TRANSPORT AFFORDABILITY FOR RELOCATED

URBAN POOR:

CASE STUDY OF SABARMATI RIVERFRONT,

AHMEDABAD

Authors : 1) Mustafa K. Sonasath, Research Associate, CoE-UT, CEPT

2) Dr. Bhargav Adhvaryu, Associate Professor, CEPT

MIED, Faculty of Technology, CEPT University.

Contents:

Introduction

Research Methodology

Literature Review

Data Collection

Data Analysis

Conclusion

Relocation:

In general terms relocation is defined as to move or be moved to a new place,

area or place of employment.

It generally is restricted to physical movement.

Relocated urban poor:

The persons to whom relocation is done are called relocated urban poor.

It is often synonymous to “project affected persons”.

What is relocation and who are relocated urban poor?

Often, relocation of urban poor is done at outskirts of the city and not in-situ.

Travel Time

Travel Distances

Travel Cost

Need for the study

?

Due to relocation, the travel

characteristics does not change at

once and urban poor tend to travel at

same places for work.

Scope and limitations:

The scope is restricted to study

affordability of relocated urban poor

from Sabarmati riverfront

The study is limited just to work trips as

work trips share maximum proportion of

total trips.

RESEARCH

OBJECTIVES &

METHODOLOGY

Transport Affordability for relocated urban poor:

Case study of Sabarmati riverfront, Ahmedabad

• Research Aim:

To study affordability of public transport for relocated urban poor.

• Research Objectives:

1) To study the travel behaviour of relocated urban poor.

2) To identify the travel patterns of relocated urban poor.

3) To measure and compare affordability index for various modes of transport.

4) To determine the modal shift distance of relocated poor using choice probability model.

5) To determine the impact of various travel parameters on the mode choice of relocated urban poor using multinomial logit model.

A. Literature Review

Reviewing papers and policy practices if any

Best Rehabilitation Practices

International National

What is affordability and how is it measured?

Case studies of various cities

Need for the

study

Relocated sites in

the city

Determining issues

they faced in

generic manner

Identify

Relocation faced

due to various

projects

Who are relocated

urban poor?

Narrowing down the

transportation issues

which they face

Which discrete models can be applied?

What type of survey method to be used?

What sampling method can be used?

1. Locations of survey

2. Who to survey?

3. What kind of primary data

will the study need?

4. What kind of secondary

data will the study need?

5. Preparing conceptual

framework for it.

Hypothesis

Relocated urban

poor afford public

transport.

Methodology

Pilot surveys

Schedule of surveys

Determining sample

size

Sample collection

from each location

E. Actions B. Data Collection

Final survey form

Primary

C. Analysis

Preliminary analysis of data

Studying their travel behavior

Studying the O-D pattern of work

trips before and after relocation

Studying income classes for both

scenarios

Studying their modal share for both

scenarios

Exploring what more can be

interpreted?

Travel patterns

Comparison of index by

different modes

Determining the break

even distance from which

mode changes

Affordability index for

various modes

Determining the sensitivity

of various parameters

influencing mode choice

Outputs

Can policies be

implemented for

them?

Report

Presentation

Model developed

using appropriate

software

Methodology

Final proposals

Analyzing the

questionnaire

prepared

Data

Secondar

y

Setting range of distance between

slum and rehabilitation site Elasticity concept to

measure impact on them

with increase in fares

LITERATURE REVIEW

Transport Affordability for relocated urban poor:

Case study of Sabarmati riverfront, Ahmedabad

4 A’s for urban transport

Source: World Bank, 2009

vailability

ccessibility

ffordability

c cceptability

What is affordability?

“Affordability” refers to the extent to which the financial cost of journeys

put an individual or household in the position of having to make sacrifices to

travel or the extent to which they can afford to travel when they want to.

(Source: World bank)

While a family on a low income (say in the bottom quartile of the income

distribution) might be able to afford the necessary journeys to work for the

income owners of the family, they might not be able to afford trips to school

or any leisure. For such a family, urban transport would, by most standards,

be considered unaffordable.

Armstrong – Wright and Thiese, 1987 There is an affordability problem with public transport when more than 10% of households spend more than 15%

of income on work related trips.

Gomide A., S. Leite and J. Rebelo, 2004 More than 6% of income spent on transportation is considered as unaffordable. (Belo Horizonte, Brazil)

Venter and Behrens, 2005 10% of income spent on transportation is unaffordable ( South Africa)

Venter and Behrens, 2005 The relation between welfare and expenditure on transport as a percentage of income may not be monotonic.

Carruthers, Dick and Saurkar, 2005 Formed a fixed basket to estimate an affordability index.

What is affordable transportation?

World Bank, 1980 In developing countries, a reasonable level of household expenditure on bus travel should not exceed 10 percent

of household income.

How affordability is measured?

Affordability comparison of various cities

0 10 20 30 40 50 60 70 80 90

100 110 120

Average class Bottom quintile

• The affordability index in case of Brazilian cities is very high for the bottom quintile group. Vale transport system prevails in

Brazilian cities but there is no effect on bottom quintile group as mostly people are in informal sector.

• For Indian cities, the affordability index is also high for the bottom quintile group so there is a need to propose some

policies in favour of poor.

• Developed cities have index below 10 which suggests that the transportation is affordable.

Source: World Bank, 2010

Affordability threshold of 10%

The Rs.1200 crore Sabarmati Riverfront project is an urban rejuvenation project in Ahmedabad, the largest city and

commercial capital of the western state of Gujarat.

10,000 families who used to live in different neighbourhoods along the project’s 11- kilometre stretch along the river were

displaced from their locations.

These families were rehabilitated under Rehabilitation & Resettlement policy.

Sabarmati riverfront project

Source: CUE, CEPT

Source: SRFDCL

Paldi

Keshavnagar

Usmanpura

Income tax Khanpur

Shahpur

Dudheshwar

Nehru bridge Lal Darwaja

Ellis bridge Raikhad

Kagdiwad

Jamalpur

Chandranagar

New Wadaj

Vijay mill

Behrampura

Ajit mills

Odhav 1

Odhav 2

Odhav 3

Vatwa 2

Vatwa1 Isanpur

Vivekanand mill

Raipur mill

Saraspur mill

Kaisar e Hind mills

• Relocation was done at different BSUP sites in the

city.

• Majority of the sites were far off from slum sites.

• Average distance of these sites are around 7 kms

• Sites like vatwa and odhav are 12 kms away on an

average.

Shahwadi

Rustam mill

Study area

• Relocation was done at different BSUP sites in the

city.

• Majority of the sites were far off from slum sites.

• Average distance of these sites are around 7 kms

• Sites like vatwa and odhav are 12 kms away

• Total no of households: 11568

New Wadaj

Vijay mill

Behrampura

Ajit mills

Odhav 1

Odhav 2

Odhav 3

Vatwa 2

Vatwa1 Isanpur

Vivekanand mill

Raipur mill

Saraspur mill

Kaisar e Hind mills

Shahwadi

Rustam mill

1568

2464 384

1440 992

192

224

704 320

160 704

416

288

800

336

576

Source: CUE, CEPT

DATA COLLECTION

Transport Affordability for relocated urban poor:

Case study of Sabarmati riverfront, Ahmedabad MIED, Faculty of Technology, CEPT University.

Sampling:

Stratified proportionate sampling was used to ensure proportionate representation.

Distance from slum site to rehabilitated sites were divided in 3 ranges:

1) 0-6 km.

2) 6-12 km.

3) Above 12 km.

So, from all the above range 200 samples each was collected.

Total no of households in BSUP sites: 11568

Samples collected = 600

New Wadaj

Vijay mill

Behrampura

Ajit mills

Odhav 1

Odhav 2

Odhav 3

Vatwa 2

Vatwa1 Isanpur

Vivekanand mill

Raipur mill

Saraspur mill

Kaisar e Hind mills

Shahwadi

Rustam mill

60

95 20

91 74

15

17

37 12

6 27

27

15

42

24

38

DATA ANALYSIS

Transport Affordability for relocated urban poor:

Case study of Sabarmati riverfront, Ahmedabad MIED, Faculty of Technology, CEPT University.

Inter zonal trips Travel Pattern

80%

of

trip

s w

as intr

a z

onal

Before relocation After relocation

7 %

tri

ps is intr

a z

onal

Source: Primary survey

80%

of

trip

s w

as intr

azonal

7 %

tri

ps is intr

azonal

Source: Primary survey

Intra zonal trips Travel Pattern

Before relocation After relocation

57.69

12.39

2.13

1.7

15.38

4.7 0 5.98

17.72

13.71

2.53

1.68

41.13

10.97

2.1 10.12

59.09

9.5

4.13

1.23

18.59

5.78 0 1.65

15.32 8.46

3.63

0.8 56.45

8.46 2.02 4.83

64.55

4.21

2.53

0.42

16.45

4.21 0 7.59 9.63 2.1

2.93

0.42

62.34

10.87

0.84 10.87

Legends

Walk

Cycle

Pedal

rickshaw

Hand cart

Shuttle

rickshaw

AMTS

BRTS

Private

Before relocation After relocation

0-6 kms

6-12 kms

> 12 kms

Modal Share

60.44

8.7 2.95

1.13

16.83

4.9 0 5.05

14.22

8.08 3.04

0.96

53.38

10.09

1.66 8.57

After relocation Before relocation

Modal Share Legends

Walk

Cycle

Pedal

rickshaw

Hand cart

Shuttle

rickshaw

AMTS

BRTS

Private

• There is a shift from NMT to MT due to relocation, wherein the chief MT mode is shuttle rickshaw which

implies more transport expenditure.

64.56

21.38

10.69 2.81 0.56

18.37 12.43

27.35 34.81

7.04

0

10

20

30

40

50

60

70

0-2 kms 2-4 kms 4-8 kms 8-12 kms > 12 kms

Perc

enta

ge

Trip Length

Before relocation After relocation

Trip Length

• The trip length has increased significantly for the relocated urban poor.

• Due to this increase, various factors like trip time, affordability index, motorized modal share has

increased.

.

Before : Mean -2.12 km, Mode-0.5 km. After : Mean - 6.62 km, Mode-10.2 km

0

50

100

150

200

250

300

350

0 5 10 15 20 25 30 35

Freq

uen

cy

Trip Length

Frequency vs Distance

Before After

Trip time 62.31

17.02 15.19

4.71 0.77

13.83 13.69

26.97 34.16

11.24

0

10

20

30

40

50

60

70

0-10 mins 10-20 mins 20-40 mins 40-60 mins >60 mins

Perc

enta

ge

Trip Time

Before relocation After relocation

• The trip time has increased significantly for the rehabilitated urban poor.

• Due to increase in trip time after rehabilitation, the graph has shifted to right skewedness.

.

Before : Mean-15 mins, Mode-5 mins. After : Mean-41 mins, Mode-60 mins

0

100

200

300

400

500

600

0 20 40 60 80 100 120

Fre

qu

ency

Trip Time

Frequency vs Time

Before After

Affordability Index

80

104

49

0 10 20 30 40 50 60 70 80 90

100 110

AMTS/BRTS Shuttle rickshaw Private

Transport is assume to be unaffordable

when affordability index is more than

10%.

For modes like AMTS,BRTS, shuttle

rickshaw and private the transportation is unaffordable.

Affordability index of shuttle rickshaw is higher than 100% which means the bread winner of the family is

taking family members part for transportation due to which it can hamper other trips such as shopping,

leisure, religious, educational etc.

Choice models

In statistics, logistic regression modelling is a tool used to estimate probabilities of two or more

discrete outcomes given input variables such as real valued, binary or categorical

If there are 3 discrete choices, then multinomial logit model (MNL) is used..

If there are 2 discrete choices, then binary logit is used.

In this study, MNL was used to understand the influence of independent variables on mode choice.

In this study, binary logit was used to estimate the threshold distance of mode shift from NMT to MT>

Type Variable

Name Unit

Type of

Variable Description

IV Age Years Continuous Age of person

IV Gender NA Categorical Gender of a person; 0: Male, 1: Female

IV HH Income Rs Continuous Income of person.

IV Occupation NA Categorical Occupation of person; 0: Labour + Vendor + Maid 1:

Autodriver + Job, 2: Business.

IV Trip Length Kms Continuous Total trip length from origin to destination.

IV Amount Spent

Rs Categorical

Total amount spent on transport from origin to destination

IV Bread winners NA Continuous Number of earners in a family

DV Mode used NA Categorical

Mode used for transport from origin to destination; 0: Non-

motorised transport ( walk, cycle, pedal rickshaw, hand

cart) 1: Public Transport ( BRTs, AMTs, shuttle rickshaw)

2: Private

Input variables for MNL

All variables are categorized such that frequencies of samples are evenly distributed.

Travel distance is only provided in the variable in place of travel time because of co-linearity problems.

Bread winner is only provided in variable in place of family size, number of adults and children because of co-linearity

problems.

Mode used has been created combining all revealed choices

Parameter Estimates Mode choice Model β Sig Exp (β)

NMT Intercept only 18.471 .000

Bread winners 2.023 .000 7.558

Family income .000 .000 1.000

Amount spent -.149 .000 .862

Trip Length -.194 .000 .824

Age -.008 .491 .992

Occupation = 0 1.802 .007 6.059

= 1 -0.680 .290 .507

= 2 0b

Gender = 0 -15.886 .000 .000

= 1 0b

• The reference category is taken as Private mode for the model.

• For occupation, reference category is business class and for gender, female is taken as reference.

• Negative utility coefficient: 1) Amount Spent Positive utility coefficient: 1) Bread winners

2) Trip Length 2) Family income

3) Age 3) Occupation (labour class)

4) Occupation (job class)

5) Gender (Male)

The odds of preferring NMT over private increases by 7.5

for bread winners

With males, the usage of NMT decreases as compared

to females

Labour class prefer to use NMT as compared to business

class

Usage of NMT decreases for job class by 0.507 with

reference to business class

Increase in one year of age the usage of NMT

decreases, however the value is insignificant

The odds of preferring NMT decreases by 0.824 as the

travel distance increases.

Increase in usage of NMT decrease the amount spent by

0.862

Family income have a direct bearing on mode choice and

it increases by 1 for NMT

Pseudo R-square: 0.47

Key outputs from MNL 1 (Probability of choosing NMT over private

automobile)

• The probability of choosing NMT over private mode decreases as following change:

• Increase in WTL

• As age increases.

• If occupation is job class (opposite for labour class)

• If gender is male.

Parameter Estimates Mode choice Model β Sig Exp (β)

Public

Transport

Intercept only 16.389 .000

Bread winners 1.916 .000 6.793

Family income .000 .000 1.000

Amount spent .014 .264 1.014

Trip Length .087 .005 1.091

Age -.010 .338 .990

Occupation = 0 1.255 .016 3.507

= 1 -.883 .064 .413

= 2 0b

Gender = 0 -16.173 .000 .000

= 1 0b

• The reference category is taken as Private mode for the model.

• For occupation, reference category is business class and for gender, female is taken as reference.

Negative utility coefficient: 1) Age Positive utility coefficient: 1) Bread winners

2) Occupation (job class) 2) Family income

3) Gender (Male) 3) Amount Spent

4) Trip Length

5) Occupation(Labour class)

The odds of preferring PT over private increases by

6.793 for bread winners

With males, the usage of PT decreases as compared to

females

Labour class prefer to use PT as compared to business

class

Usage of PT decreases for job class by 0.413 with

reference to business class

Increase in one year of age the usage of PT decreases,

however the value is insignificant

The odds of preferring PT increases by 1.091 as the

travel distance increases.

Usage of public transport increases the amount spent by

1.014

Family income have a direct bearing on mode choice and

it increases by 1 for PT

Key outputs from MNL 2 (Probability of choosing PT over private

automobile)

• The probability of choosing PT over private mode decreases as following change:

• As age increases.

• If occupation is job class (opposite for labour class)

• If gender is male.

• The probability of choosing PT over private mode increases as following change:

• As trip length increases.

Pseudo R square

Cox & Snell 0.564

Nagel kerke 0.424

Mc Fadden 0.382

Parameter Estimates

Mode choice Model β Sig Exp (β)

NMT Intercept only 2.751 .000

Trip Length -.340 .000 .712

• The reference category is taken as Motorised mode for the model.

• Negative utility coefficient: Trip Length

The odds of preferring NMT decreases by 0.712

as the travel distance increases.

Probability of mode choice

0.5

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 5 10 15 20 25 30 35

Pro

bab

ility

Work Trip Length (km)

Probability: NMT vs MT NMT

MT

2.5

• Elasticity of demand for affordability index comes out to be 0.52.

Further debates

In-situ relocation

Concessionary fares

Relocation within modal shift distance

Thank you.