Megha Aggarwal

Dr. (Prof.) Sanjay Gupta

School of Planning & Architecture, New Delhi

Contents

• Introduction

• Delhi – Scenario

• Literature Review

• Survey Details & Findings

• Scenario Development

• Conclusions

Introduction - Background

URBAN TRANSPORT SCENE

• Motorization Rate (India)

• 1991-2001 – 10%

• 2001-2005 – 12.3%

• 2005-2011 – 16-18%

2010 – No. of Registered Motor

Vehicles = 136.8 million.

Source: SIAM (2012)

Delhi – Highest level of motorization; as well

as highest no. of cars (106 cars / 1000

population; 0.31 cars per HH in 2011)

83%

17%

% Cars

Registered

MV other

than Cars

Cars

Research Need & Objectives

Research Need

• There is an alarming increase in

car ownership levels resulting in

increased congestion levels,

pollutions , safety issues, etc.

• While national policies do

emphasize on public transport

usage there is very little thrust on

ways to achieve restraint on car

ownership and its usage

• Insignificant empirical studies in

mega cities like Delhi on measures

to restraint car ownership and its

use.

Objectives of the Study

• To review the best practices for

restraining car ownership & usage.

• To assess the socio-economic and

mobility patterns of car-users.

• To study the attitudinal behavior

of car users in selected case study

locations in Delhi towards using of

cars & the deterring factors

prohibiting their public transit

usage.

• To evolve alternate policies &

strategies for restraining car

ownership and its use.

Car Ownership Scenario

• 2011 – 17.8 lakh cars (106

cars / 1000 popn.)

• AAGR in no. of cars –

• 1990s – 8%p.a.

• 2000-2010 – 13%.

0.002.004.006.008.00

10.0012.0014.00

1990-9

1

1991-9

2

1992-9

3

1993-9

4

1994-9

5

1995-9

6

1996-9

7

1997-9

8

98-9

9

99-0

0

2000-0

1

2001-0

2

2002-0

3

2003-0

4

No

. of

Cars

(in

Lak

hs)

Year

Car Ownership Trend - Delhi

no. of

cars (in

lakhs)

Mode Share

• Car constituted

almost 40% of the

total traffic but has

only 9% modal share. Source: RITES (2007)

Car Usage (Car Travel Characteristics)

• Average Trip Length by Car – 15.5 km.

• Av. Car Utilization Rate per day – 36km. – 45 km.

Delhi - Scenario

What is happening on Delhi Roads??

Only 1 lane

left for moving

vehicles

2 lanes

encroached

upon by parking

Lajpat Nagar Market

Traffic Jam at Ashram Flyover Traffic Jam at Mathura Road

Karol Bagh Market

More than half of the

carriageway is under

parking

Literature Review - Car Ownership

Restraint Policy Examples:

1. Car License Quota

No intervention on car

ownership policy

Rigidly limited quota of car

license Eg. Beijing Eg. Shanghai

Car

ow

ners

hip

1986

2009

1.4 cars / 1000 pop. 0.90 cars / 1000 pop.

220 cars / 1000 pop. 80 cars / 1000 pop.

2. Vehicle Quota System, Singapore Fixed growth rate of car/annum

-1990 – 3% P.A.

- 2009 - 1.9% P.A.

Certificate of Entitlement (Coe)

Bidding Process

3. Increase in Taxes on Car Price, Japan

Car Ownership Restraint Policies

Licensing System Taxation (Excise

Duty, Road Tax,

Etc.)

Increasing

Interest Rate On

Car Loans

Have only short-term effect,

Delays purchase of car,

Leads to shift from higher to lower

model of particular make

Based on fixed saturation level for

the city

License plates are auctioned or

given on certain other criteria like

availability of parking space at

residential area

Relevance in Delhi’s Scenario:

1. Fixing up of Saturation level can be done, but ensuring a transparent &

equitable bidding system will be a challenge.

2. Fixing up of quota according to availability of parking space at residential

area.

3. Increasing interest rates on car price can only be feasible if the interest

rate is increased annually according to the increase in per-capita income.

Expert’s Opinion:

1. Vehicle Quota System, Resident Parking Permit Program may prove to be

relevant and feasible for car ownership restraint in Delhi.

• Summary:

Literature Review - Car Ownership

Restraint Policy (cont.)

Impact:

- 32% reduction in travel times

- 8.75 – 10 lakhs passenger / day

- decrease in air pollution - 43% reduction in SO2, 18%

reduction NO2, & 12% reduction in particulate matters

- Fatality rate dropped by 42%.

Car Use Restraint

Examples:

1. Congestion Charging – London, Singapore

Impact:

1. Total Traffic Volume

decreased by upto

14% in 1 year.

2. Modal share of car

has decreased by

over 36% in the

cordoned zone

3. PT usage has

increased by over

30%

CORDONED ZONE

CONGESTION

CHARGING

SIGNS AT THE

EDGE OF THE

ZONES

PUBLIC TELEPHONES

WITH INTERNET

PAYMENT OPTION

CAMERAS

WITHIN THE

ZONE

SIGNAGE & USE OF TECHNOLOGY IN THE CORDONED ZONE

CHANGES IN THE VOLUME OF INBOUND

TRAFFIC DURING THE MORNING & EVENING

PEAK HOURS SINCE 1975.

LOCATION OF ALS

RESTRICTED ZONE,

SINGAPORE

AREA LICENSING SCHEME, SINGAPORE

ALS RESTRICTED ZONE

WARNING SIGN

Impact:

1. Total Traffic

Volume decreased

by upto 37% in 1

year.

2. Modal share of car

decreased by over

70% in the ALS

zone.

2. Oxford Park & Ride System

Dedicated bus service from park & ride

sites to all activity areas – segregated

bus lanes, bus priority system

- 2 in 5 cars shifted to the system (1.3

million car/day less on road)

- Modal share of bus increased from

50% to 80% (in 12 years)

3. Enhancing PT supply & coverage (Bogota’s Transmillenio)

Relevance in Delhi’s Scenario:

1. Congestion charging can be adopted in places like CP, Chandni Chowk,

ITO which have good connectivity by metro & bus.

2. Park & Ride strategy is highly relevant. (Park & Ride sites can be

proposed at various regional parks, stadiums, etc. and these should be

complemented with shuttle service to the activity area.)

3. Enhancing PT supply & coverage would be highly relevant, if supported by

feeder service, pedestrian pathways, state of art infrastructure, multi-

modal integration, etc.

Expert’s Opinion:

1. Hiked & variable parking fees, park & ride facility, enhancing PT supply &

coverage have been rated as relevant policies by experts. Whereas

congestion pricing, car pool, etc. were regarded as not efficient policies

for restraining car use.

Survey Details and Findings –

Case Study Area

• Case Study Selection criteria – Car Trip Attraction Intensities,

access to Public Transport, Hierarchy of use zone.

MAP: Car OD Pattern – Delhi

(Source: RITES, 2007)

Use Type / Activity

Node

Access To PT

Bus Only Bus + Metro

Commercial Areas South Ex. Lajpat Nagar

G.K. Market Connaught Place

Work Zone

Bhikaji

Cama

Place

I. P. Estate

Nehru Place

Saket

Lodi Institutional Area

Table: Case Study Selection

Case Study - Information

Aspect Commercial Centers

Lajpat Nagar Connaught Place

No. of Establishments 234 462

Footfalls / day 16000 250000

Parking Space 2621 E.C.S. 10326 E.C.S.

Aspect Work Zones

Bhikaji Cama

Place

I.P. Estate

Footfalls / day 17500 15000

Surveys Conducted & Sample Covered

S.No. Aspect

1 Personal or HH Information (HH Income, Vehicle Ownership Details,

Car usage)

2 General Car Use (ATL, CU, Trip Purpose)

3 Attitudinal Survey (Reason for choosing Car & not PT; willingness to

shift to PT)

4

Pre- & Post- Metro Survey (Done Only For Connaught Place & I.P.

Estate) (Mode used to travel to the case location before & after Metro &

frequency of visit)

Table: Surveys Conducted

S.No. Case Study Total footfalls

/ day

Sample Covered

General Visitor Car User

1 Lajpat Nagar 16000 45 116

2 Connaught Place 250000 47 64

3 Bhikaji Cama Place 17500 45 63

4 I.P. Estate 20000 50 63

Total 187 306

Table: Sample Covered

A total of 493

persons were

covered of

which 306 were

car users.

Findings – Socio-economic

Characteristics 1. Threshold Income for a HH to own a car – Rs. 20,000/month

2. Av. HH vehicular ownership – 1.5-2 vehicles / HH

3. Av. HH car ownership – 1-1.4 cars/HH (Almost 87% HHs

owned car(s)) % HHs 1 Car

2 Cars

More than

2 cars55-60%

25-35%

8-13% 4. Frequency of Car Use

Car 1 - ~85%

daily

Car 2 – 55%

being used 1-2

times a week

Car 3 – 1-2

times a month

Indicates that having multiple car is more of a social status thing and not a necessity

Findings – Car Trip Information • ATL of Car (in km.) – 14-16 km. (during weekdays)

- 16-17 km. (during weekends)

• Car Utilization Rate – 32-34 km. (during weekdays)

- 36-37 km. (during weekends)

15.5 km. (for

Delhi (RITES, 2007)

~40 km. (for

Delhi (CRRI, 2002)

• Modal Split observed

7%

40%

17%

34%

2%

Modal Share (Comm.

Areas) Two-

wheelerCar

Auto

Metro

Bus

Not connected by metro

39%

34%

7% 0%

20%

Modal Share (Work

Zone - Bhikaji

Cama Place)

13%

33%

9% 6%

9%

30%

Modal Share (Work Zone -

I.P. Estate) Two-Wheeler

Car

Auto

Metro

Bus

Metro+Bus

Findings – Attitudinal Response • Reasons for choosing car over

other modes

90 100

17 10

30

0

20

40

60

80

100

120

Reasons

% R

esp

on

den

ts

Reasons for Choosing Car

over Other Modes

Comfortable

Convenient

Safer

Lack of PT

Saves Time

• Willingness to Shift to Bus or Metro

65

29

92

66 67

32

62

44

0102030405060708090

100

Bus Metro%

Resp

on

den

ts

% Respondents not willing to

shift to Bus or Metro Lajpat Nagar

Connaught

Place

Bhikaji Cama

Place

I.P. Estate

• Willingness to pay extra for travelling by car (in the form of hiked

parking fees or congestion pricing) – 75-80% respondents are willing

to pay upto Rs. 80-100 beyond which they would shift to other modes

Pre- & Post-Metro Analysis

• Mode Used to commute before and after coming up of Metro

26%

16%

0%22%

36%

PRE-METRO

31%

8%

2%8%17%

34%

POST-METRO

CAR 2-WHEELER TAXI BUS AUTO METRO

26%

16%

0%22%

36%

PRE-METRO

31%

8%

2%8%17%

34%

POST-METRO

CAR 2-WHEELER TAXI BUS AUTO METROConnaught Place In Case of commercial areas,

• Modal shift to metro was seen

mainly from 2-wheeler, bus, and

auto.

• Mode share in car has

increased over time.

30%

23%

30%

17%

PRE-METRO

23%

12%

13%11%

10%

31%

POST-METRO

CAR 2-WHEELER BUS

AUTO METRO METRO + BUS

I.P. Estate

30%

23%

30%

17%

PRE-METRO

23%

12%

13%11%

10%

31%

POST-METRO

CAR 2-WHEELER BUS

AUTO METRO METRO + BUS

In case of work areas,

• Modal shift to metro was seen

from cars, 2-wheeler, bus, and

auto.

• Availability of bus service from

metro station to work areas

played an important role in the

shift

Scenario 1: Delhi – Car Ownership

Business As Usual Scenario

BUSINESS AS USUAL

SCENARIO

• If trend continues, by 2022 – no. of cars will increase to 49.9 lakh cars.

(» 245 cars per 1000 population as compared to 106 cars per 1000 population in 2011 – 2.3 times more)

y = 3.7314e0.0855x R² = 0.9931

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

0 5 10 15

NO

. O

F C

AR

S (

IN L

AK

HS

)

TIME (T)

Car Ownership Trend

no. of cars (in lakhs)

Expon. (no. of cars(in lakhs))

1990 1995 2000 2005

1990-91 – 3,98,479 cars

2003-04 – 12,67,700 cars

Model Development from Car

Ownership Trend

Based on time series trend,

exponential curve best fitted.

y = 3.7314e0.0855x

R² = 0.9931

Scenario 2: Policy 1- Vehicle Quota

System

• Assessment of Desired Car Ownership Levels.

• Based on cross-sectional model formed from tpt. System characteristics

of liveable cities (Perth, Stockholm, Munich, Sydney, Zurich, Vienna,

Melbourne)

Car Congestion Index = Total Car kilometer / Network Kilometer

Average Car congestion index for liveable cities = 4540

Accordingly, assuming % area under roads to be 21% by 2022, Delhi should

limit to 210 cars /1000 population by 2022.

Livable

Cities

Asian

Cities

Car congestion index is lesser

in liveable cities, even though

they have higher car ownership

rate lesser dependence on

car

Scenario 2: Policy 1- Vehicle Quota

System (cont.) • For calculating motorization level

per year:

Where,

M = Motorization level at year ‘T’,

S = Saturation level for Car ownership

(i.e. 210 cars per 1000 population)

T = Year for which motorization level is

required.

a & b are coefficient, from the model

based on trend; i.e. y = 3.731 e(0.085x);

therefore, a = 3.731 and b = 0.085

[From the exponential model above]

M = S/(1+e(a – bT))

0

10

20

30

40

50

60

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

No

. of

Cars

(in

lakh

s)

Year

Projected No. of cars (BAU

vs. VQS Scenario)

BAU VQS

By 2022 – no. of cars will increase to

33.2 lakh (16.7 lakh lesser cars than

BAU)

Scenario 2: Policy 1- Vehicle Quota System –

Implementation Mechanism

Fixing up of Saturation Level Based on car congestion index of livable

cities

Calculation of total cars

which can be sold per year

Bidding for cars

Bidding process based on current HH

ownership levels – for ensuring equity

HHs With No Cars HHs With 1 Cars HHs With 2 Or More

Cars

Market

segmentation of

cars –

1.Car price upto 6

lakhs – 70%

2. Car price b/w 6-

10 lakhs – 26%

3.Car price above

10 lakhs – 4%

70% quota 25% quota

Subject to:

1.Scrapping or selling

of the old car;

2.More than 1 earning

member in the HH.

5% quota

Subject to:

1.Scrapping or selling

of the old car.

Certificate of car ownership

Scenario 3: Policy 2 - Car Use Restraint

Policy

• Assessment of Desired Car Usage Level

Income / household Average No. of

Cars Owned

CU (Km.) Predominant Trip Purpose

Rs. 20000 - Rs. 40000 1 20 Social

Rs. 40000 - Rs. 60000 1 28 Social, Shopping

Rs. 60000 - Rs. 80000 2 30 Work, Social

Rs. 80000 - Rs. 100000 > 2 34 Work, Social, Recreational, Shopping

> Rs. 100000 > 2 37 Work, Social, Recreational, Shopping

Table: Primary Survey Data

• Av. CU for essential purposes – 30 km.

• Average Emission levels for a liveable city – 412.5 tonnes per

day

• Achieving this would imply car utilization to be limited to 28

km. / day

• Desirable Car use level to be fixed at 30 km./day

• Implementation Mechanism

Car Mobility Card

To be issued to all motor vehicle owning people

Card to be swiped each time

while purchasing:

1. Petrol,

2. Diesel,

3. CNG

People using public transport can:

1. Either save their car points & use it in bulk

anytime;

2. Sell back their car points to the issuing

govt. agency & get their money back.

If the points are

enough, recharge

every month or

on year basis.

If more points

are reqd.

Fixing up of total car usage or

fuel consumption per car

Central

controlling

agency

1. Next set of point at

double the rate of the

original card value

Scenario 2: Policy 2 - Car Use Restraint

Policy (cont.)

Impact Comparison

Year

No. of Cars (in

Lakhs)

No. of Cars / 1000

Population

Total Car Kilometer /

Day (in Lakh

Km./Day)

GHG Emissions

(Tonnes / Day)

BAU VQS Use

Ration BAU VQS

Use

Ration BAU VQS

Use

Ration BAU VQS

Use

Ration

2011 17.8 17.8 17.8 106 106 106 605.2 7868

2017 34.1 23.5 23.5 182 158 158 1159.4 799 705.9 15072 10387 9177

2022 49.9 33.2 33.2 245 207 207 1696.6 1128.8 994.8 22056 14674 12930

Impact Assessment:

1. Vehicle Quota System – 33.5% reduction will be observed.

2. Car Use Rationing – 12% further reduction will be observed.

* Taking CU rate as 34 km./Car/Day from the

Primary Survey ** At 34 km./Car/Day, one car will emit around 4.42

kg. GHG emissions/day (Assuming car average to be

10 km./litre)

Conclusion

• Implementing VQS – likely to result in 33.5% lesser no. of cars than BAU

scenario

• Implementing Car use rationing – likely to result in 12% lesser car km.

travelled, fuel consumption, GHG emissions

• Implementing strategies like Park & Ride System, enhancing PT coverage

and supply result in only 9% lesser car km. travelled, emissions, but

implementing these help in creating liveable environment in activity areas,

by providing more walk space and reducing the space required for car

parking.

• Other major recommendations include having public transit service

connecting all markets and work places from each of the planning zones.

These buses can be planned on the basis of HoHo buses running currently

in Delhi for tourist destinations.

• Implementation of intelligent transportation system is also recommended.

• Advanced Traveller Information System (ATIS)

• Advanced Public Transportation System (APTS)