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)