Analysis of the Potential for Transit Oriented Development (TOD) and a Framework for Defining TOD Typologies in a Proposed Mass Transit Corridor

A Case Study of Bengaluru Suburban Railway Corridor

Abstract

The rapid spatial expansion, longer trip lengths and low to moderate population and built densities have necessitated Indian metropolitan cities to leverage the potential of the mass transit systems through TOD policies and Development Control Regulations (DCR). Bengaluru, currently the world’s second most congested city, aims to reduce its mobility issues by proposing a suburban railway project, intending to utilise TOD as a tool to increase ridership. The planning and implementation of TOD around a transit station have been studied to a great extent in European countries. Several authors have argued that assessing TOD potential before the operation of transit services, is a crucial and a research gap in Indian cities. The transit stations' potential is evaluated using parameters that define the objectives and principles of TOD. This study aims to measure the existing TOD potential of the stations in the proposed suburban railway corridor based on the adopted parameters, which are then measured using spatial and quantitative analysis tools to develop a TOD index. Furthermore, TOD typologies are devised using a Latent Class Clustering Model (LCCM), serving as benchmarks for advocating strategies to improve the existing TOD index. By establishing a shared standard within the same typology, LCCM lessens the complexity of administering the urban infrastructure. Thus, this study contributes to the existing body of literature by formulating an approach to assess the existing TOD potential and devising TOD typologies to enable easier incorporation of proposals. In this template file as introduction for the format of this journal. All content should follow the suggested AbstractEnstyle.

Introduction

Contextual background

India has undergone exceptional migration and population expansion, which has led to an unanticipated increase in the number of city dwellers and urban sprawl. With a population of approximately 1.4 billion, India is experiencing rapid urbanisation, causing the metropolis to spread out from its centre (Welle, 2023). According to the 2018 World Urbanization Prospects, India's rate of urbanisation increased by 2.4% between 2010 and 2018 and is projected to increase by 50.9% by 2047 (Kapoor, 2022). The government aimed to limit urbanization, but with urban growth inevitable (Indradjati, 2024), the focus has to shift to managing its challenges, such as longer commutes hours and expanding settlements near city boundaries (Angel, 2023; Behan, Maoh et al., 2008).

Bengaluru, India's fifth-largest metropolis, is rapidly growing, with limited space in the city prompting IT hubs to expand to the suburbs, driving commercial development in surrounding areas (Hanumantharaju and Hanjagi, 2019). This process significantly shapes the city's spatial development, characterized by an outward growth pattern (Sudhira, Ramachandra et al., 2003). To assess the extent of spatial growth within the metropolitan region, Bengaluru's expansion averages approximately 60 square kilometres per year, as reported by the WRI (Dhindaw, 2016). The average trip lengths for two-wheelers, cars, taxis, and buses are 9.8 km, 10 km, 13 km, and 12 km, respectively. This indicates that commuters travel relatively long distances, resulting in average road speeds of 20 km/h, which decreases to just 11 km/h in the core city area.

In addressing this challenge, a collective push for enhanced public transportation services has emerged from the Karnataka State Government, urban planners, and academicians. Encouraging a shift from private vehicles to public transit was viewed as a sustainable solution that would help reduce travel times and lower pollution levels (Guo, Nakamura et al., 2018). To effectively reduce long commute times for residents traveling from the city's suburbs, a suburban railway project has been proposed for Bengaluru and its surrounding areas. According to the RITES feasibility report, Sub-urban Railways Project will act supplementary to the BMTC and BMRCL(BMTC and BMRCL are the existing pubic transport services in Bengaluru)(RITES, 2019). The Karnataka government, in collaboration with Indian Railways, proposed four suburban railway lines in Bengaluru intended to steer commuters away from private vehicles and reduce travel times.Recognizing the potential to increase ridership and reduce travel times, the state government has initiated the implementation of a TOD scheme around all sub-urban railway stations (DULT, 2020; Kulkarni, 2022). However, the assessment of TOD potential along the proposed corridors in this city is a research gap.

TOD, globally recognised concept, integrates land use with transportation planning and encourages a shift from private to public transportation (Singh, D. Y. J., 2021). By designing vibrant, mixed-use communities with diverse employment opportunities, TOD aims to attract people through convenient public transportation access and foster liveable, high-density areas that promote sustainable growth (Vale, 2015). The planning of TOD is guided by key principles such as design, densification, diversity, mixed land use, etc. (Cucuzzella, Owen et al., 2022; Hrelja and Rye, 2024). Additionally, TOD promotes walkability and bicycling by creating pedestrian-friendly streets, reducing car dependency, and fostering sustainable urban growth (Khare, Villuri et al., 2021).

Until 1993, the formal term ‘TOD’ was not formed, and it wasn’t till the early 20^th century, the concept was implemented in the United States and Europe (Shatu, Aston et al., 2022). The first successful and well-known TOD example is Curitiba, Brazil with its Bus Rapid Transit corridor (Guo, Nakamura et al., 2018; Thomas, Pojani et al., 2018). While TOD fosters vibrant, walkable, and diverse communities, it is crucial to assess the variables that determine the TOD potential of a given area or station (Singh, D. Y. J., 2021). Evaluation of TOD potential occurs when transit systems are already operational, and development is underway. However, assessing areas or stations for TOD potential can also be crucial during the planning or proposal stage of a transit corridor (Dittmar and Ohland, 2003). This process encompasses three key aspects: evaluating the effectiveness of existing TOD areas (Khare, Villuri et al., 2019), planning strategies to identify future TOD areas (Maheshwari, Grigolon et al., 2022), and assessing current conditions of stations in relation to proposed transit services (Ibraeva, de Almeida Correia et al., 2020). This study focuses on the latter, assessing TOD characteristics in relation to a proposed metro transit service.

Need of study

The planning and implementation of TOD around transit stations have been extensively studied, particularly following the expansion of Mass Rapid Transit systems worldwide (Cervero and Kockelman, 1997). Despite many successful TOD projects worldwide, a research gap remains in assessing TOD potential prior to planning and implementation. Methods for evaluating TOD potential at the station or corridor level are still lacking in the literature (Motieyan and Mesgari, 2017). Numerous studies and literature evaluate the various parameters that determine TOD potential after transit services have become operational (Mittal and Shah, 2022). However, when a new transit service is being proposed, quantifying the relevant parameters becomes more challenging, as travel behaviour cannot be fully accounted for (Singh, Y. J., Lukman et al., 2017). This study aimed to develop a TOD index, leveraging expert opinions and spatial analysis, to help agencies prioritize station improvements before transit service launches. Additionally, the study signifies the importance of TOD typology in managing development and standardizing station comparisons, offering informed guidance for benchmark stations, which is particularly relevant and valuable for Indian cities, such as Bengaluru.

Research objectives

The objective of this study is to develop a methodology for assessing TOD potential along a proposed suburban railway corridor. This includes identifying relevant variables and tools, evaluating existing station conditions, formulating a comprehensive TOD index, and classifying stations into distinct TOD typologies using spatial analysis and the Latent Class Clustering Method (LCCM).

Methods and Materials

In this study, we utilized Meta Synthesis to identify 9 parameters and their corresponding indicators. These were further reduced using FAHP with Chang’s Extent Analysis. The adopted parameters are used to evaluate the existing situation of the stations using multiple statistical and spatial analysis tools. The results of these evaluations, when combined and compared across all stations, formed the TOD Index. To optimize resource allocation, we developed TOD typologies by classifying stations into clusters using LCCM, based on a Bayesian model. The specific details of each method are discussed in the following section.

Study area

As of 2024, Bengaluru's city population has surged to an estimated 11.99 million, with the metro area population reaching approximately 12.1 million (Census, 2022). This rapid growth highlights the city's ongoing expansion and increasing urbanization. According to the TomTom Traffic Index ranking 2023, an average passenger in Bengaluru spends around 260 hours in traffic and release 275 kg of CO₂ from congestion (TomTom, 2023).

The Bengaluru Suburban Railway Project is proposed to address these growing transportation challenges. To enhance the project's sustainability and expedite its implementation, the plan strategically avoids the time-consuming process of acquiring land in private areas for new tracks and stations. Instead, the organizations have explored the possibility of running the Suburban Rail services along the existing tracks of Indian Railways. This approach leverages existing infrastructure, minimizes environmental impact, and accelerates the delivery of much-needed commuter rail services to the region (RITES, 2019). In the construction of Phase 1, four corridors have been proposed, with Corridor 2 (Figure 1) selected as our study area. This corridor spans 25 kilometres, extending from Baiyappanahalli to Chikkabanavara, and includes 14 stations, 8 at-grade and 6 elevated. Notably, the existing Suburban Railway stations will either be integrated into the design of Indian Railway stations or developed entirely new along the Indian Railway line.

Figure 1. Station details in corridor 2

Defining the area of impact is crucial for measuring the TOD characteristics of a station. This area, surrounding a transit station, includes land use, road networks, public transport, and other urban transport features, all of which play a key role in achieving ridership and determining the TOD potential. (Maheshwari, Grigolon et al., 2022; Sun, Y., Han et al., 2024). The impact zone typically varies within a radius of 500 to 1,000 meters, depending on the geographic and climatic conditions of the area (Nikhil Vijay and Petkar, 2021). In this study, we define this zone as the area within a 1,000-meter walking distance around the stations, allowing for a walk of approximately 6 to 10 minutes. Given Bengaluru's relatively pleasant weather, walking a kilometre is practical and can be done without significant fatigue (Jose, Chowdhury et al., 2020).

Rather than defining the TOD impact zone by distance alone, we have based it on travel time. The impact zone is set to include streets reachable within 6 to 10 minutes of walking from the stations, assuming an average walking speed of 4.5 km/h (Chandra and Bharti, 2013). This was executed in ArcGIS Pro using the TravelTime Plugin. The actual walkable distance calculated with TravelTime is 16.07 kilometres, compared to 43.96 kilometres using conventional radial methods, resulting in a difference of 27.89 kilometres. This difference represents the distance that cannot be covered in 10 minutes of walking without significant fatigue. Including this additional distance in the influence zone would likely encourage more reliance on private vehicles (DULT, 2020).

Meta Synthesis of identified parameters

A structured meta-synthesis was conducted by reviewing peer-reviewed journal articles, case studies, and institutional reports published in the last two decades on TOD planning, implementation, and evaluation. Sources were identified using academic databases such as Scopus, ScienceDirect, and Google Scholar. Only studies relevant to urban and peri-urban mass transit contexts were shortlisted. Based on thematic relevance and citation frequency, 9 key parameters were finalized for further analysis (Olaru, Moncrieff et al., 2019), as shown in Figure 2.

To narrow down the parameters, the study incorporated Fuzzy analysis in the Analytical Hierarchy Process (AHP)(Alwedyan, 2024). Twelve experts, including practitioners, academicians, and private stakeholders, participated in an online AHP survey to assess the relative importance of each TOD parameter(Sharma, Saini et al., 2023). Their responses determined the global weights, which were integrated into the comprehensive TOD Index (Section 3.4). Following this, Chang’s Extent Analysis was adopted in Fuzzy analysis to further refine the experts’ input.

Given the multiple parameters involved in analysing TOD potential, a Multi-Criteria Decision-Making (MCDM) approach is used(Ibrahim, Ayad et al., 2022). AHP, developed by Saaty, is the most used MCDM method for determining the relative importance of different parameters(Saaty, 2008). Though it solves the issue of consistency of human input, it does not account for the uncertainties that are inherent in human evaluation(Chang, 1996; Motieyan and Mesgari, 2017). Therefore, to reduce these uncertainties and capture the natural variability of human judgment, Chang used Extent Analysis to address this issue(Zhu, Jing et al., 1999). By allowing the underlying fuzziness in the data to be evaluated as well, Fuzzy Analytical Hierarchy Process (FAHP) can eliminate the judgmental uncertainties that arise in the conventional AHP method (Patnala, Parida et al., 2020).

Figure 2. Meta Synthesis of the identified parameters

The AHP inputs (crisp numbers, as shown in Table 1) are converted into FAHP variables through a process called Fuzzification, which represents a range of values - higher, medium, and lower (h, m, l) - using a triangular membership function (Wey, 2013). Triangular membership is easy, fast and a reliable way of getting a crisp output from the fuzzy variables (Motieyan and Mesgari, 2017). Let X= $(x_1,x_2,x_3,\dots \dots .,x_n$ ) be a set of object, and U= $(u_1,u_2,u_3,\dots \dots .,u_m$ ) be the set of goals, then according to the Chang’s extent analysis method each object is taken and an extent analysis is performed for each goal ${(g}_i)$ . Thus we get the m extent analysis for each object like: $M_{g_i}^1,M_{g_i}^2,{\dots \dots ,M}_{g_i}^m$ where i = 1,2,3,…..,n and $M_{g_i}^j$ (j=1,2,….m) are Triangular Fuzzy Numbers (Ke, Furuya et al., 2021).

Table 1. Table showing relative importance in an AHP and FAHP analysis.

AHP input	Relative importance	FAHP input
1	Equal Importance	(1,1,1)
3	Moderate Importance	(2,3,4)
5	Strong Importance	(4,5,6)
7	Very Strong Importance	(6,7,8)
9	Extreme Importance	(9,9,9)
For reciprocal values
1/3	Moderate Importance	(1/4, 1/3, 1/2)
1/5	Strong Importance	(1/6, 1/5, 1/4)
1/7	Very Strong Importance	(1/8, 1/7, 1/6)
1/9	Extreme Importance	(1/9, 1/9, 1/9)

The steps to perform the FAHP using Chang’s Extent Analysis (Ke, Furuya et al., 2021; Patnala, Parida et al., 2020) are discussed below:

Step1: Calculate the fuzzy synthetic extent with respect to ith parameter by using the following equation.

  

$$S_i=\sum _{j=1}^m{M}_{g_i}^j\underset{}{\overset{}{⨂}}{\left[\sum _{i=1}^n\sum _{j=1}^m{M}_{g_i}^j\right]}^{-1}\dots \dots \dots \dots \left(1\right)$$

Step2: Calculate the degree of possibility that one fuzzy number is greater than another fuzzy number, $M_2\ge M_1$ where $M_2=(l_2,m_2,u_2)$ and $M_1=(l_1,m_1,u_1)$ . The degree of possibility is defined as

$V(M_2\ge M_1)=sup[min({\mu }_{M_1}\left(x\right),{\mu }_{M_2}\left(y\right))]$ …………(2)

Step 3: Calculate the degree of possibility for a convex fuzzy number to be greater than total (k) fuzzy numbers which can be defined by the equation:

$V(M\ge M_{1,}{M}_{2,},\dots \dots M_k)$

$=V[(M\ge M_1)and(M\ge M_2)and,\dots ..,and(M\ge M_k)]$

$=minV(M\ge M_i),i=1,2,\dots \dots .,k$ …………(3)

Assume that $d^{\prime }\left(A_i\right)=minV(S_i\ge S_k)$

For k = 1, 2,…..,n; $k\ne i$ . Then the weight vector is derived by

$W^{\prime }={(d^{\prime }\left(A_1\right),d^{\prime }\left(A_1\right),\dots \dots ,d^{\prime }\left(A_n\right))}^T$ …………(4)

Where $A_i(i=1,2,\dots ..,n)arenelements$

Step 4: The minimum values in Step 3 is taken as ‘weight vectors’ for each parameters and then normalized to get the Crisp Weight of each parameter (Defuzzification): $W={(d\left(A_1\right),d\left(A_1\right),\dots \dots ,d\left(A_n\right))}^T$ , where W is a non-fuzzy number and denotes the weights for each parameters.

Step 5: Consider the top-ranking parameters that collectively account for 75% and above of the total weight as a standard cut-off.

Data Collection

For every parameter and their indicator, majority of the data utilized in this study is sourced from the Master Plan and other secondary data provided by Bruhat Bengaluru Mahanagara Palike (BBMP) as shown in Table 2.

Table 2. Data Collection and Sources

Parameters and Indicators		Unit (per station area)	Data used and Source
1	Design
1.1	Street Design	Number of streets with 90 degree turns	Road Network, BBMP
1.2	Open Space	Area (Sq. Km) under public green spaces	Land use map, CMP Bengaluru, 2020
1.3	Multimodal Integration	Area (Sq. Km) under catchment of auto stand and bus stops	Land use map, Master Plan 2018
1.4	Walkable Roads	Length (Kms) of footpath	Road Hierarchy, BBMP
1.5	LL cluster area	Area (Sq. Km) under L- L cluster	Land use map, Master Plan 2018
1.6	Block Size	Average size of block (Sq. Km)	Land use map, Master Plan 2018
1.7	Possible Cycle path	No of Roads with RoW of 12 m and above	Road Hierarchy, BBMP
2	Density
2.1	Population Density	Number of People per Sq. Km	Master Plan, Zonal Plans
2.2	Road Density	Length of roads in Km per Sq. Kms	Road Network, BBMP
2.3	Commercial Density	Number of commercial establishments	Land use map, Master Plan 2018
3	Land use
3.1	Zhang and Guidon Mixed-ness Index	Share of non-residential land use (Share of residential land use to non-residential land use)	Land use map, Master Plan 2018
3.2	Vacant Land	Share of vacant land to total area
4	Diversity
4.1	Shannon entropy	No unit (As per formula)	Land use map, Master Plan 2018
4.2	Ritsema-Koomen	No unit (As per formula)
4.3	HHI	No unit (As per formula)

Analysing the stations - Rationale and Process

Design

Design is a critical element in TOD planning, as highlighted by numerous studies and research (Rhoads, Rames et al., 2023). Key design features, such as an efficient street network, well-maintained sidewalks, and optimally sized blocks, contribute to a more walkable and accessible environment (Rhoads, Solé-Ribalta et al., 2023).

Street Design

Street design is a key factor in assessing the TOD potential of a station area. In this study, we analyse the number of 90-degree turns within each station's TOD zone. Angular connectivity, represented by these 90-degree turns, is indicative of an effective street layout that enhances the mobility of both people and vehicles (Nag, Sen et al., 2022). This system, not only supports better traffic flow and accessibility but also encourages active transportation modes like walking and cycling (Maheshwari, Grigolon et al., 2022; van Nes, Yamu et al., 2021). We calculated the angles between segments of roads using the Bearing Angle tool in ArcGIS Pro. As human brain cannot explicitly differentiate between angular differences of less than 5 degrees, we ranked the streets based on the deviation from the major axis of the surrounding streets (Huang, B.-X., Chiou et al., 2020; Yıldırım and Çelik, 2023). In this study, we determined that streets with greater deviations from the major axis (90°) would receive lower rankings.

•    Rank 1- Roads aligning with major axis
•    Rank 2- Roads having ± 5° deviation from major axis
•    Rank 3- Roads having deviation between ± 5° to ± 10° from major axis
•    Rank 4- Roads having deviation between ± 10° to ± 15° from major axis
•    Rank 5- Roads having deviation more than ± 15° from major axis

All the streets within the station areas were evaluated, and those with ranks of 3 or higher were identified. The counts of these streets were then aggregated for all stations along the corridor.

Green Public Space

Open and green spaces enhance the liveability index, thereby increasing TOD potential, as improving living standards is a key objective of TOD (Purwohandoyo, Reinhart et al., 2023). Incorporating green spaces within a TOD framework has resulted in a 15% rise in the liveability index, attracting more residents and boosting overall sustainability (Elderbrock, Russel et al., 2024). The unit of measurement is the area (square metres) under green public space.

Multimodal Integration

Multimodal integration, including the effective coordination of paratransit with mass transit systems, significantly enhances transit ridership by improving accessibility and last-mile connectivity (Randall, Brugulat-Panés et al., 2023). Connecting public transport, walking, cycling, and paratransit services enhances ridership and overall usage of the mass transit system (Vegad and Bhagat, 2020). This analysis uses the consolidated catchment area within 200 meters of all bus stops and auto stands in the impact zone as the unit of measurement.

Figure 3. Spatial representation of Design analysis

Walkable Streets

Walkability is greatly affected by the availability and condition of footpaths (Veronica and Shirly, 2025), which should be free from obstructions and accessible to all (Loo, 2021). Research consistently highlights the benefits of walkable streets in enhancing overall area development and are a key focus for stakeholders, planners, and governments (Guzman, Arellana et al., 2022; Lee, 2020). This study measures the number of streets with accessible footpaths on at least one side within each station's impact zone.

Possible Cycle Tracks

Walking and cycling infrastructure are essential for promoting sustainable active mobility (Loo, 2021). As there are currently no cycle tracks in any of the stations’ impact zone, we assessed the streets to identify potential locations for future cycle tracks. The unit of measurement is the total length of streets with a Right of Way (RoW) width greater than 12 meters, which is necessary for accommodating cycle tracks (ITDP-India-Programme, 2020).

Block Size

A block consists of multiple plots grouped together and surrounded by roads on all sides (Słomska-Przech and Słomski, 2024). Smaller blocks enhance the urban fabric and create a more efficient street network (Anabtawi and Scoppa, 2022). They are also easier to redevelop, as land acquisition is simpler, and similarly sized smaller blocks are well-suited for plot amalgamation, facilitating smoother redevelopment processes (Bibri, Krogstie et al., 2020). The unit of measurement is the average block size at each station. This is further scrutinized using a Multi-Variate Clustering analysis in ArcGIS Pro using the ‘k-Means clustering’ algorithm. By categorizing block sizes into similar groups, we can assess TOD potential-stations with a higher number of blocks in the 'smaller block' category are more favourable for redevelopment schemes (Maheshwari, Grigolon et al., 2022).

Spatial Correlation

Spatial Correlation refers to the degree to which the characteristics of one location are similar to those of nearby locations. In the context of urban form, it helps identify patterns in the spatial arrangement of built environments. Urban settlements typically have three types of layouts: Dispersed, Clustered, and Random. Among these, the Clustered arrangement is the easiest to redevelop because it consists of blocks with similar sizes and characteristics (Ibrahim, Ayad et al., 2023). This analysis is performed in ArcGIS Pro using Spatial Auto-Correlation tool and uses the Moran’s Index statistic to evaluate the correlation. The output is Moran’s index with Z-score, and p-value. The significance of z-score can be checked by the significance value generated by the tool (ESRI, 2022).

Density

Density plays a crucial role in the success of TOD, as it directly supports consistent ridership for Mass Transit Systems. High-density development around transit stations is a key recommendation from stakeholders, planners, and researchers alike (Singh, Y. J., Fard et al., 2014). This study analyses three types of densities: Population density (people per square kilometre), Road density (length of roads per square kilometre), and Commercial establishment density (number of commercial establishments per station area).

Population Density

TOD revolves around increasing the number of people in the vicinity, leading to greater use of mass transit and reduced reliance on private vehicles (Ibrahim, Ayad et al., 2022). This can be achieved by raising the Floor Area Ratio (FAR) around station areas based on road width, Land value capture and managing Development Control Regulations (DCR)(Thomas, Pojani et al., 2018).

Road Density

Road density enhances connectivity within station areas and boosts the potential for walkable streets by integrating footpaths (Sun, G., Wallace et al., 2020). It also supports the development of commercial and institutional establishments along these roads (Sung and Oh, 2011).

Commercial Density

The optimal urban capacity of each station is achieved through a diverse mix of land uses and amenities, particularly commercial and business establishments (Zhao, Yang et al., 2018). The unit of measurement is the number of commercial establishments in the station area.

Land use

TOD emphasizes the integration of land use and transportation, making it crucial to analyse the land use around each station. This study assesses land use by evaluating the diversity of land uses in the impact area and identifying available vacant land (Pengjun and Shengxiao, 2018). A balanced mix of residential, commercial, and recreational uses around transit stations can increase transit ridership by up to 25% (Nesmachnow and Hipogrosso, 2022).

Mixed-ness Index

Land use will be analysed using the Zhang and Guidon Mixed-ness Index, which measures the proportion of non-residential land use within the station area (Shi, Zhao et al., 2021). This mix promotes walking and cycling by providing more opportunities for people to live close to where they work, shop, or access services (Ramlan, Osman et al., 2021). Zhang and Guidon Mixed-ness Index (Huang, R., Grigolon et al., 2018) is determined by the formula below.

$\frac{{\sum }_{\cap 𝐢}^{}{𝐒}_{𝐜}}{{\sum }_{\cap 𝐢}^{}\left({𝐒}_{𝐜}+{𝐒}_{𝐫}\right)}\forall 𝐢$ …………(5)

${𝐒}_{𝐜}$ = Sum of the total area under non-residential land uses

${𝐒}_{𝐫}$ = Sum of the total area under residential land use within i.

Vacant Land Availability

The availability and ownership of vacant land near a station is a critical factor for development. Vacant land offers a significant advantage for new projects or establishments, as it simplifies the process of land acquisition (McPhearson, 2012).

Diversity

Diversity in land use refers to the integration of various functions, both horizontally and vertically, within a development (Elldér, Haugen et al., 2022). The goal is to provide residents with convenient access to essential amenities, workplaces, recreation, and entertainment within the influence zone (Talal and Santelmann, 2021). Following indeces are used to analyse diversity:

Shannon entropy

This index is based on quantifying the share of each land-use in the diverse land-use using natural log (Dewa, Buchori et al., 2022).

$-{\sum }_{}^{}\left(\frac{𝐧}{𝐍}*ln\frac{𝐧}{𝐍}\right)$ …………(6)

$𝐧$ = Area of Specific Land use within station area

$𝐍$ = Total area of analysis i.

Figure 4. Spatial representation of Density, Diversity and Land use analysis

Ritsema and Koomen Entropy

Diversity is determined by the share of individual land-use from all other land-use Higher entropy value indicates higher TOD potential of the area(Ritsema van Eck and Koomen, 2008).

$\frac{{\sum }_{\cap 𝐢}^{}{𝐒}_{𝐜}}{{\sum }_{\cap 𝐢}^{}\left({𝐒}_{𝐜}+{𝐒}_{𝐫}\right)}\forall 𝐢$ …………(7)

$𝐧$ = Area of Specific Land use within station area

$𝐍$ = Total area of analysis i.

Herfindahl and Hirsehman (HHI) Index

The Herfindahl-Hirschman Index (HHI) is a widely recognized metric for assessing market concentration. A higher HHI suggests a market with fewer competitors and greater dominance by a single entity, approaching a monopoly (Peleckis, 2022). It is calculated as, $\sum {\left(𝐢=1\right)}^{𝐤}{\left(100*{𝐏}^{𝐢}\right)}^2$ , where ${𝐏}^{𝐢}$ = Share of individual Land uses from the total station area i (Huang, R., Grigolon et al., 2018).

Comprehensive TOD Index by Normalization

Spatial and statistical analyses is used to evaluated and compared across all stations using a comprehensive TOD index. Indicator values are normalized using the Maximum-Minimum Normalization technique, accounting for both beneficial and non-beneficial factors (Educative, 2024).

Each indicator used in the TOD Index is classified as either beneficial or non-beneficial based on its impact on TOD potential. A beneficial relation, such as higher population density, increases the TOD index, while a non-beneficial relation, like a larger average block size, reduces it (Supaprasert, Lohatepanont et al., 2021). This normalization technique converts all parameter units to a uniform scale, ranging from 0 (minimum value) to 1 (maximum value) size(Patnala, Parida et al., 2020). The empirical formula for each type of relation followed in the research is discussed below:

Beneficial Positive Relation = $\frac{X-X^{min}}{X^{max}-X^{min}}$ …………(8)

Beneficial Negative Relation = $\frac{X^{max}-X}{X^{max}-X^{min}}$ …………(9)

To maintain the scope and focus of the study, only parameters with scores above a defined cut-off, established based on thresholds identified in prior literature were retained for further analysis. The final Global and Local Weights were calculated solely for these selected parameters, using expert rankings from the AHP survey as shown in Table 3.

Table 3． Table showing Global weights of Parameters and Indicators

1	Design	0.382
1.1	Street Design	0.2
1.2	Open Space	0.05
1.3	Multimodal Integration	0.15
1.4	Walkable Roads	0.2
1.5	LL cluster area	0.2
1.6	Block Size	0.15
1.7	Possible Cycle path	0.05
2	Density	0.25
2.1	Population Density	0.4
2.2	Road Density	0.35
2.3	Commercial Density	0.25
3	Land-use	0.211
3.1	Mixed-ness Index	0.75
3.2	Vacant Land availability	0.25
4	Diversity	0.158
4.1	Shannon entropy	0.225
4.2	Ritsema-Koomen Entropy	0.375
4.3	HHI	0.4

TOD Typology

TOD typology refers to the classification of areas or stations based on their potential for TOD based on certain characteristics and criteria. This classification helps in understanding the varying needs, opportunities, and challenges of different station areas and facilitates more targeted planning and development strategies (Gu, Lin et al., 2024). By classifying station areas into distinct typologies based on their TOD potential, planners can tailor strategies to address specific characteristics and needs, optimize resource allocation, and develop targeted policies (Ibraeva, de Almeida Correia et al., 2020).

For accessing the typology, a Latent Class Clustering Method (LCCM) is implemented, which is a statistical modelling technique used to identify groups or clusters of observations with similar characteristics (Sfeir, Rodrigues et al., 2022). It is based on the patterns of response to a set of categorical and continuous variables (Sfeir, Abou-Zeid et al., 2021). LCCM identifies groups or clusters within data based on underlying patterns that are not immediately apparent through traditional descriptive analysis (Lahoz, Pereira et al., 2023). It estimates the prevalence of these hidden or latent classes, revealing structures within the data that are not directly observable (Huang, R., Grigolon et al., 2018).

Figure 5. LCCM Algorithm

The methodology in the model (Brusa, Pennoni et al., 2024) is followed as below (Figure 6).

1.    Define Latent Variables - Uses the statistical technique called Expected Maximum Likelihood Approach.
2.    Initial Guessing - Uses Parameter estimates to update Expected values to come as close to observed values.
3.    E-Step - It provides Initial (Random) estimate to fill in missing information on Latent Class Membership.
4.    M-Step – Maximum Likelihood step produces Maximum Likelihood (ML) estimates from complete (contingency table).
5.    Iteration Step is repeating the E-step again where it uses the parameter estimates to update expected values for cell counts (n) in complete tables.

LCCM generates a variety of clusters, and by conducting a model fit analysis, we can determine the most suitable cluster that best aligns with the data (Sfeir, Abou-Zeid et al., 2021). There are several methods to evaluate the model's fit, as outlined below.

•    Bayesian Information Criterion (BIC) and Akaike’s Information Criterion (AIC) = The lower, the better (Brownlee, 2019)
•    Log-Likelihood value = The higher, the better (Naldi and Cazzaniga, 2020)
•    Entropy values = The higher, the better (Weller, Bowen et al., 2020)
•    P-value = The lower, the better (Lezhnina and Kismihók, 2022)

Figure 6. Methodology followed by the model.

When LCCM is applied to continuous data instead of binary data, it is referred to as Mixture Modelling (Sfeir, Rodrigues et al., 2022). In this approach, each estimated value is associated with several components or classes. The theory behind Mixture Modelling under the Latent Class Model is that the model identifies hidden normal distributions within the data (Su, Zhang et al., 2021). This method has been used in this study to classify the stations into TOD typology.

Results and Discussion

Results of Fuzzy AHP

As discussed in the previous section, FAHP is used to reduce the identified parameters. The top-ranking parameters that collectively account for 75% or more of the total weight were selected as a standard cut-off. These chosen parameters include Design, Density, Diversity, and Land Use as shown in Table 4.

Table 4. Results of the Fuzzy AHP analysis

Parameters	Fuzzy AHP	Rank
Connectivity (S1)	0.00%	9
Demand (S2)	3.00%	7
Density (S3)	19.71%	2
Design (S4)	29.43%	1
Destination (S5)	0.75%	8
Diversity (S6)	11.83%	4
Market Potential Value (S7)	9.46%	6
Housing (S8)	9.80%	5
Land use (S9)	16.35%	3

Results of Spatial Analysis and developing a TOD Index

The spatial analysis of the selected parameters, along with their respective indicators, produces an overall normalized score for each station. This score is calculated by multiplying the contribution of each indicator by its global weight. The final score, presented in Table 5, represents the comprehensive TOD Index, which reflects the existing TOD potential of all stations in the corridor.

Table 5. TOD Index for stations

Sr. no	Stations	TOD Index
1	Chikkabanavara	0.361
2	Myadarahalli	0.422
3	Shettyhalli	0.471
4	Jalahalli	0.314
5	Yeshwantpur	0.582
6	Lotegollahalli	0.384
7	Hebbal	0.338
8	Kanakanagar	0.495
9	Nagavara	0.578
10	Kaveri_Nagar	0.480
11	Banaswadi	0.332
12	Sevanagar	0.440
13	Kasturi_Nagar	0.360
14	Baiyappanahalli	0.380

The results indicate that Yeshwantpur station achieved the highest TOD index score, signifying its strong TOD potential. This is primarily due to the presence of a truck terminal and a perishable goods market, which significantly contribute to the station's commercial density. Yeshwantpur’s existing high potential, which corroborates the findings of RITES, has led to its selection as an interchange with a higher expected ridership and increased trip generation.

Result of LCCM analysis

In the Latent Class Clustering Model, LatentGold 5.1 was used to generate outputs for various cluster sizes. We evaluated cluster sizes from 2 to 6 and found that a 3-cluster model provided the best fit and the most balanced distribution of stations (van der Nest, Passos et al., 2020). The software also produced a Cluster Quality output, which assessed the quality of the clusters (Figure 7). The software assigns each station to one of the three clusters, as illustrated in Figure 8. Each cluster represents a TOD typology, which serves as a benchmark for formulating infrastructure proposals and development strategies (Lyu, Bertolini et al., 2016).

Figure 7. Cluster Size and Cluster Quality

The clusters are named based on literature specific to South Asian countries (Lyu, Bertolini et al., 2016). Cluster 1, termed the Corridor Station Cluster, is characterized by mixed land use, medium to high density, street-oriented retail, and new developments (Su, Zhang et al., 2021). Cluster 2, known as the Centre Station Cluster, features high density, significant commercial density, and diverse land uses (Woo, 2021). Cluster 3, called the Neighbourhood Station Cluster, is defined by predominantly residential land use, small commercial establishments, and a well-connected road network (Zhou and Yang, 2024).

Figure 8. Location of Clusters in the corridor

Conclusion

This study highlights the significance of evaluating TOD potential for proposed transit services. By employing Chang’s Extent Analysis in Fuzzy AHP (FAHP), we selected and refined parameters, accounting for uncertainties in human judgment. These parameters enabled the analysis of current conditions in station impact zones, culminating in the creation of a comprehensive TOD Index. The index revealed that stations with higher population density, stronger commercial presence, and better street design, such as Yeshwantpur, Nagavara, and Kanakanagar, exhibited higher TOD potential. This suggests that TOD potential is closely linked to factors that support higher ridership, walkability, and land-use efficiency. Furthermore, the similarity in scores among adjacent stations highlights the interdependence of spatial development within a transit corridor, reinforcing the need for coordinated planning.

To optimize resource allocation and tailor interventions, we developed TOD typologies using the Latent Class Clustering Method (LCCM). This approach is particularly useful in resource-constrained settings, where it can guide phased investments and station-specific strategies. By identifying clusters of stations with similar characteristics, LCCM reveals underlying spatial patterns often missed by traditional analyses. This method provides a structured way to link station characteristics with typology-based planning, supporting scalable and efficient TOD implementation.

Unlike many TOD studies that focus on post-implementation outcomes, this research contributes a proactive, data-driven framework for assessing TOD potential in advance of transit service rollout. It aligns with contemporary efforts to integrate spatial analytics with planning practice, especially in developing country contexts like India. The study demonstrates that TOD planning is most effective when guided by measurable indicators and typology-based strategies, which can be adapted to other emerging transit corridors.

Author Contributions

Conceptualizing, S. M., and P. V.; Methodology, S. M., and P. V.; Software, S. M.; Resources, S.M. and P. V.; data curation, S. M., and P. V.; Writing-original draft preparation, S.M.; Writing-review and editing, S. M., and P. V.; Supervision, P. V. All authors have read and agreed to the published version of the manuscript.

Ethics Declaration

The authors declare that they have no conflicts of interest regarding the publication of the paper.

Acknowledgments

The authors would like to extend their heartfelt gratitude to Mr. Darsan Kumar PJ as well as to the experts who generously contributed their time and insights to the AHP survey. Special thanks are also due to Dr. Yamini Jain Singh and Mr. Mark Brussel from the University of Twente, The Netherlands, for their invaluable guidance and encouragement.

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