Mobility energy use for different residential urban patterns in India

Anil Kashyap, Jim Berry, Stanley McGreal,

School of the Built Environment

Aim of the research

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To consider relationships between, transport, spatial design of the built environment, energy use in mobility, and sustainability outcomes.

To use a case study approach to a middle tier city in India To assess planned (i.e. structured) versus unplanned (i.e.

organic, unstructured) residential neighbourhoods in terms of energy use and mobility patterns

To show whether better planning and design can contribute to a more energy efficient and environmentally sustainable urban form.

Literature review

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• Mobility is essential part of society• Relationships exist between residential density, household

vehicle use, and household vehicle fuel use1

• Rapid increase in motorised mobility due to the increase in2

household income, commercial and industrial activity, availability of motorised transport and improvement in road infrastructure

• Increase in mobility has energy use implications

1 Brownstone (2009), 2Singh (2006),

Literature review

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• Residents living in suburban neighborhoods drive more and walk less than their counterparts in traditional neighborhoods3 .

• Individuals choose the number of trips by each mode to maximize their utility4

• Residential neighborhood characteristics may be a good predictor for non-motorized travel3

• Built environment influences individuals’ travel behavior 3.• Increasing residential density impacts on household vehicle

holdings and vehicle fuel usage5

3Cao et al. (2009), 4Crane et al. (1996, 2001), 5Fang (2009),

Methodology

• Case study selection– Growing city in National Capital Region of Delhi– Spatially distinct , socio-economic homogeneous neighbourhoods

• Questionnaire survey to identify– Major activity nodes, Trip distances, no of trips, mode of travel– 225 households (22.5%)

• Calculation of energy use– Based on Specific energy consumption values for different modes

(Stead, 2001)– 1.96 MJ/passenger-km travelled by motorised modes– lower and upper range of frequency calculated using the 95%

confidence interval around the mean value

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Case study location

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• Located in fastest growing National Capital Region Delhi

• Administrative Centre at district level• Population of 250,000 (Census 2001)• Growing at rapid pace with new residential and

industrial development in the periphery

• Situated on major regional road and rail network

• Government focus on developing as self contained city to ease population pressure on Delhi

Urban form – unplanned element

Prior to planning intervention, town grew organically around the central core or business spine or artery

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Organic/Traditional urban structure of NHs

Central Core

Road Networks

New Development

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Urban form – planned element

The developments are mainly in the form of green field development, in the outskirts of existing towns

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Central Core

Road Networks

Planned NH - Grid- Iron pattern

Facilities

Building blocks

New Development

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Mobility pattern and energy use

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Activity nodes Trip distance (Km) Mode share (%) Trip frequency (n)

Car/ Jeep Motor bike/Scooter

Car/ Jeep

Motor bike/Scooter

Car/ Jeep Motor bike/Scooter

Local shopping 3.10 2.81 37.90 47.00 10.81 11.09Other shopping 6.55 6.44 41.40 46.70 5.19 5.76Primary education 4.25 5.15 6.50 13.10 24.00 24.00Higher education 0.00 8.09 4.30 47.80 0.00 24.55Medical facilities 3.94 4.15 48.20 42.20 1.80 2.15Work 10.96 9.69 45.60 43.50 23.66 24.22Professional services 3.93 3.58 34.60 50.90 3.76 3.44Religious/comm. facilities 4.04 6.16 23.00 29.10 11.76 13.19Social & leisure 12.25 9.26 25.00 16.90 3.32 9.04Entertainment 122.40 0.00 80.60 3.20 1.24 0.00

Activity nodes Trip distance (Km) Mode share (%) Trip frequency (n)Car/Jeep

Motorbike/ Scooter

Car/Jeep

Motorbike/ Scooter

Car/Jeep

Motorbike/ Scooter

Local shopping 1.50 2.54 3.65 48.86 5.0 6.0Other shopping 0.00 2.58 0.00 44.44 0.0 4.3Primary education 0.00 6.00 0.00 5.10 0.0 24.1Higher education 0.00 7.80 0.00 27.78 0.0 23.7Medical facilities 4.55 3.90 5.00 46.58 1.7 2.2Work 7.00 6.44 8.21 46.27 24.4 23.6Professional services 2.67 2.54 4.02 45.54 2.0 3.0Religious/comm. facilities 2.25 3.10 2.02 16.16 10.5 10.8Social & leisure 13.28 3.63 12.85 20.00 2.8 2.7Entertainment 110.83 4.57 25.53 14.90 1.8 1.7

Mobility pattern – Trip distance

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Mobility pattern – Trip distance

13Outside Neighbourhood tripsOutside Neighbourhood trips

Mobility pattern – Mode

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Mobility pattern – Mode

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Comparative analysis

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Comparative analysis

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Activities Unplanned Neighbourhood

Planned Neighbourhood

% difference

Local shopping 281.02 994.50 71.7Other shopping 93.09 1073.68 91.3School education 1719.63 3867.41 55.5Higher education 2186.87 2359.31 7.3Medical facilities 250.08 251.97 0.7Work 5163.45 8462.60 39.0Professional services 190.10 441.00 56.9Religious & Comm. facilities

828.17 1860.15 55.5

Social & Leisure 817.02 1639.87 50.2Entertainment 2626.86 2641.63 0.6Total 14156.30 23592.12 40.0

Travel to activities namely work, shopping, school education, religious and social trips consumes significantly lower energy

Key findings

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• Mixed land use neighbourhoods use more non-motorised modes

• Car trips for local shopping is lower than walking, motorbike and other locally available means of transport such as auto-rickshaw, cycle rickshaw

• Public transport rather than cars, used for higher order shopping trips, school trips and higher education trips

• Non-motorised modes of travel increases with the location of facilities within the same neighbourhood or in adjoining neighbourhoods

Key findings

• Share of motorised travel to medical facilities, work and professional services is high

• Walking trips is higher for travel to religions/community and social facilities.

• Non-motorised modes more extensively used for local facilities

• Higher trip distance for higher order shopping for more specialised items

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Conclusions : Process

Influence of urban form characteristics on energy use for household mobility can inform policy decision making

Need for a more responsive planning system to manage the growth dynamic within cities in India

Well defined neighbourhoods and non-motorised use of transport reduces per capita carbon emissions

Infrastructure to support activity nodes and service provision in a sustainable way

Conclusions: Actions

Integrated planning and transportation policy response

Financial institutions need to support investment in sustainable transport.

Innovative design solutions to reflect the principles of sustainability agenda

Up skilling key actors in future implication of climate change on urban environment

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Thank you for your attention

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