(PDF) Planning for Resilient Transit-Oriented Development: Case of Delhi

©2024 Taylor & Francis Group, ISBN 978-1-032-78443-4 DOI: 10.4324/9781003487890 Planning for Resilient Transit-Oriented Development: Case of Delhi Mutum Chaobisana1 and Prof. Sanjukkta Bhaduri2 1 Ph. D Scholar, Urban Planning, School of Planning and Architecture, Delhi, India E-mail:

2 Professor, Head, Urban Planning Department, School of Planning and Architecture, Delhi, India E-mail:

Abstract Transit-oriented development (TOD) has been a universally adopted technique to plan for integrated land use and transportation, to reduce private mode and shift to sustainable mobility. TOD helps in development of climate-resilient cities by reducing the GHG emissions, requirement of land to accommodate growing population and help bring about long-term climate resilience. Due to the nature of the intense development in a TOD, it is pertinent to plan TODs to maximise resilience. The study takes cognizant that planning for a resilient TOD is reliant on the maximum population the TOD catchment area may hold within the carrying capacity of the area. This will help to accommodate population to the extent that the TOD catchment area can support, without damaging the natural and built environment and trading off the available natural resources. The TOD catchment area has a certain carrying capacity and if exceeded may reduce the desired outcome of a successful TOD and reduce the efficiency of the TOD further. The TOD catchment is dependent on carrying capacity which are further categorised into two typologies- assimilative capacity (natural resources based of the catchment area like air quality, water availability and environmental resources) and supportive carrying capacity (man-made and built resources and infrastructure like housing, physical and social infrastructure). There are different scales of TOD – regional, city and community level. In the research, community level is selected. The community level has an influence of one lakh population as per the Master Plan for Delhi. This lower scale was considered amongst the other scale as such TOD demonstrates different functions like trip origin (residential predominant catchment) and trip destination (commercial, institutional, and industrial predominant catchments which are work and activity centers) in lower scale. Such categories are not present at city level and regional level TOD as they usually function as a trip destination. The study is based on residential land use – residential predominant profile representing a trip origin TOD. The aim of the study is to gain an understanding of that planning a sustainable and efficient TOD is dependent on the carrying capacity of the TOD catchment area. The methodology used in the study are (i) Fuzzy Delphi for identification of indicators, (ii) selection of comparable TOD Nodes in Delhi by developing a hierarchy of TOD Future is Urban – Urban Resilience, Capacity Building, and Nature Bases Solution Editors: Utpal Sharma, Swati Kothary and Vibha Gajjar ©2024 Taylor & Francis Group, ISBN 978-1-032-78443-4 DOI: 10.4324/9781003487890 nodes, and (iii) Multiple regression for establishing the relationship between the dependent (Efficiency of TOD) and independent variable (Carrying Capacity of the TOD catchment). Fuzzy Delphi method was adopted for selection of indicators and 5 dependent variables (11 indicators) of efficiency of TOD node and 6 independent variables (19 indicators) of carrying capacity were used for the analysis. A methodology for selection of TOD Node for the study was established by identifying all the TOD Node in Delhi which falls under community level TOD depending on the average daily ridership, population in the catchment area and residential land use as per Zonal Development Plan of the Delhi Development Authority (DDA). Finally, 5 such TOD catchment areas were selected. A relationship between the dependent and independent variables was established using multiple regression through data from the 5 TOD catchment areas. The research concludes by establishing a relationship that functioning of a TOD Node efficiently and sustainably (within its available resources- natural and built) is dependent on the carrying capacity of the TOD catchment. The results of the study showed that the independent variables, i.e., carrying capacity of the TOD catchment area comprising 6 variables namely, Population Density, Built Environment, Physical and Social Infrastructure, Public Space, Air Quality and Land Availability are significant in determining the efficiency of the TOD Node. Infrastructure (physical and social) was the most important factor. This implies that appropriate infrastructure is forerunner for efficient and sustainable TOD planning at community level. The article provides policy recommendations for the development of TOD as a resilient neighbourhood within the TOD carrying capacity to minimise the adverse impact of development and related outcomes. Keywords: Carrying Capacity, Resilient Neighbourhood, Transit-oriented Development Introduction identifies city as one of the five key systems that generate the most The present 55% of the world urban greenhouse gas emission. Taking areas is expected to increase to 68% by urgent climate action in cities and 2050 and about 90% of the increase to orientation towards sustainable take place in Asia and Africa (United development could reduce emissions Nations, 2018). Urban areas account from urban buildings, materials, for more than 70% of CO2 emissions transport, and waste by nearly 90% by (UN Habitat, 2020) and are the key 2050 (IEA, 2021). Collectively, the actors of climate change. Developed seven sectors that provide energy, regions such as East Asia, South Asia water, mobility, shelter/buildings, and Sub-Saharan Africa is estimated to waste management, food, and green contribute to 90% of urban growth (UN public spaces are associated with C. C., 2020). Climate Action Plan approximately 90% of global GHG (2021-2025) of the World Bank emissions (Ramaswami A, 2016). The Future is Urban – Urban Resilience, Capacity Building, and Nature Bases Solution Editors: Utpal Sharma, Swati Kothary and Vibha Gajjar ©2024 Taylor & Francis Group, ISBN 978-1-032-78443-4 DOI: 10.4324/9781003487890 use of fossil fuel will dominate through ‘Transit Oriented Development’ 2050 with coal, oil and gas continuing (TOD). to meet 74% of the energy demand (Nyquist, November 2016). Emission TOD aims to reduce sprawl, with an from transport have increase steadily approach of integrated development accounting for 24% of direct CO2 with high-capacity transit, compact emissions from fuel combustion. In development with high density, mixed India, transport sector is responsible land use with live and work within for 13.5% of the country’s energy- walkable and bikeable related CO2 emissions, with road neighbourhoods, accessible public transport accounting for 90% of the open spaces for a safe environment sector’s final energy consumption with an overarching aim to reduce (Wilson, 2020). Urban transport has carbon emissions in long term. 64% share of all travel kilometres in India and it is expected to triple by Transit-oriented development (TOD) 2050 (Lerner, 2011). In a study using has been a universally adopted data of 46 international cities, the technique to plan for integrated land aspect of the implication of TOD on use and transportation. Due to the economy of the city found that the nature of intense development in a regional product per capita was higher TOD, it is pertinent to plan TOD in a city which is less dependent on stations to maximise resilience in the automobiles and auto-oriented cities TOD project area and beyond. are poor economic performers (Kenworthy, 1996). Transit-oriented City's challenges are manifold and Development cannot be solved in a disintegrated Transit Oriented Development manner; hence a holistic and dynamic planning approach is the urgent need of TOD is concept of managing urban the hour. The A-S-I approach is a way development in a transit corridor which to structure policy measures to reduce has characters like a mixed used the environmental impact of transport community within a walking distance in cities, the approach was first of a high-quality public transit stop officially mentioned in 1994 in with high density, compact, pedestrian German parliament´s Enquete oriented and vibrant neighbourhood Commission (Daniel Bongardt, 2019). (Institute, 2016). TOD is a planning The A-S-I principle constitute 3 technique or strategy wherein intensive principles - ‘Avoid’ (reduce motorized urban development is encouraged transportation), ‘Shift’ (to more around public transit station to compel sustainable modes such as public people to live and work near transit transport and non-motorized transport) station which reduces the use of and, ‘Improve’ (greener cleaner automobile and promote public transit technologies for motorized transport). (Gregus, 2002). TOD is a mixed-used One of the tools of the ‘Shift’ model community that promotes effective aims at use of public transport as a land-use growth within a foot walking prime focus with development around distance (quarter mile or 500-800 m) of the transit nodes and is termed as Future is Urban – Urban Resilience, Capacity Building, and Nature Bases Solution Editors: Utpal Sharma, Swati Kothary and Vibha Gajjar ©2024 Taylor & Francis Group, ISBN 978-1-032-78443-4 DOI: 10.4324/9781003487890 a high-quality public transit stop with i. Residential TOD high density, compact, pedestrian- ii. Activity centre TOD oriented and vibrant neighbourhood iii. Potential TOD and (Crutis, 2012). iv. TOD non-suitability (less likely for the residents to use the public TOD Influence Zone transport) The immediate area adjoining a high- Based on the function and scale of the capacity transit corridor or node in a TOD influence, TOD can be TOD with intensive development is categorised into typologies. generally referred as TOD Influence Zone/Area. A TOD influence zone TOD catchment and carrying refers to the area centred around a capacity transit station taking a walking distance as a radius. The walkable The TOD influence area and TOD distance generally accepted is a quarter catchment area is interchangeably mile around a transit node, i.e., 400m- used in various research. However, in 800m. Concept of Influence Zone in this research TOD catchment is a zone TOD ranges from 400 m to 800 m adjoining the TOD influence Zone distance from transit station ( (Crutis, (which is the immediate area 400m to 2012); (Cervero R. &., 1997). As per a 800m radius where transit station is TOD study in 21 cities of America, located), and can be called the 400m (a quarter mile) catchment for transition area which is in between the jobs was relevant and for population a TOD influence Zone and the general catchment of 800m (a half mile) existing development/ built up. These determines the direct influence areas in general reflects the benefits of (Guerra, 2012). TOD proximity. Such area extended approximately 1.2 km in Australia TOD typologies (Sim, 2015). As per a study on TOD catchment area, it was observed that Calthrope, (1993) identified three an area up to 1.5 km from the transit types of setting where TOD can be station clearly shows the impact of the developed. The three categories TOD (Guerra, 2012). This study include redevelopment site, infill site adopts 1.5 km as TOD catchment area and new growth area. Redevelopment which is the bilkable distance from the on existing site and infill site would TOD Node or the transit station. require selection of the site, integration of viable existing uses and its Carrying capacity of a catchment area surrounding for success of TOD. For is the maximum population that the new growth areas, the objective is to designated area can support without hold sprawl and other topographical damaging the productivity of the and ecological constrains needs to be habitat permanently. The concept of considered while selecting the site. A carrying capacity needs to be adopted study in Brisbane (Kamruzzaman, in planning for TOD to realize 2014) identified four typologies of efficiency of the TOD node without TOD as follows: trading off the available resources. Future is Urban – Urban Resilience, Capacity Building, and Nature Bases Solution Editors: Utpal Sharma, Swati Kothary and Vibha Gajjar ©2024 Taylor & Francis Group, ISBN 978-1-032-78443-4 DOI: 10.4324/9781003487890 TOD and resilience nations. TOD being originated from developed nations and is slightly TOD is a long-term planning approach different in terms of settlement pattern to achieve sustainable development from the developing countries. The through compact development around Indian cities are adorned with existing transit nodes and corridors. Although high densities, organic pattern of academia, planners, policy makers and growth and land uses, inadequate and urban administrators recognize the limited supporting infrastructure and importance of TOD in building movement pattern with modal share resilient and sustainable cities, the different in the Indian cities. implementation of the approach has not delivered the expectations (Ray, Future is TOD: As per Ministry of 2022). As per ITDP (2017), TOD helps Housing and Urban Affairs (MoHUA), build resilient cities and communities. by 2025, 27 cities in India will have In a study in Jabodetabek, Indonesia MRTS and about 1,000 km of metro (Hasibuan, 2014), TOD helps build rail line is under construction. At resilience in the urban structure by present metro rail is operational in 19 reducing fuel consumption and carbon cities with about 750 km. TOD Node emission, and maintaining the planning and design needs to build availability of green open space area resilient communities where the for the urban ecosystem. As per the UN adverse impact on the environment is Climate Technology Center and minimized. (Ibraeva A. d., 2020). Network, TODs have been shown to reduce travel by at least half compared Research Gap: There is a need to to average urban development. A study understand the Carrying Capacity for in Australia found that residents of the TOD catchment area based on the TODs each generated on average 4 function of the station and the scale of tonnes of greenhouse gas from their influence of the TOD Node. There is a daily travel, or 2.5 tonnes in well- research gap in understanding the located TODs, compared with 8.4 carrying capacity of a TOD catchment tonnes in standard fringe development while planning TOD efficiently. In (UN C. P., 2023). Another study in present cases, TOD planning is Dhaka, Bangladesh on the effects of undertaken considering the universally TODs on CO2 emission showed that accepted TOD principles. In India, controlling the spatial heterogeneity although cities have formulated their and spatial dependency of a TOD policies and regulations, national neighbourhoods, TODs have the policies/ State Development Policies potential to reduce CO2 emissions for do not consider the Carrying Capacity work and school trips. However, of TOD Influence Zones. This has led design of the TOD neighbourhood to a planning process of remains a challenge for capitalizing neighbourhoods uninformed of the full environment benefits. local resources and capacities where an optimum and sustainable development Need for Localization: The TOD in is not realized to the extent possible. Indian cities needs to be dealt as per local context as these cities are different from the cities in developed Future is Urban – Urban Resilience, Capacity Building, and Nature Bases Solution Editors: Utpal Sharma, Swati Kothary and Vibha Gajjar ©2024 Taylor & Francis Group, ISBN 978-1-032-78443-4 DOI: 10.4324/9781003487890 Study aim, scope, and reduced to a quarter, hence was not considered. limitations Aim of the study Case study Aim: The aim of the study is to develop Justification for Case City Area: a strategy to plan resilient and efficient Delhi TOD at a residential neighbourhood In Delhi, TOD was conceptualized in scale by planning the TOD catchment 2010, and incorporated in Master Plan area based on its carrying capacity. in 2015. The latest modified TOD Scope and limitations Policy and Regulation were notified in December 2019 & March, 2021 Scope: The scope of the research is that respectively. it is based on local level – neighbourhood scale TOD, where After completion of Phase IV of the other higher order scale like city or Mass Rapid Transit System (MRTS), regional level are not considered. The more than 40% of Delhi will be under neighbourhood-level TOD show the TOD Influence Zone (800m predominant land use in the TOD radius). catchment like residential, Delhi’s TOD policy needs to commercial, institutional, industrial, reconsider the following issues: etc., whereas higher-order TOD Node that serves city or regional level of i) TOD policy with a ‘One size fit influence, does not show such land use All’ codes, rigid and restrictive character. The study is focused on the norm which disregards the predominant land use – the residential functional attribute, the scale of the use zone which functions as trip origin. TOD influence area and order of The indicators used in the study are the population/ catchment served; quantitative indicators only. ii) Take contingency of parameters in brownfield areas- existing built-up Limitations: The strategy for primary areas, i.e., 40% unplanned with data collection was made considering 60% of the total population residing the pandemic situation (Dec 2019 to in the unplanned settlements, on the April 2021) and limited to key other hand TOD development in informants/ stakeholders which greenfield areas. Almost all the included transit riders, non-motorized TOD influence zone in the transit (NMT) drivers, real estate brownfield area. agents, residents’ welfare associations, iii) More than 97% is urban population shop owners, etc. (Census 2011) and highly dense Transit ridership data for Delhi for the settlements. Hence, high density as month of September 2019 was used for per TOD policy needs to be analysis (pre-COVID-19). It is considered based on the availability important to understand that post- of supporting infrastructure. Delhi Covid-19, the ridership of transit has the highest population density compared to any State/UT in India Future is Urban – Urban Resilience, Capacity Building, and Nature Bases Solution Editors: Utpal Sharma, Swati Kothary and Vibha Gajjar ©2024 Taylor & Francis Group, ISBN 978-1-032-78443-4 DOI: 10.4324/9781003487890 with 11,320 persons per sq.km. viii) Land under Embassies/Diplomatic (Census 2011). There is a need to Enclaves/Foreign Missions assess how much is too much for ix) Unauthorised colonies articulating the densities in a x) Green Development Areas (GDA) particular TOD. & Low-Density Residential Areas (LDRA) The MRTS in Delhi is operated by DMRC extending to CNCR- Gurgaon, The selection criteria for the TOD Faridabad, Ghaziabad, Bahadurgarh, Nodes for the study are as follows: Noida and Ballabhgarh- More than 333 stations. i) Ridership (average daily ridership) of the selected metro stations to be Areas exempted from TOD in the range of 20,000 to 30,000 ii) Level of influence of the selected As per the Master Plan for Delhi 2041, metro stations to be a the following areas are exempted from neighbourhood level TOD and cannot be developed as a iii) Land use- Residential land use of TOD as per the statutory document: the TOD catchment area selected for Metro Stations within the i) Land under drains, natural water administrative jurisdiction of bodies, notified forest, any other National Capital Territory (NCT) environmentally protected areas. of Delhi as per the Master Plan for ii) Zone ‘O’ and buffers (Yamuna Delhi or respective Zonal River Flood plains) Development Plan of the areas. iii) Villages notified under the Land Pooling Policy Profile of the case study area iv) Monument Prohibited Area v) Civil Lines Bungalow Area (as per As per the selection criteria of the layout plan of North Delhi study, 5 residential TOD catchment Municipal Corporation & DDA) areas were identified with the details as vi) Walled City listed in Table 1. vii) Lutyen’s Bungalow Zone, Chanakyapuri (as per sub-zone D- 13 of Zone-D) Table 1: Profile of the case study areas Case Study Station names Popul Catchment TOD Avg. Area ation Area [Ha] IZ Daily Area Ridership (Ha) Mayur Vihar 1. Mayur Vihar 98709 440 162.5 28514 Phase 1 2. Mayur Vihar Pocket 1 3. Mayur Vihar Ext. Future is Urban – Urban Resilience, Capacity Building, and Nature Bases Solution Editors: Utpal Sharma, Swati Kothary and Vibha Gajjar ©2024 Taylor & Francis Group, ISBN 978-1-032-78443-4 DOI: 10.4324/9781003487890 Dwarka 1. Dwarka Sector 9 52504 270 186.7 21752 2. Dwarka Sector 10 3. Dwarka Sector 11 Munirka 1. Munirka 57963 417 154.2 19149 2. Vashant Vihar Karkardooma 1. Karkardooma 72534 378 147.6 22666 2. Preet Vihar Rajaouri 1. Rajaouri Garden 86756 394 139 30295 Garden 2. Tagore Garden (Source: Author’s compilation) Figure 1. Hierarchy of Metro Stations - typology based on scale and land use Future is Urban – Urban Resilience, Capacity Building, and Nature Bases Solution Editors: Utpal Sharma, Swati Kothary and Vibha Gajjar ©2024 Taylor & Francis Group, ISBN 978-1-032-78443-4 DOI: 10.4324/9781003487890 Figure 2. Location of the case study areas- 5 residential TOD catchment areas Methodology The selected list of indicators are categories into two sets- indicators for Indicator Selection Methodology carrying capacity of TOD (independent variables) and indicators After identifying relevant indicators for efficiency of TOD Nodes for the efficiency of TOD and the (dependent variables). carrying capacity of TOD catchment from the literature review, a Indicators for carrying capacity of comprehensive list of indicators was TOD prepared. The Fuzzy Delphi technique was used for the selection of The 19 independent variables, i.e., indicators from the comprehensive indicators for the carrying capacity of list of indicators. the TOD catchment with respective units of measurement (in brackets) are Two rounds of expert/ stakeholder as follows: opinion were collected as follows: ENVIRONMENT RESOURCE i) First from 284 experts/ i) Land available for development stakeholders from 08.01.2019 and redevelopment (sq.m) to 16.01.2019. ii) Vacant land available for ii) Second from 250 from development (Ha) 15.11.2020 to 01.12.2020. iii) Air Quality Index (Index) Future is Urban – Urban Resilience, Capacity Building, and Nature Bases Solution Editors: Utpal Sharma, Swati Kothary and Vibha Gajjar ©2024 Taylor & Francis Group, ISBN 978-1-032-78443-4 DOI: 10.4324/9781003487890 POPULATION DENSITY xviii) Per capita public open space iv) Net Population Density (PPH) (sq.m.) v) Gross Population Density (PPH) xix) Organized accessible playground (sq.m.) PHYSICAL INFRASTRUCTURE vi) Water Supply- coverage of Indicators for efficiency of TOD tapped water supply in the TOD catchment area (%) The 11 dependent variables, i.e., vii) Water Supply- Per capita per day indicators for the efficiency of the water supply (LPCD) TOD with respective units of viii) Solid Waste Management measurement (in brackets) are as (SWM)- collection and follows: segregation of SWM in the TOD catchment area (%) TRANSIT RIDERSHIP RATE ix) SWM- Geographical coverage of solid waste collection system i) Avg. daily transit ridership in no. (%) (no.) ii) Accessibility to transit- No. of SOCIAL INFRASTRUCTURE transit access points (no.) x) Education facilities: Coverage iii) Interchange to different routes of educational facilities (primary from the same transit node (no.) school) per 10,000 population (no.) LAND USE ENTROPY xi) Healthcare facilities: Number of iv) Area under mixed land use in hospital bed per 10,000 persons TOD Influence Zone (Ha) (min. coverage) (no.) xii) Street vending spaces: Number ACCESSIBILITY FACTOR of street vendors per 1,000 v) Street intersections with population in the catchment controlled pedestrian crossing areas (no.) (12m) ROW (no.) vi) Active frontage adjacent BUILT ENVIRONMENT Sidewalks in m (Avg.) (m) xiii) Built Density/ DU per Ha vii) Percentage coverage of footpaths (Ratio) with 1.8 m to total street length xiv) Share of affordable housing (12m ROW) [Footpath standards (small format houses less than (IRC 103-2012)] (km) 60 sq.m.) to total housing stock viii) Walkability (Ped Shed area) (%) within 500m and 800m radius (%) xv) Share of rental housing in the TOD catchment area (%) LAST MILE CONNECTIVITY xvi) Mean Floor Area Ratio (FAR) of ix) Average time taken to reach the developed plots (Ratio) nearest metro station (in min.) x) Geographical coverage of NMT OPEN SPACES service in the catchment area (%) xvii) Per capita accessible green area (sq.m.) EMPLOYMENT DENSITY Future is Urban – Urban Resilience, Capacity Building, and Nature Bases Solution Editors: Utpal Sharma, Swati Kothary and Vibha Gajjar ©2024 Taylor & Francis Group, ISBN 978-1-032-78443-4 DOI: 10.4324/9781003487890 xi) Areas under job centers of the total The primary and secondary data developed area (Ha) collected for the 5 residential TOD Nodes and catchment areas for the Normalized data for the indicators dependent and independent variables were normalised and are presented in the Table 2 and Table 3 respectively. Table 2. Normalized data of the dependent variables- Efficiency of TOD Accessibility Factor Last Mile Employ Transit ridership rate Connectivity ment Density DEPEND Street Active Covera Walk Walk Avera Geogra Areas Avg. Acces Interch ENT intersec frontag ge of abilit ability ge phical under daily sibilit ange VARIAB tions e footpat y (Ped time covera job transit y to to LES- with adjace hs with (Ped Shed taken ge of centers ridership transi differe EFFICIE controll nt 1.8 m Shed area) to NMT of the in no. t- No. nt NCY OF ed Sidew to total area) 800m reach service total of routes TOD pedestri alks in street withi neare in the develop transi from NODE an m length n st catchm ed area t the crossin (Avg.) (12m 500m metro ent (Ha) acces same g (12 ROW) statio area s transit m) in km n (in (Ha) points node ROW min.) (Numb er) Mayur 7 0.75 4.5 56 41 8 163 19.5 28514 7 1 Vihar Dwarka 19 1.2 18 67 52 8 187 28.0 21752 6 0 Munirka 6 0.85 9.5 60 42 10 123 23.1 19149 6 0 Karkardo 9 1 11 61 44 13 123 31.0 22666 4 1 oma Rajaouri 13 0.75 8 63 41 9 152 21.0 30295 8 0 Garden Source: Author’s analysis Table 3. Normalized data of the dependent variables- Efficiency of TOD Air Land availability Population Built Environment Qual Density ity INDEPENDEN Air Land Vacant Net Gross DU per Afforda Rental Mean T VARIABLE- Quality available land populati Populati Ha ble house Floor CARRYING Index for available on on (DUs in housing to Area CAPACITY (Avera developm for Density Density resident stock total Ratio OF TOD ge ent per developme (PPH) (PPH) ial and (small housin (FAR) CATCHMENT AQI) capita (in nt to total mixed houses g of the sq.m) developabl land of less stock develo e area use) than 60 ped sq.m) plots Index sq.m Ha PPH PPH Ratio No. No. Ratio Mayur Vihar 0.67 0.02 1.0 1.00 0.98 1.00 1.00 0.84 2.50 Dwarka 0.24 0.03 0.6 0.59 0.30 0.65 0.11 0.00 1.85 Munirka 0.00 0.17 0.2 0.77 0.43 0.13 0.27 0.17 2.25 Karkardooma 0.67 0.12 0.1 0.79 0.65 0.63 0.28 0.01 1.90 Rajouri Garden 0.27 0.17 0.0 0.89 0.95 0.53 0.79 1.00 2.60 Physical Infrastructure Social Infrastructure Public Spaces Future is Urban – Urban Resilience, Capacity Building, and Nature Bases Solution Editors: Utpal Sharma, Swati Kothary and Vibha Gajjar ©2024 Taylor & Francis Group, ISBN 978-1-032-78443-4 DOI: 10.4324/9781003487890 INDEPENDEN SWM Geogra Water Water Educati Health- Street Green Per Per T VARIABLE- - phical Supply Supply on- Number Vendor areas in capita capita CARRYING Colle covera - - Per Covera of per total access public CAPACITY OF ction ge of Covera capita ge of hospital 1,000 catchme ible open TOD of solid ge of per day primary bed per populati nt area green space CATCHMENT segra waste tapped LPCD school 10,000 on in area/ in gated collecti water water per persons the parks/ sq.m SWM on supply supply 10,000 catchm playgr system in populati ent ounds catchm on areas in ent sq.m % Ha Ha LPCD No. No. No. Ha sq.m sq.m Mayur Vihar 0.56 0.37 1.00 0.05 0.40 0.00 1.00 0.26 0.08 0.01 Dwarka 0.24 0.00 0.44 0 0.40 0.95 0.24 0.62 1.00 1.00 Munirka 1.00 0.52 0.96 0.07 0.60 0.62 0.13 1.00 0.38 0.05 Karkardooma 0.80 0.28 0.00 0.07 1.00 0.56 0.47 0.37 0.29 0.32 Rajouri Garden 0.51 0.36 0.03 0.06 0.20 0.01 0.55 0.27 0.10 0.00 Table 4. TOD Efficiency of Case Study Areas based on Carrying Capacity of the TOD Catchment Name of Air Land Populati Built Infrastr Public TOD Per TOD Quali availabil on Environ ucture Space Efficienc cent Nodes ty ity Density ment y age Index Mayur 0.67 0.56 0.99 0.95 0.47 0.12 0.56 55.5 Vihar Dwarka 0.24 0.28 0.45 0.25 0.53 0.87 0.55 55.1 Munirka 0.00 0.13 0.60 0.19 0.45 0.48 0.43 42.6 Karkardoo 0.67 0.31 0.72 0.31 0.68 0.33 0.47 46.8 ma Rajouri 0.27 0.15 0.92 0.77 0.25 0.12 0.60 59.7 Garden Multiple regression analysis Population Density (PD) -0.4530 The normalized data was run through Built Environment (BE) 0.6120 multiple regression and the following Infrastructure (I) 0.1753 result as shown in Table 5 was Public Space (PS) 0.1123 obtained. Table 5. Multiple regression results Using the results of the Multiple Regression, the coefficients were Coefficients placed in the standard formula Intercept 0.5201 Y = b1* (x1) + b2*(x2) +… bn* (xn)+ Constant Air Quality Index (AQ) 0.1409 The following equation 1 is obtained Land availability (LA) -0.5199 for the study: Future is Urban – Urban Resilience, Capacity Building, and Nature Bases Solution Editors: Utpal Sharma, Swati Kothary and Vibha Gajjar ©2024 Taylor & Francis Group, ISBN 978-1-032-78443-4 DOI: 10.4324/9781003487890 Efficiency of TOD = 0.14(AQI) - redeveloped as per the prescribed 0.51 (LA) - 0.45 (PD) + 0.61 (BE) + TOD Policy in the Master Plan for 0.17(I) + 0.11 (PS) + 0.52…… (1) Delhi. The efficiency of the TOD is Factor Analysis: Principal determined by the dependent Component Analysis (PCA) variables represented by 6 indicators, namely Air Quality Index, Land The study further adopted Principal Availability, Population Density, Component Analysis (PCA) to Built Environment, Infrastructure understand significant factors which (Physical and Social), and Public contribute to the relationship of the Spaces. efficiency of TOD Node (dependent variable) and the carrying capacity of Key findings of the efficiency of the TOD catchment (independent TOD Node carriable). The value of efficiency of the TOD at Correlation matrix the existing scenario is calculated and presented in Table 4. It is evident that Principal Component Analysis (PCA) the existing settlement are not of the independent variable to developed as per the TOD principles establish which indicator is the and shows that the TOD functions strongest contributor in the inefficiently with all the 5 TOD relationship. Also, if there are Nodes within a range of 40-60% variables that do not contribute to the efficiency. The present case study for relative change in the established assessing the TOD efficiency in the relationship. The closer the value is to existing metro stations developed in a 1 or -1, the variables show a strong city (Delhi) where the settlement was positive or negative correlation. If already in existent can be referred to there is a negative sign, it indicates one of the variants of TOD which is that when one variable increases the usually referred to Development- other decreases, i.e., the variables are oriented Transit (DOT). DOT is a inversely correlated. Here, as per the typology of development where correlation matrix, it shows that transit is built in an existing Population Density and Built development to serve the travel need Environment are strongly corelated in of the populace, here transit follows a positive manner and Public Space development rather than vice-versa and Population Density are strongly (Dittmar H. &., 2012). The same TOD corelated negatively. The results of Nodes may be considered for further the Correlation matrix are represented studies in future, when these areas are in Table 6. Table 6. Correlation matrix Correlation matrix (Pearson (n)) Variables Air Land Populati Built Infrastru Public Quality Availabilit on Environment cture Spaces y Density Future is Urban – Urban Resilience, Capacity Building, and Nature Bases Solution Editors: Utpal Sharma, Swati Kothary and Vibha Gajjar ©2024 Taylor & Francis Group, ISBN 978-1-032-78443-4 DOI: 10.4324/9781003487890 Air Quality 1 0.799 0.539 0.490 0.456 -0.460 Land 0.799 1 0.430 0.544 0.359 -0.254 Availability Population 0.539 0.430 1 0.914 -0.426 -0.959 Density Built 0.490 0.544 0.914 1 -0.519 -0.765 Environment Infrastructure 0.456 0.359 -0.426 -0.519 1 0.382 Public Spaces -0.460 -0.254 -0.959 -0.765 0.382 1 Values in bold are different from 0 with a significance level alpha=0.05 Eigenvalues 31.8% of the total variation. The eigenvalues in the PCA shows that the It is observed that factorial axes F1 two axes F1 and F2 together captures explained 59.02% of the total 90.84% of the variation for the biplot. variation. Factorial F2 explained Table7. Eigenvalues Eigenvalues F1 F2 F3 F4 Eigenvalue 3.541 1.909 0.456 0.093 Variability (%) 59.025 31.820 7.602 1.553 Cumulative % 59.025 90.845 98.447 100.000 3. Biplot for 5 Residential land In the correlation circle, the use case studies correlation of the 6 variables is represented. It is observed in the figure that the most influential Variables (axes F1 and F2: 90.84 %) variables appear close to the circle and 1 the component which does not 0.75 Infrastru Land availability Air influence appears closer to the centre cture 0.5 Quality of the circle. Public 0.25 Space All the 6 variables are influential as Population 0 Density indicated in the biplot. There are F1 (59.02 %) clearly three group of components in F2 (31.82 %) -0.25 Built each axis, one component which Environment -0.5 consist of – Air Quality and Land -0.75 availability. The other component consists of- Population Density, and -1 -1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1 Built Environment. The third component consists of Infrastructure and Public Space. Future is Urban – Urban Resilience, Capacity Building, and Nature Bases Solution Editors: Utpal Sharma, Swati Kothary and Vibha Gajjar ©2024 Taylor & Francis Group, ISBN 978-1-032-78443-4 DOI: 10.4324/9781003487890 Contribution of the variables (%) F1 F2 Sum % Air Quality 13.6 23.8 37.4 18.7 Land availability 11.3 22.8 34.1 17.0 Population Density 26.5 2.4 28.8 14.4 Built Environment 24.8 2.3 27.1 13.5 Infrastructure 2.1 45.1 47.2 23.6 Public Space 21.7 3.7 25.4 12.7 The two variables, namely, as a trip origin node (residential land population density and built use character) shows that efficiency of environment contribute higher in F1 the TOD is dependent on the carrying axes. 3 of the variables, namely, Air capacity of the TOD catchment area. quality, land availability and The carrying capacity of the TOD is infrastructure contribute higher in F2 determined by the maximum axes. The most significant contributor population that the catchment area can for residential land use is accommodate without major adverse infrastructure, indicating the most impact on the environment resource significant factor contributing to and available infrastructure determine the efficiency of TOD supporting the built up and population catchment in a residential character in the catchment area. neighbourhood is infrastructure. The study shows that the efficiency of It can be concluded that the TOD is dependent on the identified contribution of all identified six six variable of the carrying capacity of variables is significant factors in the the TOD catchment, namely, air Efficiency of TOD Node, listed as quality index, population density, follows: built density, infrastructure (physical- water supply and solid waste i) Air Quality Index management; social- education, ii) Land availability healthcare facilities and street iii) Population Density vending spaces) and public spaces. iv) Built Environment v) Infrastructure The factor analysis undertaken using vi) Public Space. Principal Component Analysis (PCA) shows that all the six variables of the Results carrying capacity of the TOD catchment are significant contributor The aim of the study was to develop a to efficiency of the TOD Node. strategy for efficient and resilient However, land availability and public planning of the TOD at spaces availability has relatively less neighbourhood scale which function Future is Urban – Urban Resilience, Capacity Building, and Nature Bases Solution Editors: Utpal Sharma, Swati Kothary and Vibha Gajjar ©2024 Taylor & Francis Group, ISBN 978-1-032-78443-4 DOI: 10.4324/9781003487890 contribution compared to the other that TOD is associated with better air four variables- Air Quality Index, quality (Peiqin, 2019). TOD helps Population Density, Built Density, improve air quality amongst other benefits (Dittmar & Ohland, 2012), and Infrastructure. The most (Cervero R. , 2004). Another study significant contributor to efficiency of revealed that land market and the efficiency of the TOD Node is availability of land for TOD are infrastructure. contributors for the success of TOD (Cervero & Dai, 2014). It is also Discussion important that population density and activities are concentrated in TOD TOD has been promulgated as a long- areas for its success. Australian TOD term planning approach to achieve in Brisbane showed a concentration of climate resilience and sustainable people and dwelling in TOD areas development. Due to the TOD (Yang & Pojani, 2017). There is a principles of compact development, it need to enhance and upgrade the helps preserve land for open spaces infrastructure amenities in the TOD and other environmentally sensitive areas, to create a positive loop strategies or planning. Green TOD between the development and the through sustainable building, energy urban growth (Harrison, Rubin, and waste practices can reduce carbon Appelbaum, & & Dittgen, 2019). emissions and energy consumption Based on Public spaces in TOD study nearly by 30% compared to in Tokyo, it was found that high conventional development (Ito, vegetation and maintaining a 2022). On a wider perspective, TOD microclimate adds to sustainable is instrumental in increase revenue of improvement in the urban structure the transit providing agency, especially in TOD areas revitalize neighbourhoods, reduce (Mukhamedjanov, Kidokoro, Seta, & sprawls, land conservation, & Yang, 2021). TOD has also been congestion relief, reduce outlays for able to improve public spaces through roads and road expenditure, improve innovative financing and provide high safety of pedestrians and cyclists, quality public and green areas to increase land values, increase enhance the users experience affordable housing options, retail (Teklemariam & & Shen, 2020). sales, environment benefits reduce parking; also economic benefits in TOD has been implemented terms of time saved in travelling, worldwide and the outcomes were infrastructure layouts, access to diverse, revealing that projects labour pools, etc. (Cervero R. , 2004) depend on a variety of factors, trends, The mass transit system encourages and complex interrelations between public mode and shift from private them (Ibraeva & et al.). There is a mode, leading to reduce CO2 need to localize the planning of TOD emission. based on the context of Indian cities which are spatially, contextually, and A study on TOD and its impact on air functionally heterogenous from the quality in 37 cities in China showed cities in developed nations where TOD concept was first Future is Urban – Urban Resilience, Capacity Building, and Nature Bases Solution Editors: Utpal Sharma, Swati Kothary and Vibha Gajjar ©2024 Taylor & Francis Group, ISBN 978-1-032-78443-4 DOI: 10.4324/9781003487890 conceptualised. A study based in MRTS has been operational in the last China, highlights the due to the 2 decades. The scope of the study was presence of a substantial differences based on a lower order hierarchy of between the U.S. and China, in terms TOD Node (neighbourhood level) and of population density, land use TOD Nodes functioning as residential intensity, personal income level, character (trip origin). While planning urban spatial structure, and propensity for such residential neighbourhood to use public transport/ transit, it level TOD Node, planning would not be appropriate to utilise the consideration should include U.S. based TOD planning parameters following parameters for improving and Chaina needs to develop its own the efficiency of the TOD Node TOD parameters based on local within the carrying capacity: circumstances. i) Population Density – articulated Indian cities have high density, mixed residential densities in the use of activities, organic development catchment area to generate pattern and high share of pedestrian desired ridership. and cycle as a mode share in ii) Built Environment status – movement pattern. There is lack of Higher FAR, existence of large supporting infrastructure due to amount of affordable housing resource crunch and financial with tenure diversity (rental capacities of the service providing housing) as part of residential agencies and development authorities. development. In this background, and looking iii) Air Quality Index– Air quality forward to the prospects of MRTS in of the neighbourhood of the the Indian cities covering almost 27 TOD Catchment area. cities to be developed in the next iv) Availability of Infrastructure – decade, it is extremely important that Adequate physical and social sustainable approaches are adopted infrastructure provision as per while planning TODs. Design of the population within the TOD TODs at neighbourhoods are catchment. exceptional and will depend on the v) Public Space – More availability local context, which becomes a of accessible and adequate challenge to optimise the public spaces in terms of green environmental benefits and plan areas, parks and playgrounds, within the carrying capacity of the public spaces like plaza and TOD catchment. community center vi) Land available for development- Conclusion Amount of land/ vacant land for development in the TOD The study concludes with policy catchment. recommendation as derived from the research. The case is specific for a TOD planning for a neighbourhood metropolitan city with dense MRTS scale taking into consideration of the network in operation. 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