaphan for contribution in running through English proofreading.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. Overall methodology with the methodology section number identification.
Figure 1. Overall methodology with the methodology section number identification.
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Figure 2. Example of the Lorenz curve.
Figure 2. Example of the Lorenz curve.
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Figure 3. Study area map with public transport stops distribution.
Figure 3. Study area map with public transport stops distribution.
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Figure 4. Spatial distribution of public transport service capacity and share of service capacity in BMR.
Figure 4. Spatial distribution of public transport service capacity and share of service capacity in BMR.
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Figure 5. (a) Spatial distribution of SI; (b) spatial distribution of normalized supply index by population (NSIP) of public transport.
Figure 5. (a) Spatial distribution of SI; (b) spatial distribution of normalized supply index by population (NSIP) of public transport.
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Figure 6. (a) Spatial distribution of demand index (DI); (b) spatial distribution of NDIP of public transport.
Figure 6. (a) Spatial distribution of demand index (DI); (b) spatial distribution of NDIP of public transport.
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Figure 7. Spatial distribution of public transport supply–demand gaps. (a) at 10% threshold; (b) at 20% threshold; (c) at 30% threshold.
Figure 7. Spatial distribution of public transport supply–demand gaps. (a) at 10% threshold; (b) at 20% threshold; (c) at 30% threshold.
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Figure 8. Lorenz curves of public transport supply and population. (a) Categorized by province; (b) categorized by household income.
Figure 8. Lorenz curves of public transport supply and population. (a) Categorized by province; (b) categorized by household income.
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Table 1. Number of public transport services in Bangkok Metropolitan Region (BMR) by mode.
Table 1. Number of public transport services in Bangkok Metropolitan Region (BMR) by mode.
DatasetData TypeData DescriptionNumber of Transport Routes/Stations/VehiclesData Source
Heavy railsPoints and linesConsists of sky trains and subways; BTS 1, MRTA 2, and ARL 3111 stations
5 lines
BusesPoints and linesComprise of non-AC buses, AC buses, and Bus Rapid Transit (BRT)4773 bus stops
2715 buses
197 routes
Bangkok Mass Transit Authority (BMTA)
Bangkok Metropolitan Administration (BMA)
VansPointsContain the main vans stations in Bangkok and vicinities24 stationsOpen Street Map (OSM)
TaxisPointsStay points of taxis obtaining from taxi probe data2678 taxisTaxi stay point analyzed by Ranjit [67]
BoatsPoints and linesConsists of boat lines and piers along Chao Phraya river and Saen Saep canal69 piers
6 lines (Chao Phraya river)
2 lines (Saen Saep canal)
Marine Department
TrainsPoints and linesIntercity trains on the Northern, North Eastern, and Southern Train Lines stop.69 stations
5 routes
State Railway of Thailand (SRT)
1 Bangkok Mass Transit System Public Co., Ltd. 2 Mass Rapid Transit Authority of Thailand. 3 Airport Rail Link.
Table 2. Data description for demand index calculation.
Table 2. Data description for demand index calculation.
DatasetSection NameSurvey InformationBoundaryNumber of ZonesData Source
Household travel demand surveyHousehold informationHouse type, number of a family member, number of own vehicles in the householdTDS1607 zonesOTP, MOT
Personal Travel Details (Age between 4 to 80)Vehicle type, trip information, trip number, trip purpose, origin, destination, travel mode
Population Density-Number of people in each areaAdmin477 zonesBORA
Income-Average household incomeTAZ846 zonesOTP, MOT
Table 3. Proposed a new transport performance index (TPI) for public transport in BMR.
Table 3. Proposed a new transport performance index (TPI) for public transport in BMR.
Major DimensionMinor DimensionCurrent IndicatorsProposed Indicators
AvailabilityThe density of the public transit network
The density of transit station
Number of public transport mode availability
The proportion of area covered by public transport walking catchment
Capacity -Number of service capacity
The proportion of the number of service capacity and population
Supply Public transport supply index
Equity *Service equity-Public transport supply–demand gap
Gini coefficients
* New index dimension suggestion.
Table 4. Public transport services availability and capacity in BMR by mode.
Table 4. Public transport services availability and capacity in BMR by mode.
NonthaburiPathum ThaniNakhon PathomSamut PrakanSamut Sakhon
Number of TAZs5468158419327846
Total area (sq. km)1571.02637.461512.392149.01999.97863.927733.77
Average area of TAZ
(sq. km)
Number of transport stops
Heavy rail (stations)881300100111
Buses (stops)37203191701433011214774
Vans (stops)115222224
Taxis (stay points)166531219243387782677
Boats (Piers)5811000069
Trains (stations)37121001767
Total number of transport stops55796613661987002187722
% number of transport stops72.25%8.56%4.74%2.56%9.07%2.82%100.00%
Total public transport service area (sq. km)579.5084.6349.3383.0876.61102.57975.72
% Public transport service area36.89%13.28%3.26%3.87%7.66%11.87%12.62%
Total number of service capacity (number of trips)1,650,871186,80379,17472,813181,17248,5442,219,377
% Number of service capacity74.38%8.42%3.57%3.28%8.16%2.19%100.00%
Table 5. NSIP categories and population for BMR.
Table 5. NSIP categories and population for BMR.
BMRNSIP CategoriesTotal
No Supply<2021–4041–6061–8081–100>100
BangkokNumber of TAZs8715763494325122546
% Population30.3539.919.607.013.732.327.08100.00
Nontha-buriNumber of TAZs36306331281
% Population53.3734.855.852.122.360.500.95100.00
Pathum ThaniNumber of TAZs5161000058
% Population83.7711.634.600.00%0.00%0.00%0.00100.00
Nakhon PathomNumber of TAZs3081010141
Population831,732 161,292 6152 05652 03001 1,007,829
% Population82.5316.000.610.000.560.000.30100.00
Samut PrakanNumber of TAZs65155521093
Population926,489 309,366 106,047 72,55915,824720201,437,487
% Population64.4521.527.385.051.100.500.00100.00
Samut SakhonNumber of TAZs1782000027
% Population72.6625.711.630.
Table 6. NDIP categories and population for BMR.
Table 6. NDIP categories and population for BMR.
BMRNDIP CategoriesTotal
BangkokNumber of TAZs139111766128131546
% Population45.1523.9012.876.913.467.71100.00
Nontha-buriNumber of TAZs4921224381
Population872,595 219,43937,87142,99462,00920,2281,255,136
% Population69.5217.483.023.434.941.61100.00
Pathum ThaniNumber of TAZs456420158
Population955,556 148,01172,48127,860024,0801,227,988
% Population77.8212.055.902.270.001.96100.00
Nakhon PathomNumber of TAZs2311400341
Population628,471 283,54459,6500036,1641,007,829
% Population62.3628.135.920.000.003.59100.00
Samut PrakanNumber of TAZs4937410293
% Population53.9341.673.
Samut SakhonNumber of TAZs197010027
% Population66.9326.480.006.590.000.00100.00
Table 7. Supply–demand gaps categories and household income for BMR (10% threshold).
Table 7. Supply–demand gaps categories and household income for BMR (10% threshold).
RegionsSupply–Demand Gaps Categories (10% Threshold)
Pathum Thani
<30,00032661,19053.848190,429 15.51156,4794.6073.95
Nakhon Pathom
Samut Prakan
Samut Sakhon
Table 8. Gini coefficients categorized by provinces and household income.
Table 8. Gini coefficients categorized by provinces and household income.
Categorized ByGini Coefficients
Pathum Thani0.88
Nakhon Pathom0.86
Samut Prakan0.81
Samut Sakhon0.78
Household incomeLow income0.83
Middle income0.70
High income0.63
Overall BMR0.75
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Peungnumsai, A.; Miyazaki, H.; Witayangkurn, A.; Kim, S.M. A Grid-Based Spatial Analysis for Detecting Supply–Demand Gaps of Public Transports: A Case Study of the Bangkok Metropolitan Region. Sustainability 2020, 12, 10382.

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Peungnumsai A, Miyazaki H, Witayangkurn A, Kim SM. A Grid-Based Spatial Analysis for Detecting Supply–Demand Gaps of Public Transports: A Case Study of the Bangkok Metropolitan Region. Sustainability. 2020; 12(24):10382.

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Peungnumsai, Apantri, Hiroyuki Miyazaki, Apichon Witayangkurn, and Sohee Minsun Kim. 2020. "A Grid-Based Spatial Analysis for Detecting Supply–Demand Gaps of Public Transports: A Case Study of the Bangkok Metropolitan Region" Sustainability 12, no. 24: 10382.

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