Analyzing the factors influencing Indian public transportation: A case of COVID-19
Graduation Thesis Presentation
Sarthak Sahu
181081
Table of contents
• Results and Discussion (overall & dimension-wise, IRM)
• Sensitivity analysis
• Conclusion
Introduction and Background- Pre COVID era
Public vs Private mode
 In 2001, about 18% of the Indian population was dependent
upon the public modes of transportation for daily commuting.
In 2018, this figure grew to 34%.
 Public transportation has been the backbone of the Indian
transportation system with rail, road, air, and water services
(Bank, 2014).
% passenger share
 With 36.5 million passengers daily, the Indian public
transportation system is among the most heavily used
transportation systems in the world.
70%
60%
58%
50%
40%
34%
30%
20%
8%
10%
0%
Personal (2/4
wheelers)
Public Transportation
Shared/Other
Figure 1: Share of Transportation by modes (Statista, 2018)
 According to National Highway Authority of India (NHAI),
65% of total passenger traffic is carried by road transport.
 Buses carry 90% of these passengers and with informal modes
like auto rickshaws, form an essential constituent of road
transportation.
 India has about 1.6 million registered buses owned by
government and private operators (Devulapalli, 2019).
Figure 2: Modal share of different modes in Indian cities
Introduction and Background-Pre COVID era
 The second highest passenger modal share is that of the
Indian Railways (IR). Indian Rail network is the fourth
largest in world with 74,003 coaches, 12,147 locomotives
and carries 23 million passengers daily (Railway, 2019).
 By 2023, Indian Railway prepares to run India’s first high
speed train between Mumbai and Ahmedabad (Mehta, 2020).
 With regards to air transportation, the domestic Air traffic
continued to grow and stood at 144 million passengers in
2019 (Times, 2020).
Figure 3: How Indians go to workplace (Singh, 2016)
 Low cost airlines proved to be more successful than fullservice carriers grabbing a major share in passenger traffic
while the full service carriers continued to fall (Express,
2020).
Figure 4: Share of types of vehicles in India (The Times of India, 2018)
Introduction and Background- COVID-19 era
 90% of the bus operators reported reduced ridership while 81%
reported no ridership at all.
 The number of people carried by Public Road Transport
Undertaking Buses (per day) also fell from 70 million to 4
million while the average daily domestic-cum-international
passengers reduced from 0.55 million to 0.2 million.
 Naik (2020) estimated the losses suffered by Indian aviation
sector to be USD 3-3.6 Billion in June quarter, loss to Indian
Railways to be USD 65000 million during the lockdown and the
Toll revenue to reduce by 6.5 to 8%.
 A survey by the World Bank showed that the bus industry faces a
loss of USD 7 billion each month due to inactivity (Gupta, 2020).
Figure 5: Sales of Automobiles in India in million units (Statista, 2020)
Preferred workplace of Indians during
COVID-19
34%
66%
Work from home
Office
Figure 6: Indian Employee’s preferred workplace during the
COVID-19 (Economic Times, 2020)
Regression analysis for growth in number of vehicles
Type
CAGR
2-wheelers
7.8%
Highest
Commercia 4.7%
l vehicles
3-wheelers
3.2%
Passenger
vehicles
2%
Lowest
Biggest Challenges
 Dealing with the problems of informal modes
 Financial constraints of operators to sustain operations
 Low patronage due to fear of safety
 Lesser number of service availability
 Lack of safe practices
 Increase in fares
World Transport Forum provided an overview of the challenges that transportation operators are currently facing
during the lockdown. These challenges were divided into four main areas related to: operational and service
delivery, financial management, crew management, and fleet management.
Pandemics and Transportation
Over the last 300 years, the world has seen several major pandemics.
 The severity of the Spanish flu is attributed to the modern transportation modes due to
which is spread very quickly.
 Infected passengers and crews of ships and trains helped in increasing the area affected by
the flu.
 Air travel has been instrumental in the spreading of flus like SARS (2002-03) and Avian
Flin (2005) due to its convenience and ubiquity (Luke & Rodrigue, 2008).
 Transportation modes can be considered to be a vector especially in public transit modes.
 Once a global pandemic becomes apparent, the first step taken is ceasing international
travel through air while domestic lockdowns occur at subsequent steps according to the
situation at hand (Neister, 2019)
Measures taken by other countries
 Organizations realized the importance of digital platforms and proper communication channels to schedule
transportation modes to accommodate the demand volatility.
 Working hours to be distributed in a staggered manner to ensure very low peak hour demand.
 Since the occupancy of buses are low, to prevent underutilization, many cities have increased the mobility of buses
in the city area.
 Formulating new tax waiving plans, extending the validity of licenses and other documents like permits, fitness
certificates and insurance papers. Increasing the mobilization of city buses through dedicated lanes, digital
payments and relaxation on permit restrictions to make resources available for buses.
 Hong Kong went ahead and introduced provisions for reimbursing all the regular repair and maintenance costs to
support taxi and bus drivers. Apart from this, for their franchised ferry and bus operators, they created plans for
reimbursing insurance premiums for 6 months.
 USA has allocated an emergency fund of $25 billion of Federal Transit Administration in order to support transit
systems to respond better to the repercussions of COVID-19
 Several cities like Paris, London, Wuhan, Milan, Bogota are seeing this pandemic as an opportunity to promote
new avenues on Non-motorized transportation which is eco-friendly and sustainable. These cities have come up
with low-cost cycling and walking infrastructure and are trying to transform their urban mobility.
Research Objectives

To identify the factors affecting the public transportation system in India during the pandemic and
developing an interrelation framework.

To measure the cause-and-effect influence of each factor and sub-factor.

To prioritize the factors based on weightage and propose implications based on the inferences drawn by
the research.
Research Questions

What are the factors affecting public transportation system in India during the pandemic?

What are the impacts of individual factors on the public transportation in India during the pandemic?

How are these factors interrelated and what is their mutual interdependence?

What is the significance of individual factors in a consolidated decision framework?

What are the implications of the proposed study for practitioners and researchers?
SAP-LAP
analysis
transportation modes.
SAP Analysis

Situation




Lockdowns at various levels and sealing of state
borders to prevent the spreading of the virus.
Organisations in transportation sector are facing
tremendous losses and risks of becoming
vulnerable in the market.
Some public transportation modes have started
but they are operating at very low occupancy and
are facing increased operational costs due to
expense on safety procedures and to maintain the
hygiene of the fleet.
Many people directly/indirectly related to the
industry have lost their jobs.





Government of India,
State governments,
Indian Railways, Ministry of Civil Aviation,
MoRTH,
Organisations in transportation sector, drivers,
Fleet owners, OEMs
Learnings



Formulating




Limitations of reach of public transportation
modes during the pandemic.
Effect of public’s trust on the transportation
modes.
Less trust leads to more use of private
transportation modes.
The funds and technology at hand.
Loss due to inactive fleet.
Dependency of people on transportation sector
directly/indirectly.
Actions
Process
policy
to
restart
the
public
Reducing total available seats in transport modes
due to government rules regarding social
distancing.

OEMs sanitising the fleet properly to reduce the
fear among the passengers in using public
transportation modes and regain their trust.

Reducing the total number of routes to avoid the
containment zones thus hampering interchange
connectivity.
Increased use of E-ticketing/E-payment system
to avoid any physical touch.
Measures to cut down the total costs
LAP Analysis
Actors



Unlocking the lockdown at various levels at
Unlock 1.0, 2.0 etc.
Special Shramik Express trains to move the
migrant workers back to their homes.
Looking for needs of passengers and devising
the routes accordingly.
Many private organisations fired off employees
to cut their costs.


Implementation of strict regulations regarding
the safety and hygiene of passenger.







Performance
Revamping of the Indian public transportation
system.
Proper implementation of regulations can lead to
higher customer satisfaction.
Reduction in use of private vehicles.
Development of a sustainable transportation
system.
Proper usage of available fleet.
SWOT Analysis of Public Transportation System in India














Strengths
Wider Reachability, door to door
services
Cost efficient
Environment friendly
Reduces congestion
Enhances overall mobility
Safer than private modes
Provides lots of job opportunities
Government
Initiatives
like
Bharatmala, PMGSY, UDAN, etc.
Opportunities
Financial support from government,
especially in EV segment
Providing better service options
More vocational, special tours and
packages
Adopting ICT services to enhance
customer experience
Increasing
FDI
and
private
participation
Increasing
demand
due
to
construction of ROWs













Weakness
Substandard Figure in mind-set of
people
Low average speeds
Low inclusivity
Inadequate infrastructure
Shortage of skilled drivers
Weak interchange connectivity
Lack of digitization and updation of
technologies
Negligence towards intermediary
modes like autos
Threats
Vulnerable market conditions
High share of personal vehicles
Poor conditions in Rural areas
Increasing operational costs due to
fuel prices
Increasing pollution level
Low seat occupancy
Reduced number of trips
Shortage of workforce
Increased time lapse at transit stations
Reduced interchange connectivity
Lesser availability of driver necessities
Increased operational costs
Operational
Nationwide lockdown
Frequent Temporary Shutdowns at Various
Levels
Sealing of State Borders Due to Lockdown
Governmental
regulations
Infrastructural
Factor identification
Lack of Passenger’s Confidence in Hygiene of
Public Transport
Fear of Obstruction of Social Distancing
Norms
Reduced Reliability on Public Transportation
from Passenger’s Perspective
Lesser Need for Travel
Behavioural
Financial
Lesser active fleet
Issues with e-ticketing and e-payment
infrastructure
Reduced last-mile connectivity
Declined profits
Increased market risk
Increased expenditure on safety procedures
Increased unplanned expenses
Methodology
Multi-criteria Decision Making (MCDM)
The analysis of factors affecting public transportation in India during the COVID-19 pandemic is a complex
decision-making process as lots of factors require not only quantitative, but also qualitative assessment. Multi
criteria decision making (MCDM) techniques are well known for situations that include conflicting objectives
and presence of numerous factors.
DEMATEL
The Decision-Making Trial and Evaluation Laboratory (DEMATEL) strategy is utilized to discover the causeeffect interdependencies of the factors and subfactors.
The DEMATEL technique helps in analyzing the causal dependency of variables with the use of influential
relation maps
ANP
The analytic network process ANP is a decision finding method which helps us weigh and prioritize the factors.
g-DANP = integration of DEMATEL and ANP using Grey scale
Total Relation Matrix
O1
O2
O3
O4
O5
O6
O7
O8
I1
I2
I3
F1
F2
F3
F4
B1
B2
B3
B4
G1
G2
G3
O1
O2
O3
O4
O5
O6
O7
O8
0.022
0.068
0.012
0.035
0.063
0.055
0.006
0.027
0.086
0.075
0.066
0.039
0.036
0.095
0.028
0.109
0.116
0.115
0.101
0.128
0.118
0.092
0.06
0.029
0.019
0.041
0.034
0.025
0.003
0.044
0.064
0.042
0.024
0.057
0.027
0.052
0.017
0.101
0.069
0.086
0.089
0.111
0.103
0.077
0.043
0.086
0.008
0.021
0.022
0.015
0.032
0.049
0.035
0.037
0.012
0.066
0.059
0.038
0.032
0.071
0.083
0.09
0.057
0.114
0.107
0.07
0.041
0.088
0.02
0.013
0.077
0.045
0.005
0.029
0.036
0.019
0.029
0.045
0.024
0.058
0.013
0.048
0.039
0.046
0.04
0.106
0.098
0.067
0.029
0.077
0.032
0.022
0.011
0.047
0.003
0.029
0.032
0.031
0.034
0.018
0.009
0.026
0.012
0.031
0.03
0.04
0.048
0.106
0.099
0.09
0.039
0.054
0.019
0.036
0.045
0.012
0.005
0.034
0.049
0.048
0.047
0.024
0.023
0.021
0.015
0.042
0.037
0.042
0.036
0.101
0.08
0.08
0.023
0.042
0.056
0.008
0.017
0.019
0.003
0.012
0.061
0.011
0.018
0.021
0.013
0.017
0.017
0.018
0.026
0.025
0.026
0.087
0.088
0.08
0.011
0.044
0.004
0.013
0.02
0.007
0.015
0.009
0.023
0.029
0.012
0.02
0.007
0.076
0.054
0.028
0.039
0.036
0.02
0.049
0.038
0.038
I1
0.068
0.093
0.014
0.034
0.036
0.02
0.004
0.065
0.025
0.049
0.015
0.067
0.029
0.063
0.055
0.094
0.082
0.099
0.079
0.118
0.105
0.065
I2
0.021
0.032
0.006
0.009
0.01
0.017
0.002
0.016
0.014
0.019
0.01
0.02
0.017
0.065
0.029
0.043
0.091
0.088
0.044
0.071
0.059
0.053
I3
0.039
0.056
0.019
0.015
0.046
0.07
0.003
0.052
0.067
0.025
0.011
0.019
0.009
0.035
0.016
0.035
0.042
0.075
0.049
0.101
0.08
0.041
F1
F2
F3
F4
B1
B2
B3
B4
G1
G2
G3
0.085
0.084
0.015
0.068
0.052
0.036
0.005
0.092
0.075
0.073
0.039
0.029
0.049
0.096
0.089
0.115
0.103
0.105
0.103
0.126
0.107
0.099
0.051
0.09
0.017
0.033
0.032
0.028
0.005
0.088
0.045
0.043
0.028
0.082
0.015
0.083
0.083
0.06
0.073
0.071
0.081
0.108
0.107
0.098
0.018
0.032
0.007
0.014
0.017
0.009
0.002
0.026
0.024
0.033
0.017
0.049
0.017
0.022
0.041
0.078
0.071
0.093
0.054
0.03
0.023
0.019
0.021
0.024
0.013
0.023
0.038
0.037
0.019
0.019
0.03
0.044
0.025
0.029
0.039
0.064
0.012
0.059
0.053
0.055
0.049
0.031
0.033
0.036
0.014
0.024
0.007
0.013
0.013
0.011
0.001
0.02
0.026
0.065
0.008
0.047
0.007
0.054
0.015
0.023
0.09
0.093
0.031
0.027
0.025
0.03
0.012
0.019
0.003
0.008
0.009
0.011
0.001
0.01
0.037
0.081
0.02
0.016
0.011
0.054
0.014
0.056
0.024
0.09
0.023
0.035
0.032
0.024
0.031
0.09
0.013
0.059
0.064
0.045
0.006
0.048
0.052
0.068
0.038
0.029
0.025
0.066
0.029
0.086
0.08
0.041
0.043
0.075
0.078
0.062
0.013
0.026
0.007
0.009
0.009
0.005
0.002
0.02
0.018
0.019
0.005
0.01
0.02
0.023
0.011
0.051
0.072
0.04
0.011
0.087
0.076
0.043
0.008
0.01
0.015
0.004
0.004
0.006
0.011
0.005
0.009
0.006
0.003
0.02
0.009
0.01
0.009
0.008
0.029
0.029
0.015
0.011
0.015
0.027
0.006
0.006
0.005
0.016
0.007
0.003
0.001
0.012
0.004
0.018
0.002
0.004
0.002
0.011
0.003
0.015
0.024
0.024
0.01
0.072
0.008
0.022
0.004
0.008
0.002
0.006
0.003
0.007
0.001
0.004
0.013
0.006
0.005
0.009
0.011
0.009
0.003
0.007
0.038
0.01
0.02
0.067
0.062
0.007
Total Influence Matrix of Main Factors (Dimensions)
O
I
F
B
G
O
0.03
0.031
0.034
0.02
0.006
I
0.038
0.026
0.04
0.036
0.007
F
0.033
0.035
0.05
0.027
0.008
B
0.056
0.068
0.077
0.054
0.019
G
0.089
0.077
0.068
0.05
0.032
Influence Among Factors’ Dimensions and Sub Criteria
In the Total Relation Matrix,
We calculate Sum of Rows (R)
And sum of columns (D)
If R-D >0, it is a ‘cause’ factor
If R-D <0, it is an ‘effect’ factor
‘F’ and ‘I’ are the highly influenced
dimensions while ‘O’, ‘B’ and ‘G’ are
influencing dimensions.
G is the most dominant Cause factor while
F is the most affected factor.
B3 is the most dominant cause sub-factor
while F1 is the most affected sub-factor.
Influential Weights in the Stable Matrix
O1
O2
O3
O4
O5
O6
O7
O8
I1
I2
I3
F1
F2
F3
F4
B1
B2
B3
B4
G1
G2
G3
O1
O2
O3
O4
O5
O6
O7
O8
I1
I2
I3
F1
F2
F3
F4
B1
B2
B3
B4
G1
G2
G3
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
0.045
0.035
0.036
0.028
0.023
0.027
0.021
0.019
0.104
0.055
0.07
0.101
0.081
0.048
0.056
0.043
0.042
0.075
0.031
0.023
0.017
0.019
Importance of Weight Computation of Factors Through g-DANP
Local weights
Global weights
Global weight of subfactor/weight of main factor = Local weight
More is weight, higher is the rank and thus it needs higher
priority
Classification of Performance of Factors Affecting Public Transportation
in India during COVID-19 Pandemic
Results and Discussions from DEMATEL
 From (𝑅𝑖 -𝐷𝑖 ) values, 10 sub-factors have been classified into the ‘Cause’ group
and 12 sub-factors into the ‘Effect’ group.
 The ranking for the cause group is G1>G2>G3>B2>B4>B1>F3>B3>O8>I2.
 This result suggests that ‘Nation-wide lockdown (G1)’ is the most causal factor
which affects the public transportation system in India.
 The ranking of ‘Effect group’ is F1>O2>O1>I1>F2>O4>O5>O3>O6>I3>F4>O7.
 Thus, decreased profits, reduced number of trips, and Low occupancy are the
main effects of the pandemic on the public transportation system in India.
Overall cause and effect analysis
The influence analysis of main factors report that Operational factors (O), Governmental regulations (G) and
Behavioural factors (B) fall in the cause category. In contrast, Infrastructural factors (I) and Financial factors (F) fall
in the effect category. This implies that ‘F’ and ‘I’ are the highly influenced dimensions while ‘O’, ‘B’ and ‘G’ are
influencing dimensions
Dimension wise cause and effect analysis
Their relative ranking is Reduced number of trips(O2), Low
seat occupancy (O1), Increased time lapse at transit
stations(O4), Reduced interchange connectivity(O5), Shortage
of workforce(O3), Reduced accessibility(O6), Lesser driver
necessities (O7). Increased Operational Costs (O8) is the only
cause among these sub-factors
Of the three infrastructural sub-factors, two come
under the ‘effect’ category while the third one
comes in the ‘cause’ category. Lesser active fleet
(I1) and reduced last mile connectivity (I3) come
under the effect category with ratings of 2.104 and
1.373. Issues with e-ticketing and e-payment
infrastructure (I2) is a cause factor
Dimension wise cause and effect analysis
Behavioural Factors
0,8
0,7
0,6
R-D
0,5
0,4
0,3
0,2
0,1
0
0
0,5
1
1,5
2
2,5
3
R+D
Three of the four financial factors come under the
effect category which are Declined Profits (F1),
Increased Market risk (F2) and Increased unplanned
expenses(F4). Increased expenditure on safety
procedures and equipment (F3) is the only cause
factor with a value of 1.734. Thus, if we find a way
to reduce the expenditure, the profits can increase
making the transportation system financially stable.
All the behavioural factors fall into the cause
category. The relative order of their
prominence is Reduced reliability on Public
transportation (B3) > Fear of obstruction of
social distancing norms (B2) > Lack of
passenger’s confidence in hygiene of public
transportation (B1) > Lesser need for travel
(B4)
Dimension wise cause and effect analysis
All the Governmental Regulations fall in the cause category. The order of
importance is Nationwide Lockdown (G1) > Frequent temporary shutdowns at
various levels (G2) > Sealing of state borders due to lockdown (G3). The reason
all these sub-factors fall under the ‘cause’ category is because they influence the
other factors mentioned above.
Results and Discussions from ANP
Lesser active fleet (I1),
Reduced profits (F1),
Increased Market risk (F2),
Reduced reliability on Public transportation from passenger’s perspective (B3), and
Reduced last mile connectivity (I3)
are the top five sub-factors affecting the public transportation system in India during the
pandemic.
Sensitivity Analysis
Sensitivity analysis is a famous practice which determines the stability of the results when the input values are
varied marginally.
Since human decisions constitute the primary inputs of this study, a sensitivity analysis was used to scrutinize
the results.
Since F has the highest priority, we check if variation in value of F will affect the final result.
O
I
F
B
G
Case 1
0.29504
3
0.28873
9
0.1
0.24082
6
0.07439
1
Case 2
0.26222
4
0.25662
1
0.2
0.21403
8
0.06611
6
Normal
0.286
Case 3
0.22940
5
0.22450
4
0.3
0.191
0.18725
0.059
0.05784
2
0.234
0.229
Case 4
0.19658
6
0.19238
6
0.4
0.16046
1
0.04956
7
Case 5
0.16376
7
0.16026
8
0.5
0.13367
3
0.04129
2
Case 6
0.13094
8
0.12815
0.6
0.10688
5
0.03301
7
Case 7
0.09812
9
0.09603
2
0.7
0.08009
7
0.02474
2
Weights Assigned to Main Factors during Sensitivity Analysis
Case 8
0.06531
0.06391
4
0.8
0.05330
9
0.01646
7
Case 9
0.03249
1
0.03179
7
0.9
0.02652
0.00819
2
Sensitivity Analysis
Cas
O1
e
After case 3, the rankings
start stabilizing
O2
O3
O4
O5
O6
O7
O8
I1
I2
I3
F1
F2
F3
F4
B1
B2
B3
B4
G1
G2
G3
1
5
9
8
11
14
13
17
18
1
4
3
12
16
22
21
6
7
2
10
15
20
19
2
3
7
11
10
15
17
16
19
20
1
5
3
4
6
14
12
8
9
2
13
18
22
21
4
9
13
12
15
17
16
19
20
1
7
5
2
3
8
6
10
11
4
14
18
22
21
5
9
13
12
15
17
16
19
20
2
7
5
1
3
8
6
10
11
4
14
18
22
21
6
9
13
12
15
17
16
19
20
3
8
7
1
2
5
4
10
11
6
14
18
22
21
7
9
13
12
15
17
16
19
20
5
8
7
1
2
4
3
10
11
6
14
18
22
21
8
9
13
12
15
17
16
19
20
5
8
7
1
2
4
3
10
11
6
14
18
22
21
9
9
13
12
15
17
16
19
20
5
8
7
1
2
4
3
10
11
6
14
18
22
21
10
9
13
12
15
17
16
19
20
5
8
7
1
2
4
3
10
11
6
14
18
22
21
9
13
12
15
17
16
19
20
5
8
7
1
2
4
3
10
11
6
14
18
22
21
Conclusions
1. For betterment of the public transportation sector, the policy makers would have to pay more attention to the ‘cause’ factors to
reduce the influence of the ‘effect’ barriers.
2. The study reveals that governmental regulations are the key ‘cause’ factors which affect the public transportation sector in
India. Relaxation in governmental regulations like easing inter-state connections and lockdown norms can play a crucial role in
the upliftment of the public transportation sector in India.
3. By going through the factor interrelationships, we observe that reduced reliability on public transportation from passengers’
perspective (B3) is the prime cause factor. Transportation operators must take steps to gain passengers’ trust on public
transportation as behavioral factors have high influence on public transportation.
4. The study reveals that Reduced profits (F1) is the most affected factor. This is due to increased operational expenses. Decisions
taken to reduce overheads and operational costs will play a crucial role towards mitigating this externality.
5. The paper presents a general framework for all the organizations in the transportation sector. The same framework with
modified initial values can be used to provide ranking list for specific organizations for their specific needs.
6. In terms of Triple Bottom-line (TBL) approach, it is expected from industries that they not only focus on Profits, but also on
People and Planet.
References
1.
Abikova, J. 2020. Application of fuzzy DEMATEL–ANP methods for siting refugee camps. Journal of Humanitarian
Logistics and Supply Chain Management, 10, 347-369.
2. Ahmad, T. & Chang, J. S. 2020. Lessons learned from the experience of Indian policies towards sustainable transport
systems. Transportation Research Procedia, 48, 2999-3018.
3. Alam, M. A. & Ahmed, F. 2013. Urban Transport Systems and Congestion: A Case Study of Indian Cities. Transport
and Communications Bulletin for Asia and the Pacific, 82, 33-43.
4. Alejandro Tirachini, O. C. 2020. COVID-19 and Public Transportation: Current Assessment, Prospects, and Research
Needs. Journal of Public Transportation, 22.
5. Arvind Kumar, T. P. S. 2020. Covid-19 and its Impact on India's Transport Sector. International Transport Forum
[Online]. Available: https://www.itf-oecd.org/sites/default/files/docs/covid-19-impact-transport-sector-india_0.pdf
[Accessed 4 June 2020].
6. Bank, W. 2009. India Transportation. Available: https://www.worldbank.org/en/news/feature/2011/09/23/indiatransportation [Accessed 5 November 2020].
7. Bank, W. 2010. The World Bank Annual Report 2010 : Year in Review. World Bank 2010a [Online]. Available:
https://openknowledge.worldbank.org/handle/10986/5906 License: CC BY 3.0 IGO [Accessed 20 January 2021].
8. Bank, W. 2014. The Transport Sector in India. Available: https://www.worldbank.org/en/country/india/brief/transportsector-india [Accessed 19 October 2020].
9. Bernal, L. 2016. Basic Parameters for the Design of Intermodal Public Transport Infrastructures. Transportation
Research Procedia, 14, 499-508.
10. Büyüközkan, G. & Güleryüz, S. 2016. An integrated DEMATEL-ANP approach for renewable energy resources
selection in Turkey. International Journal of Production Economics, 182, 435-448.
11. Büyüközkan, G., Güleryüz, S. & Karpak, B. 2017. A new combined IF-DEMATEL and IF-ANP approach for CRM
partner evaluation. International Journal of Production Economics, 191, 194-206.
Thank You