Unlocking Cities

The impact of ridesharing across India

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Unlocking Cities
The impact of ridesharing across India

Commissioned by

Vincent Chin, Mariam Jaafar, Suresh Subudhi, Nikita Shelomentsev, Duong Do and
Irfan Prawiradinata

April 2018
ABOUT THIS REPORT

Over the last eight years, there has been a rise in the number of ridesharing firms
providing services in India. These include Uber and Ola. Ridesharing has intro-
duced a new mode of transport to commuters. Despite being in a nascent stage,
ridesharing has begun to influence the transport landscape. It is evident that
ridesharing has the potential to be a vital part of the solution to the region’s
transportation needs.

Uber has commissioned the Boston Consulting Group to assess the potential
benefits that greater adoption of ridesharing may bring to Asian cities. The findings
in this report were reached through research utilising publicly available transport
data, interviews with transport experts, and primary research involving commuters
in each city. The cities covered in this report are Delhi, Mumbai, Bangalore and
Kolkata. For comparison purposes, we have illustrated key indicators for Singapore,
Kuala Lumpur, Jakarta, Surabaya, Bangkok, Hong Kong, Taipei, Ho Chi Minh City,
Hanoi and Manila.

2 Unlocking Cities
In Brief
•• Growth in population and wealth have led to an explosion in transport demand
in India—an increase of 8x since 1980.

•• This explosive growth has put significant strain on Indian transport infrastructure.
Major Indian cities are constantly ranked among the world’s most congested.

•• Going forward, solutions should create a balance between capital investments

required to expand capacity with new infrastructure and improving the efficien-
cy of existing assets.

•• Ridesharing can play a key role in ensuring higher efficiency in the use of existing
assets such as private vehicles. Ridesharing is a new point-to-point transport
model characterised by seven features: (1) Flexible supply base, (2) Smart dis-
patching, (3) Dynamic pricing, (4) Customer network effect, (5) Dynamic routing,
(6) Demand pooling and (7) Feedback collection and management system.

•• With a combination of these features, ridesharing can deliver four major

benefits which can ultimately reduce congestion by 17%-31% and optimise
infrastructure investment:

ǟǟ Providing alternatives to car ownership

ǟǟ Accelerating public transport adoption

ǟǟ Optimising infrastructure timing and location

ǟǟ Supplementing incomes

•• However, realising these benefits would depend on collaboration between the

rideshare ecosystem and the public sector. The willingness of governments to Ridesharing can help
review outdated regulations to support rideshare growth will be an important to reduce congestion
first step. by 17%-31% and
optimise
ǟǟ Public sector: Develop an appropriate regulatory framework to enable infrastructure
rideshare to achieve the necessary scale investment

ǟǟ Rideshare ecosystem: Enhance rideshare offerings, particularly pooling

ǟǟ Collaboration between public sector & rideshare: Co-develop programs

and incentives to encourage adoption of rideshare and pooling, particularly
in conjunction with public transport

The Boston Consulting Group 3

 Executive Summary
Driven by a rapid increase in wealth and population, travel demand in India has
grown eight-fold since 1980. However, the country’s public transport infrastructure
has lagged behind, leading to high levels of congestion across major cities.

We studied the current transportation situation in four of India’s largest cities,

namely Delhi, Mumbai, Bangalore and Kolkata. Our aim was to assess how ride-
share could help these cities to relieve congestion. In the course of our research, we
also compared the status and development in these Indian cities with that of major
metropolitan cities in South East and East Asia. These comparison cities were cov-
ered in a similar BCG study published in 2017.

The level of congestion is significantly higher in Indian cities than comparable cit-
ies around Asia, averaging 149%. This is partly attributable to India’s large popula-
tion and high population density, as well as an under-developed public transporta-
tion network (especially rail-based transport). Additionally, the use of private car is
relatively high in Indian cities. It is the most common mode of private transporta-
tion across the four cities surveyed (modality share ranges from 22%-45%).

High levels of private car usage have led to significant inefficiency. Ridesharing of-
Rideshare vehicles fers one way to improve the utilisation of these vehicles. Our study found that ride-
were able to achieve a share vehicles average 1.95x higher utilisation per year (measured as people-kilo-
high level of efficiency metres per vehicle per year). Furthermore, on an average, 80% of the commuters
in comparison to we surveyed expressed some willingness to forgo purchasing a car if rideshare could
private cars meet their desired level of service. In this way, rideshare adoption can potentially
help to reduce private car ownership across these cities.

Ideally, rideshare vehicles are able to achieve a high level of efficiency due to sever-
al advantages inherent in their technologies and operating models:

•• Flexible supply base consisting of predominantly private vehicles

•• Smart dispatch system through smartphone applications

•• Dynamic pricing to incentivise drivers onto the road during peak demand

•• Customer network effect connecting drivers to a large number of available

passengers

•• Dynamic routing to avoid traffic

•• Demand pooling to match multiple passengers into the same vehicle

•• Feedback collection and management system to improve quality and encour-

age adoption

To demonstrate the magnitude of this impact, we created a theoretical scenario in

which ridesharing matches private car ownership in terms of affordability, timeli-
ness and availability. Our estimates show that this scenario would reduce the num-

4 Unlocking Cities
ber of vehicles required to meet travel demand and bring several benefits in the
four cities covered.

Specifically, in Delhi and Mumbai, the key benefits would include (1) Providing al-
ternatives to car ownership, (2) Accelerating public transport adoption, and (3) Sup-
plementing incomes. In Bangalore and Kolkata, the key benefits would include (1)
Providing alternatives to car ownership, (2) Optimising the timing of infrastructure
investment, and (3) Supplementing incomes.

In this optimal scenario, a reduction of 33%-68% in private cars and congestion re-
duction by 17%-31% can be achieved across these cities. Consequently, this could
significantly help these cities improve their amenity by saving approximately 760 to
22,000 acres of unnecessary parking space in each city.

India’s Transport Growth Journey

India’s rapid growth in population and wealth over recent decades has led to a consid-
erable strain on its transport infrastructure. Since 1980, the country’s population has
nearly doubled (~90% growth), replacing China as the world’s most populous nation.
In the same period, its Gross Domestic Product (GDP) per capital grew by more than 5
times, with most of the growth recorded in the period since 2000.1 Based on research
demonstrating the relationship between transport demand, population and wealth,
we estimate that transport demand has increased by almost 8 times since 1980.2

EXHIBIT 1 | Indexed Estimated Growth in Travel Demand (1980 = 100)

Index

800

600

400

200
1985

2003
1980
1981
1982
1983
1984

1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002

2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016

Australia Singapore Hong Kong Malaysia Indonesia India

Vietnam Japan Korea Thailand Philippines

Sources: World Bank; OECD; National Center for Sustainable Transportation; BCG analysis.

The Boston Consulting Group 5

 Major Indian cities are now consistently ranked among the world’s most congested.
In Delhi, congestion was estimated to cost the city USD 9.6 billion (~12% of its GDP)
annually in fuel waste, reduced productivity, air pollution and accidents.3 According
to the latest World Bank Global Infrastructure Outlook, India would need approxi-
mately USD 4.5 trillion investment in infrastructure by 2040, of which USD 974 bil-
lion would be required for railway and road networks alone.4

In this report, we focus on the current state of transportation and congestion in four of
Congestion is the largest cities in India, namely Delhi, Mumbai, Bangalore, and Kolkata, where total
estimated to cost the congestion costs were estimated to be as much as USD 22 billion per year. We then esti-
four cities USD 22 mate the potential positive impact of ridesharing in each city. Indian cities share similar
billion per year challenges in terms of high congestion and under-developed public transport networks
unable to meet demand. However, each city’s transport network has unique character-
istics. The cities covered in this study may be further divided into two categories:

•• Delhi and Mumbai: These two cities are mega urban centres with relatively
more developed modern public transport systems. According to our interviews,
the Delhi metro is frequently considered to be the best quality transportation
network in the country, while Mumbai’s suburban rail, new metro line, and Bus
Rapid Transit (BRT) are known to be the busiest nationwide. Nevertheless,
congestion levels remain high due to a large number of private vehicles and the
low quality of road infrastructure. With the burgeoning population and the
growing prosperity of Delhi and Mumbai, the reliance on cars is expected to
increase, adding more pressure to road networks.

•• Bangalore and Kolkata: Relatively smaller than Delhi and Mumbai in terms of
population, these two cities have less modern and more road-based public trans-
port networks. While investments are being made to build more modern rail lines,
the current lack of public transport capacity, coupled with huge private vehicle
ownership numbers (both cars and motorbikes), has caused intense congestion.

Going forward, each of these cities are expected to face diverse challenges that
would require different approaches to achieve sustainable solutions. However, each
city will need to reduce their reliance on private vehicles and find more efficient
point-to-point transport alternatives.

•• Overall, travel by public transport accounts for 19% and 54% of kilometres
travelled in Delhi and Mumbai, respectively. The need to further improve
overall quality, network reach and ease of access from feeder transport modes
have been recognised as key challenge areas. Governments of both cities have
indicated that maintaining control over vehicle growth and encouraging the use
of public transport are their key objectives going forward.

•• For Kolkata and Bangalore, congestion levels are relatively higher than in
other cities, despite their smaller populations. This is driven by the limitation of
their older public transport networks which are primarily road-based, along
with a significant growth in private vehicles. Looking forward, a combination of
infrastructure improvement, addition of more modern mass transit as well as
efficient alternatives to vehicle ownership are likely to help curb congestion.

6 Unlocking Cities
 EXHIBIT 2 | Peak Hour Congestion (% Additional Time to Travel in Peak Hours)

200

171
162

150
135 132 134
129
112
105
100
79
65 68 70
63 67%
57
50

0
Delhi Bangalore Singapore Surabaya Taipei Bangkok Manila

Mumbai Kolkata Hong Kuala Jakarta Ho Chi Hanoi

Kong Lumpur Minh

Sources: TomTom traffic index; Google API; Uber; Government statistics; BCG analysis.
Note: Asia average taken from average of East Asian cities based on TomTom traffic index.

EXHIBIT 3 | Cost of Congestion Across Cities

Avoidable social cost of congestion (USD Billion)

10 9.60

5.92
6
4.8 0

1.97
2

0
Delhi Mumbai Bangalore Kolkata

Congestion
129% 135% 162% 171%
level

Sources: Centre for Urban Economic Studies, Department of Economics, University of Calcutta, Kolkata, India.

The Boston Consulting Group 7

Ridesharing and Its Implications for Indian Cities
Ridesharing players such as Uber and Ola have emerged in India over the last five
years. While their entry has not been without controversy, commuters have benefited
from a wider array of transport options. For the purposes of this report, we will focus
on the benefits of ridesharing in meeting a city’s transport needs, as well as the key
challenges which have emerged with the introduction of this transport model.

WE DEFINE RIDESHARING AS A POINT-TO-POINT TRANS-

PORT MODEL CHARACTERISED BY SEVERAL ELEMENTS:

•• Flexible supply base: Flexible connect with a large network of

driver supply base that utilises potential passengers
existing private vehicles and taxis,
and can scale quickly to meet •• Dynamic routing: Routes
demand vehicles in a manner that avoids
traffic and minimises travel time
•• Smart dispatching (smart
supply-demand matching): Efficient- •• Demand pooling: Pools passen-
ly maps supply against demand gers based on live demand;
(one-to-one matching) and dispatch- increases vehicle occupancy and
es trip requests in the best way passenger-km delivered per
vehicle
•• Dynamic pricing: demand-re-
sponsive prices, with higher price •• Quick feedback collection and
when demand surges to provide management: Real-time feed-
incentive for drivers to go where back and management system to
they are most needed ensure quality, safety, and satisfac-
tion of customers
•• Customer network: Rideshare
technology allows drivers to

Flexible supply base. Rideshare opens up opportunities for qualified drivers to

work part-time. Through the app, rideshare drivers can choose to drive only when it
is most convenient, and makes the most economic sense depending on their per-
sonal schedule. Therefore, drivers can easily maintain other jobs while driving for
rideshare to supplement their income. This flexible arrangement allows rideshare
to attract large numbers of part-time drivers, to have an ample yet flexible supply
base to meet demand.

Smart dispatching (smart supply-demand matching). Upon receiving booking re-

quests, rideshare technology transmits them to the nearest vehicle. The system de-
termines the most suitable driver for each request to maximize overall efficiency.

Dynamic pricing. Demand and pricing signals allow rideshare to efficiently mobi-
lize drivers to where they are most needed. Areas where customer demand signifi-
cantly exceeds driver supply will be shown to have higher pricing multiples (also
known as dynamic price or price surge). The higher the demand for drivers, the

8 Unlocking Cities
higher the price multiplier. Through the app, participating drivers can check re-
al-time market conditions (demand in the areas, pricing multiples, etc.). While there
have been debates on the use of dynamic pricing, this mechanism does provide
drivers with both information and incentive to move to high-demand areas and pro-
vide customers greater accessibility and choice.

Customer network. Rideshare allows drivers to access a large and expanding net-
work of passengers, thus decreasing the time drivers spend waiting for a booking.
This also increases the efficiency of supply-demand matching and facilitates pool-
ing services. As more customers join these platforms, the revenue pool for drivers
also increases, attracting new drivers to join. The flow-on effect of a growing net-
work is essential to realize the full potential of rideshare.

One factor appreciated by customers using rideshare, and which encourages loyalty,
is the added convenience of electronic payment, stored profiles, and digital records.
Frequent bonuses are given to customers who introduce new riders or drivers to
these platforms. In addition, car-hire platforms worldwide have introduced incen-
tives such as discounts and points redeemable for prizes to encourage repeat book-
ings (for example, the GrabRewards programs in Southeast Asia, Uber and SPG Pre-
ferred Guest Program, the Lyft and Delta Frequent Flyer partnership in the US,
etc.). Together with a simplified registration process, these features have encour-
aged rideshare growth.

Dynamic routing. Once driver and rider are matched, drivers are provided with re-
al-time updates on traffic conditions and the best route to the pickup location and
destination. The dynamic routing feature keeps drivers informed of the best routes
so they don’t have to rely on their personal experience and knowledge of the roads.
This feature not only maximizes driver benefits and helps to avoid congestion, but
also ensures that customers are adequately informed about their trips. Knowing a
driver’s location, contact details, and estimated arrival time, as well as the approxi-
mate cost of their trip, provides customers with an added sense of security, especial-
ly if they are new to the area.

Demand pooling. With sufficient levels of adoption, rideshare can improve vehicle
efficiency further through demand-pooling or trip-sharing. Drivers may have better
income opportunities because they are able to pick up multiple passengers on a sin-
gle trip. Pooling also lowers fares for customers, who share the cost of the trip. Ride-
share quickly maps available routes for multiple bookings, compare existing book-
ings to identify potential synergy, and direct drivers to pick up multiple passengers
on similar routes. Perhaps most importantly, demand- pooling also reduces the
number of cars required to meet transportation demand. It can help to alleviate
traffic congestion and reduce environmental impact.

Quick feedback collection and management. Rideshare prompts users, both

drivers and riders, to give trip ratings immediately after their trips. If users fail to
do so, they will often be prompted again before booking their next ride. This system
encourages quick and efficient feedback collection, and averaged ratings are then
used as signals for the quality of both drivers and riders. This two-way feedback sys-
tem helps to improve accountability.

The Boston Consulting Group 9

In Delhi and Mumbai, greater ridesharing adoption could benefit the cities by:

•• Providing alternatives to car ownership: Among the commuters surveyed on

average, 89% plan to buy a new car in the next 5 years. This underscores grow-
ing wealth in these cities and the general perception that owning a car is a
symbol of success. However, over 79% of the respondents who plan to purchase
a car also indicated that they would consider aborting their purchase plans,
should the availability and timeliness of ridesharing equal or exceed private car
ownership. Substituting ridesharing for private cars could eliminate between
46%-68% of the total cars on the road in these cities—going a long way towards
eliminating congestion during peak hours.

This means that the reduction in private car ownership would also support
‘liveability’ in these cities. With reduced car ownership, land previously used for
parking spaces, could be re-allocated to enhance living conditions, such provid-
ing additional housing and social infrastructure. Fewer private cars usage would
also help to significantly reduce CO2 emissions.

•• Accelerating public transport adoption: Despite a significant investment in

quality public transport infrastructure, the modal share of public transport
remains dismally low. Ridesharing has the potential to act as a feeder to public
transport, particularly if applications and incentives are developed to encourage
intermodal transport usage. Outlying areas without easy access to public
transport would particularly benefit, if there is a sufficient number of rideshare
drivers to serve those areas.

•• Supplementing incomes: On average 25% of the private car owners surveyed in

Delhi and Mumbai expressed a keen interest in becoming rideshare drivers. An
additional 43% were somewhat willing to consider the role in order to generate
supplemental income.

In the case of Kolkata and Bangalore, the cities stand to benefit the most from
ridesharing by:

•• Providing alternatives to car ownership: Currently, Kolkata and Bangalore are

ranked among the most congested cities in the country. In these cities, on
average 89% of commuters surveyed indicated plans to purchase a car in the
next five years. In Kolkata, where congestion was perceived to be the most
severe among the cities studied, the same respondents showed the highest
likelihood (~52% highly willing) to forgo purchasing a car if ridesharing could
meet their transport requirements on price, timeliness and availability.

•• Optimising infrastructure timing & location: Kolkata and Bangalore have

ambitious plans to expand transport networks, especially by building modern
metro lines. However, roll-out will require considerable time and funding.
Ridesharing can assist governments to ‘right-time’ infrastructure investment,
serving outlying areas where there may be insufficient demand to warrant fixed
asset investments at the moment.

10 Unlocking Cities

 EXHIBIT 4 | Percentage of Respondents Who Plan to Buy a Car Within the Next Five Years

Respondents (%)

120

92 91
90 86 86 88
83 83 84
79 79 81
Ø 77

60 57
51

36
30

0
Delhi Bangalore Hong Taipei Jakarta Kuala Manila
Kong Lumpur
Mumbai Kolkata Singapore Hanoi Ho Chi Surabaya Bangkok
Minh

Source: BCG survey.

EXHIBIT 5 | Willingness for a Planned Car Buyer to Forgo Purchase, Provided Rideshare Meets
Desired Levels of Availability, Price, Timeliness

Respondents (%)
88 89 90
90 85 85 87
81 81 81 82 82 82 Ø 84
79 79
9 18
23 28 37 42 42 Highly willing
30 40
39 45
60 38 41
52

73
68
30 58
51 54 51
47 47 47 Somewhat willing
41 42 40
38
30

0
Delhi Bangalore Singapore Kuala Taipei Surabaya Hanoi
Lumpur
Mumbai Kolkata Bangkok Hong Jakarta Manila Ho Chi
Kong Minh

Source: BCG survey.

The Boston Consulting Group 11

 •• Supplementing incomes: Car owners in these two cities indicated higher
willingness to drive for a ridesharing platform in order to increase their incomes.
Between 27%-37% of car owners here indicated high willingness, while an
additional 40% indicated moderate willingness to drive.

To give a sense of the magnitude of the potential impact, we assessed a hypothet-

ical scenario where ridesharing, including pooling, fully substitutes private car us-
age in each city. For instance, in Delhi, this would mean that ridesharing becomes
50% of total kilometres travelled by motorized vehicles.

Under this scenario, ridesharing substantially reduces the number of vehicles re-
quired due to the higher people-kilometres ratio provided by each rideshare vehicle
as compared to private cars. Congestion would also decline significantly as a result
of the reduction in the number of cars trips. Furthermore, significant space, total-
ling as much as 33,000 acres, could be re-purposed from vehicle parking.

Vehicles other than private cars

While private cars have the highest modality share among private vehicles by pas-
senger-KM a wide variety of vehicles are used across the cities surveyed.

Auto-rickshaws: Auto-rickshaws comprise an important part of the Indian trans-

portation system. Their usage is common among commuters due to their conve-

EXHIBIT 6 | Efficiency Gap Between Rideshare Vehicles vs. #1 Common Modes5 of Private Vehicles
(Car or Motorbike)

Average people-KM per vehicle, per annum

3.3x
100,000
1.6x
1.4x 1.5x
80,000
3.4x
1.7x 2.7x

60,000 3.2x
1.3x
1.8x 1.8x
2.0x 1.9x
40,000 1.7x

20,000

0
Bangalore Mumbai Jakarta Taipei Manila Hong Surabaya
Kong
Kolkata Delhi Kuala Bangkok Singapore Hanoi Ho Chi
Lumpur Minh

Privately Owned Vehicle Rideshare Private Motorbike

Source: BCG survey.

12 Unlocking Cities

 EXHIBIT 7 | Percentage of Private Vehicles and Total Vehicles Reduced with Rideshare

(%)

80
71 73
68 70
66
63 63
60
60 56 57 55
53
46 46 46
40 42
39 39
40 35
33 31

22 24
20 15
11
8 8

0
Delhi Bangalore Singapore Manila Jakarta Bangkok Hanoi

Mumbai Kolkata Hong Kuala Taipei Surabaya Ho Chi

Kong Lumpur Minh

% of cars % of total vehicles % of private motorcycles

Sources: Government statistics; BCG analysis.

1. With rideshare scenario under which ridesharing replaces private cars as the #2 or #3 mode of transport in respective citi es and pool constitutes
50% of rides 2. Total number of vehicles includes private cars, motorcycles, buses, taxi and rideshare cars, 3. Total number of cars include private
cars and ridesharing cars.

EXHIBIT 8 | Road Congestion During Peak Hours Before vs. After Rideshare (2017)

(%)

200
-19%
-17%

150 -27%
-31%

100

50

0
Delhi Mumbai Bangalore Kolkata

Private Car Rideshare

Source: BCG analysis.

Note: Reductions in congestion based on high-adoption, high-pooling scenario.

The Boston Consulting Group 13

EXHIBIT 9 | Estimated Reduction in Avoidable Social Cost of Congestion as a Result of Rideshare
Adoption
Avoidable Social Cost of Congestion (USD Billion)

10

8 2.98

6
1.01

4 1.30
6.62
4.91
2 3.50 0.37
1.60
0
Delhi Mumbai Bangalore Kolkata

Congestion
31% 27% 17% 19%
Reduction

Potential Cost Reduction Remainder

Source: Centre for Urban Economic Studies, Department of Economics, University of Calcutta, Kolkata, India; BCG analysis.

EXHIBIT 10 | Estimated Space That Can be Saved by Adopting Rideshare Assuming Rideshare
Substitutes for Private Cars
ACRES SAVED WITH RIDESHARE LANDMARK EQUIVALENT

Delhi 22,369 4x Indira Gandhi Airport

Mumbai 4,553 3x Chhatrapati Shivaji Airport
Bangalore 5,446 12x Bangalore Palace
Kolkata 759 19 NiccoPark Theme Park
Singapore 2,155 2x Sentosa
Hong Kong 3,124 67x Victoria Park
Taipei 4,000 197x Botanic Gardens
Kuala Lumpur 23,681 139x Lake Gardens
Bangkok 38,440 273x Lumpini Park
Jakarta 26,308 6x Soekarno-Hatta Airport
Hanoi 8 38 4x Old Quarter
Ho Chi Minh 905 18x Zoo1
Manila 8 ,308 26x EDSA
Surabaya 1,347 1x Tanjung Priok

0 30,000 40,000 Acrres

Sources: ASEAN Maritime Working Group, Data.Gov.Sg, FIFA, MapDevelopers/Google Maps, HDB, HK Census and Statistics Dept., LTA, Manila
Times, Perdana Botanical Garden, URA, Thanhnien News, The Straits Times, Taipei Botanical Garden, expert interviews, BCG analysis.
Note: Size of local landmarks vary greatly between cities. Area represents estimated total flat area of all parking lots (existing and needed) to serve a
city's car population. Area estimated by deriving ratio of cars (private + rideshare) to estimated parking lots in Singapore (~2.2) and then
extrapolating this ratio to car populations in other markets. Assumes standard parking lot (19m2), Area saved under hypotheti cal scenario in which
rideshare becomes displaces private vehicles in terms of modal split and 50% of rideshare is pooling.
1. HCMC Zoo and Garden complex.

14 Unlocking Cities

nience and accessible pricing. Across the four cities, we estimate the share of au-
to-rickshaws to range between 8%-43% of the total passengers-KM travelled by
public transport. This share has the potential to increase in the near future as gov-
ernment continues to issue more permits (e.g. Delhi’s auto-rickshaws population
more than doubled in 2016, reaching ~198,000).6

Like traditional taxis, auto-rickshaw drivers could stand to benefit from working with
rideshare. This benefit is illustrated through the collaboration between Uber and Kaa-
li-Peelis taxis (refer to section “Utilisation of taxi” below for more details). However,
due to the price differences, consumers are unlikely to switch from rickshaws to ride-
share. Thus, we have excluded auto-rickshaws from our quantitative analysis.

Motorbikes: In the cities studied, our model estimates that the share of motorbikes
ranges from the lowest in Mumbai (12%) to the highest in Bangalore (32%), as mea-
sured by proportion of the total KM travelled. While these levels are lower com-
pared to those of private cars, they still comprise a considerable proportion of each
city’s transportation demand. This makes motorbikes a significant contributor to
congestion.

As an extension to the base scenario above, where rideshare is a substitute for pri-
vate cars, we also examine the effects it may have on motorbike population. While
replacing motorbike usage with rideshare adoption is less feasible given their cost
differences, our calculation shows that even if only ~1/3 of the current motorbikes
could be substituted with rideshare in addition to the base scenario above, it could
reduce between 22% to 38% of the total vehicles in each of the cities studied.

Achieving sustained benefits

On a standalone basis, rideshare can act as a more efficient means of transport
than private cars. However, its full benefits may be best realised with a higher By 2022, additional
adoption of rideshare and pooling in conjunction with public transport. rail public transport
adoption of close to
•• Delhi and Mumbai have recently invested in public transport development and 11% of total KM
have ambitions to significantly increase their adoption. In Mumbai however, we travelled would be
estimate that even with the planned expansions and full capacity utilisation of needed to maintain
current and future rail lines, the city may be unable to maintain the current today’s congestion
levels of peak congestion by 2022. For Delhi, the main hurdle is improving level in Delhi
public transport adoption. Given these constraints, according to our estimates,
additional rail public transport adoption of close to 11% of total KM travelled
would be needed by 2022, alongside utilisation of rail capacity, to maintain
today’s congestion levels.

•• Bangalore and Kolkata intend to significantly increase rail- based public

transport capacity. Despite these plans, we estimate that the added capacity of
rail transport alone will not be sufficient to reduce their congestion levels. This
is especially true for Bangalore, where we estimated that the added rail trans-
port capacity still falls below the required level to maintain its congestion levels
by 2022. According to our estimates, additional rail public transport adoption of
close to 21% of total kilometres is essential, in conjunction with public transport,
to maintain today’s congestion levels.

The Boston Consulting Group 15

 The benefits of ridesharing discussed above are supported by recent studies con-
ducted on ridesharing, shared mobility and car-pooling around the world. These in-
clude recent case studies from India.

Benefit 1: Providing alternative to car ownership

Ridesharing and car-sharing have been shown to act as a substitute for car ownership
in some cities. For example, in London, researchers surveyed car-share users and found
that 31% of users declined to purchase a car that they would have purchased oth-
erwise, while 6% of car owners planned to or had recently disposed off a car due
to ridesharing availability.7 By comparison, in Austin, Texas, researchers found that
when Uber and Lyft were temporarily suspended in the city, roughly 40% of those af-
fected switched to a personal vehicle as their primary transport mode and ap-
proximately 9% purchased a vehicle, in response to the suspension.8, 9

In a recent Shared Mobility Survey across six key cities in the US, rideshare users
reported the lowest car ownership levels. On average, 30% of shared mobility
users, including rideshare, reported shedding their vehicles.10

Benefit 2: Reducing congestion through increased efficiency

More passengers per vehicle

A recent study conducted across six US cities, namely Chicago, Los Angeles, Nash-
ville, San Francisco, Seattle and Washington DC, showed that only ~5% of ride-

EXHIBIT 11 | Estimated Public Transport Demand in Relation to Public Transport Capacity in 2022
% KM travelled by public transport

100

8 18 3
80
65 63
61 58 62
60
52 52
46
39 40
40 36 35
30 29
26 23 26 24
19 18 17
20 13
9
2
0
Delhi Bangalore Singapore Taipei Bangkok Manila Surabaya

Mumbai Kolkata Hong Kuala Ho Chi Jakarta

Kong Lumpur Minh

Estimated public transport 2017 % KM travelled by public transport

capacity by 20221 2022 % KM travelled by public transport to maintain current peak congestion
Sources: Government announcement on transport infrastructure master plan; BCG analysis.
1.Capacity is estimated based on current rail network and new rail lines/existing line extensions in operation before 2022 in each city: Thomson East
Coast Line, Downtown line 3 extension for Singapore; total new railway projects equivalent to 25% of current capacity in Hong Kong; Circular Line
stage 1, Anking Line, Danhai LRT, Wanda Line stage 1, Xinzhuang Line extension for Taipei; MRT Line 2 for Kuala Lumpur; 10 new rail lines and 3
existing line extensions for Bangkok; first Metro Line and 3 LRT lines for Jakarta; 6 Metro Rails (total 109 KM) for Ho Chi Minh; 6 new railway lines
(total 246 KM) for Manila; one monorail for Surabaya.

16 Unlocking Cities

share users drove solo, which is 5-6 times lower compared to non-rideshare us-
ers.11 This higher vehicle occupancy is one of the key ways by which ridesharing
could help reduce congestion.

This particular benefit was demonstrated in Jakarta where, in 1992, the government
introduced a policy that required vehicles to carry at least three occupants when
travelling on main routes during peak hours (3-in-1 policy). This policy, however,
lifted in 2016 due, at least in part, to concerns regarding the informal passen-
ger-for-hire (i.e. ‘jockey’) economy that had emerged as a consequence.12

A recent study by researchers at Harvard and MIT universities, found that following
the repeal of this policy, morning and evening congestion on the newly-liberalised
routes leaped by a staggering 46% and 87% respectively. Moreover, not only did
congestion leap on those central Jakarta roads where car-pooling was previously
mandated, it also increased during non-peak hours on routes and in areas that
were never subject to the pooling rule in the first place. In the hour following
the evening peak, for example 19:00-20:00, the repeal of this policy coincided with
a roughly 50% increase in delays.13

The increase in vehicle occupancy through ridesharing could be further enhanced

as there is evidence that pooled rides can become a substantial portion of rideshar-
ing trips. For example, in Southeast Asia, Uber’s pool option represented approxi-
mately 25% of total trips in August 2017.14

Greater vehicle utilisation per kilometre

A common challenge in cities is matching the transport supply with commuter de-
mand to ensure sufficient supply during peak hours, and reducing supply during
off-peak hours, to minimise kilometres travelled without passengers (‘unproductive
miles’). Research in San Francisco indicated that rideshare vehicle ‘unproductive
miles’ is approximately half of taxis (as a percentage of total miles).15 Respon-
siveness to flexible demand allows ridesharing vehicles to be potentially more effi- 43% of shared-mobili-
cient in meeting demand, without adding to congestion, at times of lower demand. ty users reported an
increase in their use
Benefit 3: Accelerating public transport adoption of public transport in
A study published by the National Academy of Sciences, which covered several ma- several major US
jor US cities, found that 43% of shared-mobility users reported an increase in their cities
use of public transport, while only 28% of individuals reported reduced use of pub-
lic transport.16 Where public transport use has increased, the study suggests that
ridesharing is used to complement public transport, and can support a “car light”
lifestyle. This positive effect, however, may be limited due to economic conditions
and timing of travels. For example, a recent shared-mobility study found no clear
correlation between peak-hour use of rideshare and long-term increase in public
transport usage.17

Nevertheless, transit authorities are recognising the potential for ridesharing to act
as a feeder mechanism to public transport. In Portland, Oregon, for example, a local
transit authority (TriMet) has integrated rideshare booking capabilities into its pub-
lic transit app, as a means to enhance intermodal efficiency to public transport hubs.

The Boston Consulting Group 17

Benefit 4: Optimising infrastructure timing & location
Another benefit of existing rideshare models is improved transport coverage of ar-
eas beyond the core metropolitan space. Studies in Manhattan found that outlying
areas were generally better served by rideshare, compared with taxis.18 Ride-
sharing, therefore, supports transport needs where there is less access to public
transport, and can serve as a bridging mechanism for infrastructure development
in outlying areas. In 2016, a US suburb subsidised Uber rides to the local public
transit hub, instead of using the funds to expand parking.

Benefit 5: Supplementing drivers’ income

The introduction of rideshare models has provided a portion of the workforce with
opportunities for additional income. A study in the United States has shown that
close to 80% of Uber drivers are part-time drivers.19 Flexible working hours and
the ability to smooth fluctuations from other income sources are the key rea-
sons for drivers to engage with rideshare companies.20 In Singapore, rideshare com-
panies such as Uber and Grab have also became the preferred choice for part-time
jobs among students.21

Despite the obvious potential benefits of ridesharing, concerns have emerged about
the interaction between ridesharing and other transport modes such as taxi opera-
tors and public transport players. BCG has therefore explored these concerns and
suggested potential solutions. From our assessment, we found that a net positive
outcome can be realised for all stakeholders—ridesharing is not and need not be a
“zero sum” game. For Indian cities to achieve net positive benefits, several condi-
tions must be achieved:

Ridesharing substituting against private vehicles

Ridesharing benefits are obtained by providing greater transport efficiency (peo-
ple-kilometres) compared to private vehicles. However, to provide net positive ben-
efits for congestion, cities must ensure that ridesharing does not cannibalise the
share of public transport. While there is evidence that rideshare can supplement
public transport and support car-light lifestyles (see above), there is mixed evidence
suggesting that ridesharing may act as a substitute for public transport use, under
certain conditions.22 This concern, however, is less acute in India, where reliance on
public transport remains relatively low.

This risk can be mitigated further by rideshare platforms and governments working to-
gether to establish programs that make ridesharing services an appealing complement
to public transport. For example, governments can work with ridesharing platforms to
provide commuters with live inter-modal travel data and to establish discounts or pool-
ing schemes for feeder transport to arterial public transport infrastructure.

Utilisation of taxis
The rise of rideshare has been perceived to reduce taxi ridership in some cities.
However, Ministers in both Singapore and Malaysia have suggested that rideshare
has served as a positive complement to taxis, particularly in peak hours.23, 24, 25

“In the morning peak hours where we have an inadequate supply of full-time taxi drivers,
many of the commuters’ interests are served because there are supplementary drivers that

18 Unlocking Cities

come in the form of Uber and Grab, and these are mostly part time drivers”
Both taxis and private
– Minister of Transport, Singapore vehicles can form part
of the flexible supply
“Instead of seeing the e-hailing companies as the new “taxi industry”, I prefer to look at base necessary to
it in a way where both services can co-exist. Each has its own goals… 59% of e-hailing realise the congestion
application users have never hailed a taxi before this” reduction

– Minister of Transport, Malaysia

Additionally, the emergence of rideshare technologies may have encouraged taxis

to adopt more sophisticated technological advancements such as electronic applica-
tions, dynamic pricing and smart supply-demand matching tools—enhancing their
competitive position and ultimately benefitting commuters. Furthermore, in Syd-
ney, Australia, taxi ridership has grown since the entrance of rideshare, suggesting
that the risk of disruption to taxis is uncertain and market specific.

Governments can also play a role in ensuring that taxi companies improve their com-
petitive position, while offering commuters better outcomes. For example, taxis
should be able to access the same technologies available to ridesharing vehicles. Both
taxis and private vehicles can form part of the flexible supply base necessary to real-
ise the congestion benefits outlined above. In particular, governments should ensure
that taxis can use apps to connect with passengers, while ensuring that taxis can avail
themselves of supply-demand matching mechanisms including dynamic pricing.

Partnerships between rideshare platforms and taxi companies can also benefit taxi
drivers. Recent examples of partnerships between rideshare platforms and taxi
companies include UberTAXI in Taiwan, UberFLASH in Malaysia and Grab’s part-
nerships with multiple Singaporean and Vietnamese taxi companies. These part-
nerships promise to benefit taxi drivers by offering them access to technology
which may allow more responsive matching of supply to demand, thereby increas-
ing vehicle utilisation and ridership. They may also benefit drivers by offering them
access to large networks of potential passengers.

In India, Uber collaborated with Kaali Peelis taxis in Mumbai to pilot usage of the
rideshare app among Kaali Peelis drivers. The sandbox experiment yielded strongly
positive results as drivers using the app were able to record 30% higher efficiency
(in terms of total KMs travelled per hour) compared to the control group, resulting
in 44% income uplift per hour.24

The demand for transport will continue to grow across Asian cities, leading to op-
portunities for incumbent transport models to evolve and for new transport models
to enter—ultimately leading to better transport outcomes for commuters.

Dynamic pricing to effectively match demand and supply

Dynamic pricing has long been used in the airline industry. However, technological
advancement has allowed it to be widely adopted in other areas of transportation
planning, such as highway toll roads and parking fees. Closer to home, the Indian
Railways is also considering a flexi-fare system. Usage of dynamic pricing has been

The Boston Consulting Group 19

 shown to not only effectively regulate demand, but to also improve the utilisation
of resources. For example, within the first 3 months of using dynamic pricing for its
parking space, Los Angeles city recorded an overall decrease of ~11% in hourly
rates, ~2% increase in revenues, ~10% less parking congestion and ~5% more occu-
pancy of previously under-utilised space.26

Within the ridesharing industry, dynamic pricing has been the underlying mechanism
through which driver supply can respond flexibly to variable passenger demand. It also
promotes accessible pricing, since passengers do not need to cross-subsidise vehicles for
their idle time. Nonetheless, as ridesharing continues to evolve globally, its use of dy-
namic pricing, specifically the level of surge imposed, has met with debates.

Published literature on this topic is understandably sparse (considering its emerg-

ing nature and there is not yet conclusive evidence on the exact ways that dynamic
pricing affects behaviour of drivers and commuters). For example, Diakopoulos
(2015) suggested that surge prices draw existing drivers to high demand areas, rath-
er than increase the number of active drivers on the road as usually claimed. Nev-
ertheless, most studies agree on the positive effect that dynamic pricing has in ad-
dressing peak demand while keeping acceptable service levels.27

Hall, Kendrik and Nosko (2015), showed that dynamic pricing helped rideshare, spe-
cifically Uber, in New York City, to effectively match demand-supply while main-
taining required service levels. Comparing similar peak events, when surge prices
worked as intended (e.g. after a sold-out concert in Madison Square Garden), there
was a rapid increase in active drivers within the applicable areas, matching that of
demand (~2x increase). This kept customers’ waiting time at normal level, averag-
ing 2.6 minutes, and 100% ride completion rate.

On the other hand, this efficiency was lacking when surge prices failed (e.g. New
During 26-minute Year Eve 2014-2015 during 26-minute surge pricing outage). The completion rate of
surge pricing outage rides dropped drastically to ~10% at the lowest point (compared to 100% in earlier
the completion rate of example). Waiting time also increased to 6 minutes at peak, more than 2 times the
rides dropped drasti- normal level.
cally to ~10% at the
lowest point Similar patterns were observed in Indian cities when state governments enforced
ceilings for surge prices. For example, in Bangalore, as a result of regulatory reform,
Uber imposed a ceiling on its surge prices starting from the night of May 30th, 2016.
While average demand levels did not differ significantly before and after this surge
ceiling introduction, the average estimated arrival time (i.e. customers’ waiting time
to be picked up) increased for both UberPool and UberGo services (two of its most
popular services by volume of rides) in the subsequent 10 day period. The increase
in waiting time was lower for UberGo, potentially due to its higher volume. Howev-
er, waiting time during high-demand hours (i.e. top 3 hours each day with the most
number of booking requests) increased significantly, by 12% and 15% for UberGo
and UberPool, respectively.

The impact on the level of unmet demand (i.e. level of booking requests unful-
filled) was even more acute. Average level of unmet booking requests during
high-demand hours increased by more than 3 times, in the 10 days after surge ceil-

20 Unlocking Cities

ing was introduced. This staggering increase occurred despite Uber’s efforts to tem-
porarily raise incentives for drivers during that period.

Castillo, Knoepfl and Wey (2017) referred to this situation (longer waiting time and
high unfulfilled requests) as the “Wild-Goose-Chase” (WGC). Due to a lack of drivers
willing to provide services, commuters were matched with those who were far away,
resulting in long waiting times and high cancellation rates. This inefficiency is unfa-
vourable for both drivers (waste of time and income) and commuters (long waiting
time, high cancellation rate and lower chance of being able to book a ride). Using
simulations based on Uber data in Manhattan in 2016-2017, their study showed that
the usage of surge pricing is the best method to maximise total welfare.

The same researchers, explored alternatives for rationing demand and supply, all of
which resulted in undesirable effects on the whole transportation system. For exam-
ple, one way to reduce peak demand was randomly denying riders or denying those
with long waiting time. This was inefficient compared to a pure price mechanism
where drivers match with riders who have the highest willingness to pay. To increase
supply, expanding the dispatch radius was considered, though it would lead to high-
er waiting time, and the possibility of riders being denied a trip during peak hours.

The above evidence, though limited, seems to suggest that dynamic pricing is neces-
sary to enable the full benefits of ridesharing. Restrictions such as surge ceilings ap-
pear to immediately lower these benefits to below optimal for both commuters and
drivers. Hence, while there have been various debates and regulation changes sur-

EXHIBIT 12 | Customers’ Waiting Time Before and After Surge Ceiling Was Introduced

RIDESHARE PERFORMANCE POOL PERFORMANCE

Indexed average hourly ETA

12% Indexed average hourly ETA
+15%
1.15 1.13 1.3
1.22
1.10 1.2
1.14
1.05 1.03 1.1 1.06
1.00 1.01 1.00
1.00 1.0

0.00 0.0
[Before] [After] [Before] [After] [Before] [After] [Before] [After]
Mean ETA Mean ETA Mean ETA Mean ETA Mean ETA Mean ETA Mean ETA Mean ETA
(high- (high- (high- (high-
demand) demand) demand) demand)

Before surge price cap After surge price cap

Source: Uber.
Note: Data captures service performance in the period of 10 days before and after May 30th, 2016 9PM, when surge ceiling was introduced in
Bangalore; average level of performance was calculated for each period, and indexed with 1 = average level of performance in the 10 days before
surge prices were capped. High demand hours are defined as the top 3 hours within each day based on total booking request.

The Boston Consulting Group 21

EXHIBIT 13 | Level of Unmet Booking Requests Before and After Surge Ceiling Was Introduced

RIDESHARE PERFORMANCE POOL PERFORMANCE

Indexed level of booking request un-fulfilled Indexed Level of booking request un-fulfilled
during high-demand hours during high-demand hours

5 5

4 4

3 3

2 2

1 1

0 0
Avg. increase 3.1x 3.4x

Before surge price cap After surge price cap

Source: Uber.
Note: Data captures service performance in the period of 10 days before and after May 30th, 2016 9PM, when surge ceiling was introduced in
Bangalore; average level of performance was calculated for each period, and indexed with 1 = average level of performance in the 10 days before
surge prices were capped. High demand hours are defined as the top 3 hours within each day based on total booking request.

rounding this topic in India, we believe that re-examination of current regulations

to allow for more liberal pricing is required in order to maximise public benefits.

In the case of ridesharing, this review and revision process would be most effective
if the government and the industry were to closely collaborate. While state govern-
ment could be more open to reviewing and adjusting current rules, industry players
could supply them with the necessary data enabling effective decision making.

The Path Forward: Realising the Benefits of Ridesharing

Ridesharing has the potential to support the growth in transport needs across Indi-
an cities in a more sustainable and efficient manner than that of private car owner-
ship. However, both the rideshare ecosystem and public sector must play a role to
realise this outcome. Most importantly, more open discussions are needed between
regulators and rideshare industry, to continuously refine current legal frameworks
and fully realize rideshare benefits.

Support needed from public sector

Regulatory restrictions for ridesharing vary across Indian states, with some cities
adopting a more open market position, and others imposing explicit restrictions.
While these regulations reflect differing circumstances across cities, a more open
stance would be needed if ridesharing platforms are to achieve the improved levels
of service that would encourage higher adoption:

22 Unlocking Cities

•• Vehicle supply and driver qualification restrictions: Placing limits on
rideshare may inhibit adoption as ridesharing platforms may be unable to meet
the levels of availability and timeliness that will encourage adoption and drive
substitution of private vehicles. For example, driver licensing and permit
regulations could be more consistent and streamlined among states. The July
2017 Supreme Court judgement has ruled that drivers with Light Motor Vehicle
(LMV) license may drive commercial vehicles of 60 LMV class and below.
However, many states still differentiate between private and commercial drivers,
and hence, require a one-year mandatory period for conversion. This can
significantly lower the ability of rideshare services to scale up its driver base and
reach the optimal service level. Besides this long accreditation process, addition- Regulatory restric-
al barriers exist in the form of drivers’ geographical knowledge requirement, tions for ridesharing
and restrictive vehicle requirement. vary across Indian
states, with some
•• Price controls: Price is an important characteristic of the ridesharing model. It adopting a more open
encourages flexible driver supply to meet peaks in demand, which in turn position, and others
impacts rideshare availability and travel time. Price also helps manage commut- imposing explicit
er demand—encouraging commuters to travel outside of peak demand periods, restrictions
whenever possible. While price caps were historically instituted for commut-
er-protection in certain traditional transport models, they also inhibit a key
ridesharing mechanism which helps to dynamically and responsively match
supply with demand.

•• Barriers to application usage: Mobile applications are essential to enable

ridesharing platforms to dynamically match supply with demand and to ensure
that commuters can connect with these services easily. Restrictions on the use
of applications (e.g. pooling feature) can significantly limit the benefits of
ridesharing.

Actions needed from ridesharing ecosystems

Greater willingness for commuters to adopt ridesharing, specifically pooling, is es-
sential to achieve the benefits of ridesharing. Enhanced prices, availability and
more attractive travel times can help encourage adoption.

•• Price: Across the cities surveyed, approximately 82% of non-rideshare users cite
prices as a key deterrent. The majority of respondents indicate that prices must
be at least 25% lower than their current preferred mode of transport to make
them consider pooling.

•• Availability: Rideshare availability is critical to higher adoption as lower

availability can translate to longer wait times or difficulty in securing transport
when it is needed. Lower than desired availability is cited by around 59% of
respondents as a key barrier. Across cities, respondents note that pooling
services must be at least as easily available as their current preferred mode of
transport to encourage them to adopt the method.

•• Travel time: Similarly, 69% of the respondents indicated longer commute times
as being a deterrent.

The Boston Consulting Group 23

Consequently, raising ridesharing service levels is essential to encourage greater adop-
tion. While ridesharing platforms must enhance their pooling products, improved reg-
ulatory conditions can also aid rideshare platforms to achieve their desired outcomes.

Collaboration needed between governments

and rideshare ecosystem
Greater collaboration between government agencies and ridesharing platforms is
needed to encourage modality shifts from less efficient modes of transport (e.g. pri-
vate cars) to ridesharing. This substitution is essential to create net-positive benefits
to congestion. Such collaborations are showing promise in a number of US cities.
For example, Uber has partnered with the transit authorities in Atlanta, Los Ange-
les and Minneapolis, to provide a discount to commuters - using Uber to comple-
ment public transport. Programs such as Guaranteed Ride Home in Washington DC
offer commuters who regularly use pooling (twice a week), reimbursement for
emergency travel outside of peak hours. Collaborations like these can provide im-
portant incentives to commuters to adopt ridesharing in conjunction with public
transport and to maximise the benefits of both transportation networks.

Ridesharing has the potential to positively impact the transport environment across
India. Substantial growth in adoption is necessary in all cities, if benefits are to be
realised on a sustained basis. A combination of improved service offerings from
ridesharing platforms as well as support from state and regional regulators would
be required.

Notes
1. World Bank Database, retrieved January 15th, 2018
2. Based on indexed population and GDP per capita (constant) growth from 1980-2016 for Asian
countries among top 100 GDPs in world
3. Delhi traffic chaos costs Rs 60,000 crore annually, Times of India, 2017
4. World Bank Global Infrastructure Outlook database, retrieved January 20th, 2018
5. Defined by modality share by passenger-km
6. Delhi government statistic, retrieved January 15th, 2018
7. Le Vine & Polak, 2017
8. Hampshire, Simek, Fabusuyi, Di, & Chen, 2017.
9. During this period, other rideshare platforms continued to operate within Austin. The portion of
former Uber and Lyft customers who migrated to these alternative platforms is roughly the same as
the portion that migrated to cars (roughly 40%).
10. Feigon and Murphy, 2018
11. Feigon and Murphy, 2018
12. Anggun Wijaya, 2016; Tempo.co, 2016
13. Hanna, Kreindler, & Olken, 2017
14. Lyft Blog,2015; Uber Data, 2017
15. San Francisco County Transportation Authority and Northeastern University, 2017
16. Shared mobility and the transformation of public transit, Feigon, Murphy, 2016. Shared mobility
defined as public transit, bike sharing, car sharing, ridesharing, and similar modes
17. Feigon and Murphy, 2018
18. San Francisco County Transportaton Authority, 2017; Schaller, 2017
19. Hall and Krueger, 2016
20. Farrell and Greig, 2016a.

24 Unlocking Cities

21. More tertiary students driving for Uber, Grab, The Straits Times Singapore, 2016
22. For example, in the US, according to the National Academy of Sciences study of shared mobility
users referenced earlier, 43% of individuals reported an increase in their use of public transport, while
only 28% of individuals reported using public transport less. However, a study from UC Davis
Disruptive Transportation: The Adoption, Utilization, and Impacts of Ride-Hailing in the United States
(October 2017) suggest that ridesharing may decrease use of bus and light-rail services by 6% and 3%
respectively in several major metropolitan US cities.
23. The Straits Times Feb 2017, Abdullah; 967,000 taxi trips daily in 2013 to 954,000 trips daily last year
(2016)
24. Don’t stop taxi industry from adapting to competition: Ng Chee Meng; Channel News Asia, 2017
25. Both e-hailing and taxi services can coexist’, Liow, The Star, 2017.
25. Kaali Peelis and the rideshare experience, Uber, 2017
26. Diakopoulos, 2015, Hall, Kendrik and Nosko, 2015 and Castillo, Knoepfl and Wey, 2017.
27. Xerox Smart Grid credential, retrieved January 15th, 2018

The Boston Consulting Group 25

Appendix: Detailed Methodology
The focus of this report is to assess the potential benefits of ridesharing on key In-
dian cities. We have conducted both qualitative and quantitative analysis to reach
our conclusions.

Analysis conducted
1. Qualitative research:
The qualitative research conducted as part of this report takes two primary
forms:

ǟǟ BCG survey of commuter sentiments in cities: The objective of these

surveys was to develop an understanding of commuter satisfaction with
existing transportation options, their reasons for using or not using rideshare
and pooling, their likelihood to adopt these methods, the impact of rideshar-
ing on car ownership, and commuter desire to become rideshare drivers.

ǟǟ Literature review of recent ridesharing studies: We studied these to

understand and compare the benefits and key conditions which must exist
for cities to achieve net positive benefits of ridesharing and pooling

2. Quantitative research:
The quantitative research conducted as part of this report was used to model
the potential benefits provided by ridesharing and pooling under different
adoption scenarios

1A. BCG survey of commuter sentiments in cities

Survey methodology
In January 2018, we surveyed commuters across Delhi, Mumbai, Bangalore and Kol-
kata, covering approximately 300 commuters per city. The commuters surveyed
used a wide range of transportation.

Key survey findings

Finding 1: Rideshare represents a relatively small proportion of modality

Our results suggest that, rideshare represents an average of 10% of transport used
in the four Indian cities surveyed. These results support our quantitative assess-
ment that rideshare adoption is in its nascent stages.

Finding 2: Commuters cite price as the strongest factor inhibiting rideshare

adoption

We surveyed current non-users of rideshare, in a bid to understand their key rea-

sons for avoiding this mode of transport. In all cities surveyed, respondents cited
relatively higher price of rideshare when compared to their primary mode of trans-
port, as the strongest reason. At the other end of the spectrum, awareness of ride-
share offerings was seen as the least severe inhibitor to rideshare adoption.

26 Unlocking Cities

 APPENDIX EXHIBIT 1 | Percentage of Respondents Who Use Rideshare

% total transportation mix

25

20 19

15
13 13
11 12
11 11
10
10 9 Mean: 10
8 8
7

5 5
3

0
Delhi Bangalore Hong Bangkok Jakarta Singapore Kuala
Kong Lumpur
Mumbai Kolkata Taipei Hanoi Ho Chi Surabaya Manila
Minh

Source: BCG survey.

APPENDIX EXHIBIT 2 | Reasons Cited for not Adopting Rideshare in Comparison to Respondent
Preferred Mode of Transport

% of respondents
N = 440
100

82
80
70 69
37 59
60
26 25
23
40
29

46 44 44 10 Highly agree
20 37
19 Somewhat agree

0
High prices Safety concern Longer time taken Lack of availability/ Lack of awareness
difficulties in
booking

Source: BCG survey.

The Boston Consulting Group 27

Finding 3: Majority of respondents state that improvements in price, availability
and travel time relative to their current primary mode of transport, are necessary
for them to adopt pooling

In the majority of cities surveyed, two-thirds of current non-users state that they
would be willing to adopt pooling if price, availability or speed worked out to be
better than that of their most preferred mode of transportation.

Key Finding 4: Commuters show high willingness to decrease car ownership if

desired rideshare service levels can be met

BCG surveyed the likelihood of commuters to purchase a car in the next 5 years.
Overall, commuters in all four cities indicated high intentions to purchase, averag-
ing at 87%. This relatively high probability can be attributed to the cities’ increasing
wealth and the fact that cars are considered to be a symbol for success.

We also observed the stated willingness of participants to forgo the purchase of a

car in the event that ridesharing providers achieve their desired levels of service.
The results indicate that, on average, 80% of respondents who previously indicated
plans to purchase a car were either highly willing or somewhat willing to abort
their purchase plans. This pattern is similar to the results that we had observed in
other South East and East Asian cities where congestion is a major concern.

Key Finding 5: Majority of drivers are willing to consider driving for rideshare to
supplement income

While the aforementioned topics primarily concern consumer behaviour as it re-

lates to rideshare, of equal importance is the supply of individuals willing to work
as rideshare drivers and the incentives that drive that behaviour.

Overall, across the cities studied, 69% of respondents either somewhat or highly
agreed with the statement that they would be willing to use their own cars to work
as rideshare drivers. Enthusiasm for driving was highest in Kolkata at 77%, whereas
the level of willingness ranges from 65-69% in the other three cities.

1b. Literature Review

Due to the relatively nascent nature of ridesharing in Asia, BCG reviewed studies
which assessed the impact of ridesharing in markets where ridesharing is more
common. Our review of this literature surfaced both benefits and key conditions
which must be met to achieve net positive benefits. These have been discussed in
detail in the main section of our report under the title “Ridesharing and its key
benefits”.

2. Quantitative analysis
The focus of the quantitative analysis is to assess the impact of ridesharing on road
congestion under different scenarios of rideshare adoption. We define congestion as
the percentage of time difference in travelling during peak and non-peak hours
compared to the time it would take to travel the same distance at posted speed lim-
its. We have assessed peak hours at 7-9AM and 6-8pm.

28 Unlocking Cities

 APPENDIX EXHIBIT 3 | Percentage of Respondents willing to try Pooled Private Hire Cars if
Compared to Their Most Preferred Mode of Transportation...

% of respondents
N = 440
80

69 70
68

60

40

20

0
Up to 25% cheaper Slightly more easily available Slightly faster

Source: BCG survey.

APPENDIX EXHIBIT 4 | Percentage of Respondents Who Plan to Buy a Car Within the
Next Five Years
Respondents (%)

120

92 91
86 86 88
90 83 84
81 83
79 79
Ø 77

60 57
51

36

30

0
Delhi Bangalore Hong Taipei Jakarta Kuala Manila
Kong Lumpur
Mumbai Kolkata Singapore Hanoi Ho Chi Surabaya Bangkok
Minh
Source: BCG survey.

The Boston Consulting Group 29

APPENDIX EXHIBIT 5 | Willingness for a Planned Car Buyer to Forego Purchase, Provided Rideshare
Meets Desired Levels of Availability, Price, Timeliness

Respondents (%)
88 89 90
90 85 85 87
81 81 81 82 82 82 Ø 84
79 79
9 18
23 28 37 42 42 Highly willing
30 40
39 45
60 38 41
52

73
68
30 58
51 54 51
47 47 47 Somewhat willing
41 42 40
38
30

0
Delhi Bangalore Singapore Kuala Taipei Surabaya Hanoi
Lumpur
Mumbai Kolkata Bangkok Hong Jakarta Manila Ho Chi
Kong Minh

Source: BCG survey.

APPENDIX EXHIBIT 6 | Percentage of Respondents Willing to Drive for Rideshare

Respondents (%)
88 89 90
90 85 87
85
81 82 82 82
77 Ø 81
9 18
69
65 66 23 28 37 42 42 Highly willing
30 40
45
60
24 37
26 25

73
68
30 58
51 54 51
45 47 47 47 Somewhat willing
40 40 40 40

0
Delhi Bangalore Singapore Kuala Taipei Surabaya Hanoi
Lumpur
Mumbai Kolkata Bangkok Hong Jakarta Manila Ho Chi
Kong Minh

Source: BCG survey.

30 Unlocking Cities

Road congestion is driven by a set of elements:

1. Travel speed (actual and speed limit)

2. Road capacity in terms of number of total vehicles on the road

3. Traffic volume on road during the defined periods (peak, non-peak hours)

Further details on each metric are below:

Key Findings for 2017 baseline

Road congestion in peak hours among the Indian cities studied averages at 149%,
significantly higher than those of South East Asian and East Asian cities. This
means that, on average, commuters take 1.5 times longer to travel a given distance
in peak hours compared to travel time during non-peak hours.

Public transport adoption, particularly rail, is key to managing road congestion.

However, the share of transportation kilometres conveyed by public transporta-
tion remains relatively small in Delhi and Bangalore. Mumbai and Kolkata have a
higher modality share of public transportation. However, the crippling infrastruc-
ture in these cities coupled with limited capacity to scale, results in high levels of
congestion.

APPENDIX EXHIBIT 7 | Road Congestion Driver Tree

0
Road congestion

f
1 2
Actual drive-speed Post speed limit

f
3 4
Traffic volume on road Road capacity

5 6
Passenger car
# vehicles by type
equivalent conversion

7 8 9
Total people-KM Average occupancy by
Annual KM per vehicle
demand per vehicle type vehicle type

10 11
Total people-KM Modality share per
demand of the city transport mode

Source: BCG survey.

The Boston Consulting Group 31

Appendix Exhibit 8 | Road Congestion Driver Tree Description

Metrics Description Data Source

0
• % of additional travel time on average in peak, non-peak • Tom Tom Traffic Index
Road congestion
hours, when compared to driving at post speed limit • Government statistics

1 • Tom Tom traffic data

• Actual drive speed of vehicles on the road in peak, non- • Google Map API
Actual drive speed
peak hours • Government statistics
• UBER travel data

2
• Government data
Post speed limit • Post speed limits on highways, urban roads per city
• Press search

3 • Academic studies on
Traffic volume • Total traffic measured in passenger car equivalent units Transportation Engineering
on road on the road in peak, non-peak hours • Government statistics
• UBER data

4 • Estimated number of vehicles (in passenger car

equivalent units) that a single lane can throughput by • Expert interviews on typical
Road capacity type of road design throughput per lane
• Highway: 2000 vehicles/link/lane by type of road
• Urban road: 1200 vehicles/link/lane

5
• Number of vehicles by type: private cars, buses, taxi, • Government statistics
# vehicles by type
motorcycles, ridesharing cars and etc. • UBER data

6 • Vehicle units used to convert different types of vehicles • Academic studies on

Passenger car
to standard car unit based on the size/volume taken of a Transportation Engineering
equivalent
vehicle on the road • Expert interviews

7 Total people-KM
• Total distance travelled by the population using each of • Government statistics
demand per vehicle
the modes of transport • Survey
type

8 • Average total kilometers travelled annually per type of

Annual KM • Government statistics
vehicle (private car, taxi, motorcycles and ridesharing
per vehicle • Survey
car)

9 • Government statistics
Average occupancy
• Average number of people in a vehicle per trip • Survey
by vehicle type
• Expert interviews

10 Total people-KM • Total distance travelled by all modes of transport by total

• Government statistics
demand of the city population of the city

11 Modality share per

• % of KMs travelled by each mode of transport • Government statistics
transport mode

Source: BCG survey.

32 Unlocking Cities

 APPENDIX EXHIBIT 9 | Current Road Congestion during Peak Hours across Cities in 2017
%

200

171
162

150
135 132 134
129
112
105
100
79
65 68 70
63 67%
57
50

0
Delhi Bangalore Singapore Surabaya Taipei Bangkok Manila

Mumbai Kolkata Hong Kuala Jakarta Ho Chi Hanoi

Kong Lumpur Minh

Sources: TomTom traffic index; Google API; Uber; Government statistics; BCG analysis.
Note: Asia average taken from average of East Asian cities based on TomTom traffic index.

APPENDIX EXHIBIT 10 | Current Mileage Modality Share by Vehicle Type in 2017

%

100 5 3 4 3 1 3 3 0 1 2 2 1 1 2 2 1
5 5 8 1 3 1 7 9 3
2
6
8
5 15 4 20
12 26
80 26
26 17 23
32 28
26 53 60
60 19 71
22
30 87
54

40 45 80 55
38
54 58 20
47 30 52
20 36 15
19 18 23
13 17
8 9 10
0 2
Delhi Bangalore Hong Taipei Bangkok Jakarta Surabaya
Kong
Mumbai Kolkata Singapore Kuala Ho Chi Manila Hanoi
Lumpur Minh

Ridesharing Taxi Motorbikes Private Cars Public transport

Source: Government statistics; press search; commuter surveys; BCG analysis.

The Boston Consulting Group 33

 While public transportation adoption is strongly linked to reduction of private cars
usage, the efficiency of vehicles to provide transportation is also critical. We define
efficiency based on the total people-kilometres each vehicle supplies per annum.
This metric is driven by the total annual kilometres attributed to each transport
mode, the number of vehicles supporting each transport mode in the city, and the
average occupancy of each vehicle type, which corresponds to the ridership of that
transport mode.

The ability for ridesharing vehicles to provide greater transportation benefits de-
pends on the difference in people-kilometres each rideshare vehicle provides in
comparison to other modes of transport. The figure below compares the estimated
people-kilometres ratio provided by ridesharing vehicles against private cars in
each city. Based on our estimate, ridesharing is between 1.4x-3.3x more efficient
than using a privately owned car.

Assessment of rideshare benefits in 2017

In our study, we quantified the number of vehicles that could be taken off the road
in a scenario where the most widely owned private vehicles were substituted for
ridesharing. For example, in a market where private cars provide the second highest
form of modality and ridesharing provides the fifth highest form of modality, we as-
sessed how many vehicles could be saved if ridesharing became the second highest
form of modality.

APPENDIX EXHIBIT 11 | Average Annual People-kilometers Travelled Per Vehicle Type With Pooling

RIDESHARE VEHICLES VS. # 1 PREFERRED MODE OF PRIVATELY OWNED VEHICLE

(CAR OR MOTORBIKE)
Average People-KM per Vehicle, per annum

3.3x
100,000
1.6x
1.7x
1.2x
80,000 3.4x
1.7x 2.7x

60,000 3.2x
1.3x
1.8x 1.8x
2.0x 1.9x
1.7x
40,000

20,000

0
Bangalore Mumbai Jakarta Taipei Manila Hong Surabaya
Kong
Kolkata Delhi Kuala Bangkok Singapore Hanoi Ho Chi
Lumpur Minh

Privately Owned Vehicle Rideshare Private Motorbike

Source: BCG survey.

34 Unlocking Cities

Under this scenario, ridesharing significantly reduces the number of vehicles re-
quired. In cities where private cars comprise the majority of private transport, be-
tween ~33%-68% of cars can be removed.

Congestion is predicted to decline from the reduction of vehicles due to rideshare

adoption.

Assessing the benefits of rideshare for 2022

To assess the impact of rideshare in 2022, we first estimated the increase in trans-
portation demand between 2017 and 2022. Research by the National Center for
Sustainable Transportation, United States (Circella, Tiedeman, Handy, & Mokhtari-
an, 2015) shows a strong correlation between transportation demand and wealth. It
suggests that on a per capita basis, people tend to travel more as their wealth in-
creases. These results are reinforced by BCG research on economic growth and its
effect on passenger land transportation in Organisation for Economic Co-operation
and Development (OECD) member nations, between 1970 and 2015. Our analysis
suggests an approximate 1:0.75 relationship between annual GDP growth per capi-
ta and annual growth in land transport passenger kilometres.

The increase in travel demand in the city can potentially be met by a greater adop-
tion of public transportation. Nevertheless, we estimate that the required increase
in rail network infrastructure may be greater than the capacity which will be creat-
ed by 2022 in Mumbai and Bangalore.

APPENDIX EXHIBIT 12 | Percentage of Private Vehicles (Car and Motorcycle) and Total Vehicles
Reduced with Rideshare With Pooling
(%)

80
73
71 70
68
66
63 63
60
60 56 57 55
53
46 46 46
40 42
39 39
40 35
33 31
22 24
20 15
11
8 8

0
Delhi Bangalore Singapore Manila Jakarta Bangkok Hanoi

Mumbai Kolkata Hong Kuala Taipei Surabaya Ho Chi

Kong Lumpur Minh

% of cars % of total vehicles % of private motorcycles

Source: Government statistics; BCG analysis.

1. With rideshare scenario under which ridesharing replaces private cars as the #2 or #3 mode of transport in respective citi es and pool constitutes
50% of rides 2. Total number of vehicles includes private cars, motorcycles, buses, taxi and rideshare cars, 3. Total number of cars include private
cars and ridesharing cars.

The Boston Consulting Group 35

APPENDIX EXHIBIT 13 | Road Congestion During Peak Hours, Before and After Rideshare Impact
Under Scenario Where Rideshare Substitutes for #1 Mode of Private Vehicle With Pooling
(%)

200
-19%
-17%

150 -27%
-31%

100

50

0
Delhi Mumbai Bangalore Kolkata

Private Car Rideshare

Source: BCG analysis.

Note: Reductions in congestion based on high-adoption, high-pooling scenario.

APPENDIX EXHIBIT 14 | Estimated reduction in congestion cost with pooling

Avoidable Social Cost of Congestion (USD Billion)

10

8 2.98

6
1.01

4 1.30
6.62
4.91
2 3.50 0.37
1.60
0
Delhi Mumbai Bangalore Kolkata

Congestion
31% 27% 17% 19%
Reduction

Potential Cost Reduction Remainder

Source: Centre for Urban Economic Studies, Department of Economics, University of Calcutta, Kolkata, India; BCG analysis.

36 Unlocking Cities

 APPENDIX EXHIBIT 15 | Estimated % People-kilometres That Must Be Travelled By Public
Transportation to Maintain Congestion Levels vs. Estimated Public Transportation Capacity
% KM travelled by public transport

100

8 18 3
80
65 63
61 58 62
60
52 52
46
39 40
40 36 35
30 29
26 23 26 24
19 18 17
20 13
9
2
0
Delhi Bangalore Singapore Taipei Bangkok Manila Surabaya
Mumbai Kolkata Hong Kuala Ho Chi Jakarta
Kong Lumpur Minh

Estimated public transport 2017 % KM travelled by public transport

capacity by 20221 2022 % KM travelled by public transport to maintain current peak congestion
Sources: Government announcement on transport infrastructure master plan; BCG analysis.
1.Capacity is estimated based on current rail network and new rail lines/existing line extensions in operation before 2022 in each city: Thomson East
Coast Line, Downtown line 3 extension for Singapore; total new railway projects equivalent to 25% of current capacity in Hong Kong; Circular Line
stage 1, Anking Line, Danhai LRT, Wanda Line stage 1, Xinzhuang Line extension for Taipei; MRT Line 2 for Kuala Lumpur; 10 new rail lines and 3
existing line extensions for Bangkok; first Metro Line and 3 LRT lines for Jakarta; 6 Metro Rails (total 109 KM) for Ho Chi Minh; 6 new railway lines
(total 246 KM) for Manila; one monorail for Surabaya.

APPENDIX EXHIBIT 16 | Estimated Space That Can be Saved by Adopting Rideshare Assuming
Rideshare Substitutes for Private Cars
ACRES SAVED WITH RIDESHARE LANDMARK EQUIVALENT

Delhi 22,369 4x Indira Gandhi Airport

Mumbai 4,553 3x Chhatrapati Shivaji Airport
Bangalore 5,446 12x Bangalore Palace
Kolkata 759 19 NiccoPark Theme Park
Singapore 2,155 2x Sentosa
Hong Kong 3,124 67x Victoria Park
Taipei 4,000 197x Botanic Gardens
Kuala Lumpur 23,681 139x Lake Gardens
Bangkok 38,440 273x Lumpini Park
Jakarta 26,308 6x Soekarno-Hatta Airport
Hanoi 8 38 4x Old Quarter
Ho Chi Minh 905 18x Zoo1
Manila 8 ,308 26x EDSA
Surabaya 1,347 1x Tanjung Priok

0 30,000 40,000 Acrres

Sources: ASEAN Maritime Working Group, Data.Gov.Sg, FIFA, MapDevelopers/Google Maps, HDB, HK Census and Statistics Dept., LTA, Manila
Times, Perdana Botanical Garden, URA, Thanhnien News, The Straits Times, Taipei Botanical Garden, expert interviews, BCG analysis.
Note: Size of local landmarks vary greatly between cities. Area represents estimated total flat area of all parking lots (existing and needed) to serve a
city's car population. Area estimated by deriving ratio of cars (private + rideshare) to estimated parking lots in Singapore (~2.2) and then
extrapolating this ratio to car populations in other markets. Assumes standard parking lot (19m2), Area saved under hypotheti cal scenario in which
rideshare becomes displaces private vehicles in terms of modal split and 50% of rideshare is pooling.
1. HCMC Zoo and Garden complex.

The Boston Consulting Group 37

We therefore suggest that rideshare could play a key role in complementing public
transportation, both in accelerating adoption and in serving the residual demand
that public transport infrastructure would not meet independently.

Assessing the broader benefits of rideshare

We estimate that the parking area that could be saved from a reduction in private
vehicles as a result of rideshare (in each city studied), ranges from 759 to 22,369
acres.

38 Unlocking Cities

About the Authors
Vincent Chin is a Senior Partner based in Singapore and is the Global leader of BCG’s Public Sec-
tor practice. He has over 20 years of experience in working with governments and policy makers
globally. You may contact him by e-mail at Chin.Vincent@bcg.com.

Mariam Jaafar is a Partner in BCG’s Singapore office and a member of Singapore’s Committee
on the Future Economy. She is also on the board of GovTech, the agency responsible for implemen-
tation of Singapore’s Smart Nation agenda. She has worked closely with multiple public sector cli-
ents across Asia Pacific on topics related to the digital economy, giving her a unique understanding
of the policy perspectives of the Singapore government. She can be contacted via e-mail at Jaafar.
Mariam@bcg.com.

Suresh Subudhi is a Partner in BCG’s Mumbai office of The Boston Consulting Group. He is the
Global Leader of Infrastructure sector and part of the Industrial Goods and Public Sector practice
leadership team. You may contact him via e-mail at Subudhi.Suresh@bcg.com.

Nikita Shelomentsev is a Project Leader based in Kuala Lumpur. He has over ten years of experi-
ence with consumer and automotive businesses in Europe and Asia Pacific. He has also supported
governments on strategic and change management topics such as innovation, economics and so-
cial development. He can be contacted via e-mail at Shelomentsev.Nikita@bcg.com.

Duong Do is a Consultant in BCG’s Ho Chi Minh City office and Irfan Prawiradinata is an Asso-
ciate in the Jakarta office. They can be contacted via e-mail at Do.Duong@bcg.com and Prawiradi-
nata.Irfan@bcg.com.

Acknowledgements
The authors would like to thank Panagiota Papakosta and the broader BCG GAMMA team for their
traffic congestion analysis, Jamshed Daruwalla, Jasmin Pithawala, Kim Friedman and Pradeep Hire
for design and production assistance and the BCG knowledge teams and Visual Services
department.

For Further Contact

If you would like to discuss this report, please contact one of the authors.

The Boston Consulting Group 39

40 Unlocking Cities
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