IMPLICATION OF SMART GROWTH STRATEGIES IN RESIDENTIALNEIGHBOURHOODS ON SUSTAINABLE MOBILITY - CASE

STUDY DELHI

Sandhya DameniyaDr. Sanjay Gupta

School of Planning and Architecture, New Delhi

• Introduction• Smart Growth Principles and Practices• Data base• Case neighbourhood Profile• Built environment characteristics of case neighbourhood• Mobility characteristics in case neighbourhood• Impact of built environment on mobility patterns• Built Environment Sustainability Model and its application• Summing up

Presentation Structure

Introduction

Increase in UrbanPopulation Sharefrom 27.81% in2001 to 32% in

2011

Increase in UrbanPopulation Sharefrom 27.81% in2001 to 32% in

2011

Increasednumber of

million pluscities from 23 in

1991 to 35 in2011

Increasednumber of

million pluscities from 23 in

1991 to 35 in2011

Vehicularownership

increased from 21million in 1991 to

142 million in 2011

Vehicularownership

increased from 21million in 1991 to

142 million in 2011

32% vehicle playing inmetropolitan cities

which constitutes in15% of the total

population

32% vehicle playing inmetropolitan cities

which constitutes in15% of the total

population

PCTR range 1.2 to1.57 and ATL ranges12.7 to 13.5 km. inmetropolitan cities

PCTR range 1.2 to1.57 and ATL ranges12.7 to 13.5 km. inmetropolitan cities

TransportContributes 15% share of GHG

emission inIndia

TransportContributes 15% share of GHG

emission inIndia

Existing Urbanization and Mobility patterns In India

Issues of low density and single use zoningUrban sprawlInfrastructure deficienciesWeak connectivityAutomobiles dependencyProductive land obstructionHigher cost of transport InfrastructureHigher fatalities , accidents and air pollutionExtensive fossil fuel

Concept of Smart Growth It is a policy framework which initiates an urban

development pattern in combination of highpopulation density, walkable and bicyclefriendly neighborhoods, preserved green areas,mixed-use development, accessible mass transit,and limited road construction.

Concept of Smart Growth It is a policy framework which initiates an urban

development pattern in combination of highpopulation density, walkable and bicyclefriendly neighborhoods, preserved green areas,mixed-use development, accessible mass transit,and limited road construction.

Source: Census of India 2011, Statistical Year Book India 2013, Road Transport Year Books-2007 and 2009, July 2011-Journal of Economic and Social Studies by R. DayalSharma-S. Jain-K. Singh, S. Padam and S. K. Singh- URBANIZATION AND URBAN TRANSPORT IN INDIA: THE SKETCH FOR A POLICY., Willbur Smith Report 2008.

Research NeedSprawling urban areas are resulting in increasing dependency towards vehicular mode usage andconsequently increases GHG emissionNeed to restructure the urban areas to evolve growth patterns which will deterrent the environmentquality and leads to contribute towards sustainable mobility patternsIn India there is still insufficient research in smart growth area which can lead to more informed planningpolicies resulting in sustainable development patterns

Smart Growth

EnhanceQuality of Life

Promoteeconomic

Development

Createliveable

community

Promotealternative

transportation

of housing

Preserve

natural

Smart Growth Principles and Practices

SelectedCase Study

Adopted Tool ofsmart Growth Achievements

BowdenVillage,AdelaideAustralia

• Mixed usedevelopment

• TOD/TAD• Densification

• Increase in accessibility• 70 – 80 % of reduction in GHG

emission

Newsteadand Teneriffe,BrisbaneAustralia

• Strengthentransportsystem

• Mix land use• Incorporating

Open spaces

• 3 time increase in jobs• Low Level of car Ownerships• Increase share of PT by 50%• Increase in walking and Cycling

share by 280%

Subi-CentroAustralia

• Transit Orienteddevelopment

• Mix landusedevelopment

• More tan 80% increase in Jobs• 200% increase in Public transport• 10% Shift towards Pedestrian

and Cycling from car

HammarbySjöstad,Stockholm,Sweden

• Redevelopmentof area

• Mix usedevelopment

• Transit orientedDevelopment

• Improved share of pubictransport , pedestrian and Cyclejourney by 80%

• Low car ownership and modeshare

• More then 50% Improvement inCO2 Emissions rate by Cars

Smart Growth Principles by U.S.EPASmart Growth Principles by U.S.EPA

Collaborating on Solutions

Mix Landuse

Compact Development

Transportation Options

Housing Options

Infill Development and Redevelopment

Preservation of open spaces

Incentives for Smart Development

Promote citizen participation

Best Practices Advocates compact transit oriented,

walkable, bicycle friendly land use,

including neighborhood schooling,

complete streets, and mixed-use

development with a housing choices.

Smart Growth Concept

Source: U.S. Environmental Protection Agency (2015), Getting to Smart Growth II: 100 policies for implementation, (2013). Smart Growth. (2015), About Smart Growth.Smart Growth: Unlocking Smart Growth in Australia’s Capital Cities 2014. SMART GROWTH Principles & Practices Selected Case Studies- CRP 410-01 Community PlanningLab City & Regional Planning Department California Polytechnic State University, SLO

Research Objectives and MethodologicalStages

• To appreciate the concept of smart growth and its role in achieving sustainable development• To review global best practices on smart growth development and its impact on travel

patterns• To assess built environment and mobility in case study residential neighbourhoods in Delhi• To assess the relationship between compact built environment and GHG emissions in case

study• To evolve policy guidelines for smart growth strategies at city and neighbourhood level

Research Objectives

Stage 1: Literature study on smart growthconcept, principles, indicators and bestpractices.Stage 2: Data identification and collectionStage 3: Assessment of Built-mobility Characteristics.

on mobility

environment and sustainability model.

Methodologicalstages

Stage 1: Literature study on smart growth concept, principles, indicators and bestpractices.

Stage 2: Data identification and collection

Stage 3: Assessment of Built-environment and mobility Characteristics.

Stage 4: Assessing impact of built environment on mobility

Stage 5: Development and application of Built-environment and sustainabilitymodel.

Area: 1,483 sq. km. Population: 16.76 lakhs. Population density: 11,297 persons / sq. km. Urban Population: 93% Total road length: 22,487 km Road density of 1284 km/ 100 sq.km. Total registered motor vehicles ( 2011-12): 74,38,155 (in which

62.43% are of motor vehicles/scooters, 31.50% are of car & jeeps andrest are auto rickshaw, taxis, buses and goods vehicles.)

Total number of DTC buses (2011-12): 5884 (in which 2506-low floorNon AC and 1275-Low Floor AC and 2103 are Standard buses.

Area: 1,483 sq. km. Population: 16.76 lakhs. Population density: 11,297 persons / sq. km. Urban Population: 93% Total road length: 22,487 km Road density of 1284 km/ 100 sq.km. Total registered motor vehicles ( 2011-12): 74,38,155 (in which

62.43% are of motor vehicles/scooters, 31.50% are of car & jeeps andrest are auto rickshaw, taxis, buses and goods vehicles.)

Total number of DTC buses (2011-12): 5884 (in which 2506-low floorNon AC and 1275-Low Floor AC and 2103 are Standard buses.

Brief Profile of Delhi

Source: Census of India 2011, Delhi Development Authority 2011. CDP Delhi, (2006), Dept. of Urban Development, Govt. of Delhi. StatisticalAbstract of Delhi, (2012) Govt. of National Capital Territory of Delhi. www.delhionline.in.

Profile of Delhi2000-2014

Urban Sprawl Delhi

Increased PCTR (excludingWalk Trips) from 0.72 in 1981to 0.87 in 2001

Increased PCTR (excludingWalk Trips) from 0.72 in 1981to 0.87 in 2001

Increase in vehicular population from 5.13lakhs in 1981 to 32.38 lakhs in 2001 andreached to 74.38 lakhs in 2011.

Increase in vehicular population from 5.13lakhs in 1981 to 32.38 lakhs in 2001 andreached to 74.38 lakhs in 2011.

Average Triplength 10.5km.

Average Triplength 10.5km.

Data Base

Primary Data Collection

Household Survey Network Inventory Layout Study

• H.H. Information• Personal

Information• Trip Information

• H.H. Information• Personal

Information• Trip Information

• ROW• Road Length• foot path length• Cycle path length• Intersection density

• ROW• Road Length• foot path length• Cycle path length• Intersection density

• Landuse Mix• Distances Between

Houses and PublicFacilities and transitStation/ Bus stop

• Landuse Mix• Distances Between

Houses and PublicFacilities and transitStation/ Bus stop

* The selection criterion for the case neighbourhood sites was based ondensity and connectivity to the public transport i.e., Metro and Bus

Case study sectors-Dwarka

Brief description located in South-West DelhiPopulation -10 lakhsTotal area: 5648 ha.Physical featuresComprises 29 planned sectorsDesigned for Population 30, 000Each sector is about 81 ha. (900m x

900m)Bounded by 45m to 60m roadsPlanned with 9 metro stations and also

availability of bus based public transport

GrossResidential

49%

Commercial7%

Govt.1%

PSP6%

Recreational20%

Transport14%

Utilities3%

LanduseDwarkaSectors

No. ofPopulation covered

No. ofsample

Collected

Sector 4 67 16

Sector 6 60 17

Sector 18 86 25

Sector 9 57 15

Sector 10 58 17

Sector 22 60 16

Netaji Nagar 280 80

Sampling Size:

Source: Master Plan Delhi 2021

Case Neighbourhood Profile

Parameters NN DS- 4 DS- 6 DS- 18 DS- 9 DS- 10 DS- 22Area (ha) 44 62 86 69 46 84 64

Population 12180 28912 34881 28875 24845 36881 29513

HH Size 4 4 4 3 4 3 3No. of DU 2900 6939 9767 8370 6538 10965 9018

Density (ppha) 276 464 405 418 542 439 460DU density(du/ha) 65 111 113 121 143 130 140

VehicularOwnership/HH

0.8 1.5 1.8 1.5 2.0 1.8 1.6

Expenses on travel(Rs/day)

20 20 25 27 18 22 30

Salient Features of case study neighborhood of Dwarka

*NN- Netaji Nagar*DS- Dwarka Sector

Smart Growth and Built Environment Measures

Indicators MeasuresDensity(Compactness)

Population Density= Persons/ Ha.House (D.U.) Density= No. of Houses / ha.

Entropy(Mixeddevelopment)

Mix Landuse Development –% share of residential% share of PSP% share of Commercial

ConnectivityAverage Distance to nearest transit stations/ busstops

CenterednessAverage Distance between residences to PublicFacilities (e.g. schools, health facilities etc.)

Walking andCyclingcommunities

Footpath compactness = % of road length withfoot path/ total road lengthCycle path compactness = % of road length withCycle path/ total road length

Street Design

Road LengthRoad DensityIntersection DensityAvailability of traffic calming measures(Pedestrian Crossings, Speed Tables, speedbreakers, Traffic Diversion Islands) on a stretch ofroad (< 500 meters)

Smart Growth Indicators

Source: M. Moeinaddini, Z.Asadi-Shekari, M Zairy Shah – The Relationship Between Urban Structure and Travel Behavior Challengesand Practices; Curran, Grant and Wood – Journal of Rural and Community Development 2 (2006)

Quantification of Measures

3. Centrality- a measure of proximityCentrality to n Facility = Average Distanceto facility ‘n‘{∑ (Weighted Average Shortest distance tofacility ‘n’ from Each Clusters withinNeighbourhood)}n = Type of facility

3. Centrality- a measure of proximityCentrality to n Facility = Average Distanceto facility ‘n‘{∑ (Weighted Average Shortest distance tofacility ‘n’ from Each Clusters withinNeighbourhood)}n = Type of facility

1. Dwelling unit density- a measure ofcompactness1. Dwelling unit density- a measure ofcompactness

2. Entropy- a measure of mix land use

Entropy= {-∑k [(Pi)(lnPi)}/ (ln k)Pi – Proportion of each land use from theTotal ValueK – Total no. of land use clustersValues varies- 0 to 1:0 – Maximum Specialization1 – Maximum diversity

2. Entropy- a measure of mix land use

Entropy= {-∑k [(Pi)(lnPi)}/ (ln k)Pi – Proportion of each land use from theTotal ValueK – Total no. of land use clustersValues varies- 0 to 1:0 – Maximum Specialization1 – Maximum diversity

Indicators Measures NN DS- 4 DS- 6 DS- 18 DS- 9 DS- 10 DS- 22Density(Compactness)

Population Density 277 464 405 418 542 439 460House (D.U.) Density 66 111 113 121 143 130 140

Entropy Entropy 0.36 0.76 0.83 0.72 0.82 0.85 0.82

Centeredness(AverageDistance) (inmeters)

Schools 640 593 524 544 469 433 386Retail 486 586 458 586 451 453 374Health 450 630 563 785 481Bus Stop 665 625 630 603 529 753 552Park 260 628 583 595 407 583 494

Walking andCyclingcommunities

% of road length withfoot path

33% 23% 29% 20% 35% 26% 35%

% of road length withCycle path

- - - - - - -

Street DesignRoad Length (km.) 14.8 4.5 5.7 6.25 6 7.3 4.9Road Density(km/sq.km.)

33 7.3 6.6 9.1 6.4 6.4 5.8

Built Environment Characteristics of CaseNeighbourhoods

*NN- Netaji Nagar*DS- Dwarka Sector

Intra Neighbourhood Mobility Pattern in Netaji NagarSub- Zone type 1 2 3 4 5Area (in Ha). 12 15 3 4 8Population 5516 4428 672 528 496HH Size 4 4 4 4 4No. of DU. 1379 1107 168 132 124Population Density(ppha) 456 295 210 132 62

DU density(Du/ha) 114 74 53 33 16Vehicular Ownership (veh/hh) 0.6 0.7 0.9 1.1 1.0

Netajinagar Sub-zones

Trip by mode in Netaji Nagar

Trip by purpose in Netaji Nagar

Average trip rate per capita

Average trip length in km. in NetajiNagar

Trip length by NMT and vehicularModes

GHG emission per capita

• NMT is higher in the sub-zone 1,• Vehicular trips are higher in subzone 5

• Educational trips are higher in sub zone 1,• Shopping is higher in sub zone 5 and• Recreational trips highest in sub zone 3

• NMT trip rate is higher in sub zone 1• Vehicular trip rate in sub zone 5

• Average trip length for NMT is highest insub zone 2

• NMT per capita km. travel is higher in sub zone 1&2• vehicular km. travel is higher sub zone 5

Mobility Characteristics in Case Study AreasIntra neighbourhood Mobility Characteristics across Dwarka sectorsTrips by mode in Dwarka Trips by purpose in Dwarka

Per capita trip rate in Dwarka Average trip length in Dwarka

Trip km. per capital by NMTand vehicular modes in Dwarka

GHG emission per capita inDwarka

Method of GHG emission calculation:

Total Emission = Ai x Bi x CiAi = Total Population x PCTR X % Trips ModeWhere,Ai = Trips by ModeBi = ATL by ModeCi = Emission Rate by Modei = Type of Modes

Emission Factors for Carbon Monoxide (gm./km)

S.No.

VehicleType

FuelType

FuelProporti

on

ARAI (2008) in gm/ km

CO HC Nox Co2 PM

1Two

Wheeler

2-Stroke 40% 0.16 0.86 0.02 38.6 0.057

4-Stroke 60% 0.72 0.52 0.15 45.60 0.013

2Four

Wheeler

Petrol 60% 3.01 0.19 0.21 142.86 0.006

Diesel 38% 0.06 0.08 0.28 148.26 0.015

CNG 2% 0.06 0.36 0.01 131.9 0.002

3 Bus CNG 100% 3.72 3.75 6.21 806.3 0.044

4 Auto CNG 100% 1 0.26 0.50 77.70 0.015

• Share of trips by walk is higher in DS 10 andDS 22 respectively

• Car trips are highest in DS 22

• Educational trips are higher in DS 10 ,Shopping and social trips highest in DS22

• PCTR by NMT modes are higher in DS 10• PCTR by vehicular modes is higher in sector 6

• ATL by NMT modes and vehicular mode ishigher in DS 18

• Shortest trip length is observed in DS 10

Sources: N. Sharma, Performance evaluation of CALINE 4 dispersionModel for an urban highway corridor in Delhi 2013; CPCB/MOEF- DraftReport on Emission Factor Development for Indian Vehicles (2008)

• The NMT km. travel rate is higher in DS 9 while vehicular km. travel is higher in sector 4

Impact of Built Environment Indicators onMobility Across Neighborhood Dwarka Sectors

• Higher DU density leads to lowtrip lengths for social trips

• Higher DU Density leads tohigher PCTR for intraneighborhood travel

• Higher Entropy index (higherLand use mix ) leads to lowertrip lengths

• Higher centrality index leads tohigher trip lengths

• Higher Entropy index (higherLand use mix ) leads to higherPCTR within neighbourhood

• Low centrality index ( low distanceto amenities) leads to higher PCTRwithin neighbourhood

• Higher Entropy index leads tomore NMT km. travel

Impact of Built Environment Indicators onMobility Across Neighborhood Dwarka Sectors

• Higher DU Density leadsincreased NMT km. travel

• Higher DU Density leads to willless GHG emission per capita

• Higher Entropy index leads toless GHG emission per Capita

• Low centrality index leads tomore NMT km. travel

• Low centrality index leads toless GHG emission per capita

Built Environment Sustainability Model

Derived through the relationship between smartgrowth indicators and sustainable mobilitythresholds (the shift from motorized modes tonon- motorized modes)

• To estimate existing VKT and GHG emission rates through existing built- environment structure• Can produce the required combination of DU density, entropy and centeredness to achieve the

desired VKT and GHG emission

Vehicular km. Travel per capita within theneighborhood as a link between adopted builtenvironment indicators as input parameters andGHG emission per capita as output

To achieve the desired GHG emission rates (asInput) from different combination of smartgrowth measures such as density, entropy andcenteredness (as Out put)

Further desired GHG emission rates (Input) todrive the achieved NMT km. per capita (Out put).

ModelAim

ModelUtility

BUILT

EN

VIRO

NM

ENT

SUST

AIN

ABLE

MO

BILI

TY

• To understand the relation between smart growth indicators and sustainable mobility• Drived through cross-sectional analysis of residential neighborhoods by using mathematical

relationship between smart growth measures and sustainable mobility indicators.

Built Environment Sustainability Model

DwarkaSectors

VKT km. percapita

GHG Emissionper capita

NMT km percapita

Mod

elle

dVa

lues 4 0.15 5.99 0.16

6 0.12 5.25 0.17

18 0.14 5.92 0.16

9 0.09 4.17 0.19

10 0.1 4.31 0.19

22 0.09 4.12 0.19

Obs

erve

d Va

lues

4 0.16 6.6 0.16

6 0.12 5.21 0.16

18 0.15 5.84 0.16

9 0.09 3.85 0.22

10 0.08 3.43 0.19

22 0.09 4.64 0.18

% C

hang

e

4 -7.50% -9.20% 1.30%

6 2.50% 0.80% 5.30%

18 -3.90% 1.40% 4.20%

9 -0.60% 8.40% -10.90%

10 25.40% 25.40% -2.40%

22 0.60% -11.30% 4.60%

Model Calibration

• Only Dwarka sector 10 (25%) while othersectors are show variation below 10%..

• The BESM Model has been calibrated forthe existing case study data base.

Model validation• For the Model Validation Selection of sites should be a neighborhood with

homogeneous characteristics to the case study neighborhood sites• Model Validation Conducted on two new Dwarka Sites

Input Parameters:DU Density- 122Entropy- 0.88Centrality:• Educational- 440

• Commercial-580• Health: 560• Parks: 440• Bus Stop: 580

DS 23Modelled values

observedDU Entropy Centrality

Base Year 122.0 0.82 550.0VKT km. per

capita 0.12 0.10 0.10 0.17

GHG percapita 5.26 4.55 4.48 3.45

NMT 0.17 0.19 0.19 0.55

Input Parameters:DU Density- 64Entropy- 0.78Centrality:• Educational- 920

• Commercial-780• Health: 1140• Parks: 790• Bus Stop: 1380

DS 23Modelled values

observedDU Entropy Centrality

Base Year 64.0 0.78 890VKT km.

per capita 0.23 0.13 0.29 0.35

GHG percapita 9.13 5.32 10.94 8.87

NMT 0.11 0.17 0.08 0.10

Variation not exceeding more than 30% except NMT

Variation not exceeding more than 30% except NMT

Application of BESM modelon a Site Proposed for Redevelopment

Basic DetailsArea: 44 ha.Population:Existing: 12800No. of DU:Existing: 2900Population Density:Existing: 290 PPH

Input Parameters:DU Density-66 DU/ha.Entropy: 0.36Centrality: 550

• Retail- 538• School- 522• Parks- 412• Bus Stop- 556

DU Entropy Centrality AverageBaseYear 66 0.36 550

VKT 0.23 0.36 0.13 0.24GHG 8.99 13.39 5.33 9.26NMT 0.11 0.03 0.17 0.10

Inferences:• Netaji Nagar is low rise low density

development• Lower entropy – less diverse development• Centrality is high- facilities are unevenly

and longer distanced located• Modeled values are not matching to each

other- uneven combination of buildenvironment indicators

Base Year Situation: Scenario 1- Proposed RedevelopmentScenario 1- Proposed Redevelopment Scenario 2- Desired DiversityScenario 2- Desired Diversity

Assumptions:1.No change in Landuse

Distribution2.No change in location of

Facilities (Centrality)3.Density will be increase as

per NBCC Proposal in type2 to type 5 housing only

Input Parameters:DU Density-175DU/ha.Entropy: 0.36Centrality: 550

• Retail- 538• School- 522• Parks- 412• Bus Stop- 556

DU Entropy Centrality AverageScenario 1 175 0.36 550

VKT 0.02 0.36 0.13 0.17GHG 1.72 13.39 5.33 6.81NMT 0.23 0.03 0.17 0.14

29% VKT 26% GHGEmission 40% NMT km.

per capita

Assumptions:1.Change in Landuse

Distribution2.Change in location of

Facilities (Centrality)3.Density will be increases

homogeneously to allhousing typology

Input Parameters:DU Density- 175DU/ha.Entropy: 0.9Centrality:350

• Retail- 350• School- 350• Parks- 350• Bus Stop- 370

DU Entropy Centrality AverageScenario 2 175 0.9 355

VKT 0.02 0.06 0.03 0.03GHG 1.72 3.01 1.91 2.21NMT 0.23 0.21 0.23 0.22

87 % VKT 76% GHGEmission 120% NMT

km. per capita

• Long trip lengths and high motorized mode usage are caused by the absence ofdesired level of smart growth development at residential neighborhood level

• The study reveals that compact development reflected by high DU density andhigh Entropy index and low centrality index results in low trip lengths and highNMT mode usage

• BSEM approach demonstrated in the study could provide alternate builtenvironment residential neighbourhood structures which could lead to reducedtransport dependent GHG emissions .

Conclusion

Recommendations• There is an impending need to evolve sustainable mobility environment in

residential neighborhoods of urban areas by adopting smart growth strategies.- promote high dense residential areas ,

- Promoting smaller but high rise block development for walking- Higher land use mix- Proximity to facilities

• Need to use built environment measures in neighbourhood planning