COVID-19's Impact on Mass Transit Ridership

EXPLORING THE EFFECT OF COVID-19 ON MASS
TRANSIT SYSTEM
Guide:
Mr. VISHWAS JS
Asst Professor
Dept of Civil Engineering,
PES University
PARVATI P NAYKODI ALLE VENUGOPAL REDDY
SRN: PES1UG19CV035 SRN: PES1UG19CV007
VENKATESH TUMKURKAR PRATHIK GOWDA KS
Capstone Project Presentation : Phase 2
Review-4
1

DOMAIN AREA OF THE PROJECT
INTRODUCTION
SCOPE
LITERATURE REVIEW
SUMMARY OF LITERATURE REVIEW
OBJECTIVES
PROBLEM STATEMENT
METHODOLOGY
SURVEY CONDUCTED
POWER BI ANALYSIS
ANALYSIS AND RESULT
CONCLUSION
REFERENCES
OUTLINE
2

DOMAIN AREA OF THE PROJECT
3
TRAFFIC SYSTEM

• The COVID-19 pandemic had a rapid and significant impact on mobility.
• It is mandatory to explore the effect of the pandemic on changes in travel behaviour in post-
COVID-19 times.
• It adopts an online survey from respondents were asked to report changes in travel during the
various stages of the pandemic.
• After survey we can understand how the people changes there choice of mode of
transportation, Changes in food chain management.
INTRODUCTION
4

• By comparing the data we got to know the changes taken in mode of transportation and
frequency of travel etc
• By plotting graphs we clearly understand the whole changes that have taken after covid-
19
• So after survey plotting graph was easy to understand the whole changes that have taken
,and graph helps to take measures in advance
5
INTRODUCTION CONT.

6
SCOPE OF THE PROJECT
• To explore the effect of pandemic on changes in travel behavior in post covid-19 times
• Understanding the impact of mobility pattern of individuals before pandemic and after pandemic
studies
• Impact of mass transport systems on efficient usage and operation during post covid times

SI NO TITLE YEAR AUTHOR PUBLICATION
1
POST COVID -19 TRAVEL
BEHAVIOUR PATTERNS
:IMPACT ON THE
WILLNGNESS TO PAY OF
USERS OF PUBLIC
TRANSPORT AND SHARED
MOBILITYSERVICES IN SPAIN
2021
Samir Awad-Nunez, Raky
julio, Juan Gomez and
julian sastre gonzalez
Springer nature
publications
2
THE EEFECTS OF COVID-19
ON THE TRANSPORTATION
SECTOR IN
LOUISIANA:LOOKING BACK
AND MOVING FORWARD
2021
Cody nehiba
Louisiana state
university
LITERATURE REVIEW
7

3
EVIDENCE OF A POST-COVID
CHANGE IN TRAVEL
BEHAVIOUR SELF-REPORTED
EXPECTATIONS OF
COMMUTING IN
MELBOURNE
2021
Graham currie, Taru Jain,
Laura Aston
Elsevier publications
4
IMPACT OF COVID-19 ON
TRAVEL BEHAVIOUR,
TRANSPORT, LIFESTYLES AND
RESIDENTIAL LOCATION
CHOICES IN SCOTLAND
2021
Lucy Downey, Achille
Fonzone, Grigorios
Fountas and Torran
semple
Edinburgh Napier
University
LITERATURE REVIEW CONTINUED
8

9
09-12-2022 9
SI
NO
TITLE YEAR AUTHOR PUBLICATION
5
MASS TRASIT POLICY:
RESPONDING TO COVID-19
2021
Susan E. Baer, George R.
Larkin
Scholarworks.walden.ed
u
6
IMPACT OF COVID-19
PANDEMIC LOCKDOWNON
THE PUBLIC TRANSPORT
SYSTEM
2021
Saladi S.V. Subbarao,
Raghuram Kadali
link.springer.com

7
URBAN MOBILITY IN DELHI 2021
Palak Thakur, Promit
Mookerjee, Aakansha Jain,
Aravind Harikumar
The Energy and
Resources
Institute(TERI)
8
URBAN MOBILITY IN INDIAN
CITIES
2020
Ramit raunak, Nishanth
Sawant, Dr. Shalini Sinha
Elsevier publications
10

LITERATURE REVIEW
11
1 Samir Awadh-Nunez, Ray Julio, and Juan Gomez sastra Gonzalez.
• This paper investigates people's willingness to utilize and pay for using public transportation
and shared mobility services given a set of covid-19 safety measures to be applied after the
lockdown. They examined the impact of covid-19 on the mass transit system.
• Face-to-face focus groups or interviews were not possible during lockdown conditions due to
the specific circumstances, so the necessary information was gathered via an online survey,
During the two weeks that the poll was open for responses, 984 participants provided accurate
answers.

LITERATURE REVIEW
12
2. Cody Nehiba:
• This paper is about how, when, and where people travelled. Vehicle miles travelled have almost fully
recovered after falling by approximately 40% in April 2020 compared to April 2019. Though it is still
rising, mileage is still below pre-pandemic levels and might not return to them anytime soon. Even though
mileage is lower than long-term trends, it is still higher than in recent months, which has raised fuel
demand and costs.
• Although numbers for air travel, tourism, and vehicle travel have been rising, they are still consistently
lower than they were before the outbreak. Since the pandemic made it abundantly evident that the future
can be difficult to forecast, the sector has faced issues as a result of the sudden increases in flight
passengers over the summer of 2021.

LITERATURE REVIEW
13
3. Susan E. Baer, George R-Larkin. 2.5 Susan E. Baer, George R-Larkin.
• In order to build a public transportation policy for rail and bus networks that responds to COVID-
19 and beyond, this article sought to identify and address public health, technology, economic, and
political concerns as being most pertinent. The study discussed how the pandemic significantly
impacted public transportation organizations' ability to generate their own revenue, especially the
steep drop in ridership. Inequity in public transportation was another political issue that was
discussed in this essay.
• Public transportation is disproportionately used by low-income people and people of color, and this
inequality has grown even more worrying since the outbreak. Policymakers tasked with creating an
egalitarian and effective public transportation system may find the four considerations mentioned in
this paper instructive.

LITERATURE REVIEW
14
4. Saladin S.V. Subbarao, Raghuram kadali.
• This essay examines the COVID-19 pandemic's impact on the PT system and the post-
lockdown procedures put in place by several nations. The report also offers a strategy for
swiftly returning the PT system to normal after a lock-down by suggesting a smooth relaxing
process. The COVID-19 pandemic has had a major impact on the PT system, affecting ridership
and revenue. The post-lock-down PT system scenario varies according on the nation.
• The development of public data for screening, a strategy for the operation of public
transportation, control measures at stations and on vehicles for passenger and crew safety,
sanitation of the public transportation system, and increasing ridership are just a few of the
significant measures that this paper recommends a methodology to address.

LITERATURE REVIEW
15
5. Graham Currie, Taru Jain, and Laura Aston.
•This study offers evidence that post-pandemic travel behavior may differ from pre-pandemic
travel. It implies that although public transportation usage will increase after the pandemic, it won't
reach its pre-pandemic levels. A post-pandemic decline in transport commuting of about 20% is
anticipated. Secondary evidence from a variety of international cities is used to support this impact.
The results suggest a mode shift from public transportation to driving, which will be especially
significant in downtown and CBD regions. As mandatory WFH is replaced with voluntary WFH,
this is expected to cause peak time traffic congestion after the pandemic. However, it is
recommended that WFH continue to rise over pre-pandemic levels in the future, acting to reduce
after the pandemic; this will cut peak commutes by 6% and commutes to the Melbourne CBD by
20%.

LITERATURE REVIEW
16
6. Lucy Downey, Achille fanzine, Grigoris founts and Torran Semple.
• In the current experiment, 994 people of Scotland were given access to an online survey in
order to determine their travel preferences, attitudes, and behaviors throughout the various
pandemic outbreak phases as well as their predicted travel behaviors after the pandemic. To
guarantee that the sample was representative of the Scottish population as a whole, quota
restrictions were put in place for age, gender, and family income.
• Following the implementation of COVID-19, changes in "life satisfaction" and mode of
transportation were assessed, along with changes in risk perception, trust in information
sources, and compliance with COVID-19 requirements. In addition, survey results were used to
determine

LITERATURE REVIEW
17
7. Palak Thakur, Promit Mookerjee, Aakansh Jain, and Aravind Harikari.
• This study tries to comprehend how urban transportation has changed as a result of the disruption
in passenger and freight demand following the covid-19 outbreak. An investigation on people's
perceptions of travel behavior was conducted via a survey because there were few secondary data
sources accessible and there was uncertainty at the time. In order to better understand people's
perceptions on travel behavior, the survey's target group was the urban population of India.
• Along with demographic questions about their city of residence, income, and education, the study
asked them about their habits surrounding business travel, online grocery buying, and food
delivery. Then it asked them specifically if they will make different decisions once the lockdown
is removed.

LITERATURE REVIEW
18
8. Ramit raunak, Nishanth Sawant, Dr. Shalini Sinha.
• This study makes an effort to illustrate how COVID-19 has an impact on several facets of
mobility. According to the perception survey, post-COVID-19, there will likely be a notable shift
away from public transportation in favor of walking, cycling, or individualized modes, at least in
the short term.
• The respondents have rated a variety of doable actions to resolve their worries about using public
transportation

• The results of this research might help operators deploy strategies to adopt their services and
retain users.
• Three scenarios have been used to model transport in the second half of March 2020, more
precisely in weeks 12 and 13.
• Public transport experienced so far the greatest reduction in demand (80%), while cycling and
bike sharing saw the lowest decrease (23% and 2%, respectively).
• Shift to use of private vehicles, intermediate public transport like taxis, auto, bike.
SUMMARY OF LITERATURE REVIEW
19

• To study and compare the impact caused on micromobility pattern.
• To study and compare the impact caused on supply and chain management network
• To study and compare the impact on change of transit behaviour pattern.
• To study and compare the mobility behavior on supply chain management FMCG products based
on income.
OBJECTIVES OF PROJECT

Ideal scenario of the project :
• Efficient use of urban transportation network in the selected zone of study without any disruption
to the supply and chain management of goods ,various transport mode facilities for the passengers.
Real scenario of the project:
• The change in the urban transportation network based on the pandemic effect, which has affected
the supply and chain management of goods, travel behavior of passengers, for various
transportation facilities
PROBLEM STATEMENT

22
1.Feedback study
a. Collection of data through online mode and offline mode
b. Analysis of data using power bi Microsoft tool
c. Comparison based on social feedback
METHODOLOGY

• We did survey for students, selected area is PES UNIVERSITY and near by areas around 5 to
10 km.
• In first review we have collected 927 responses from students
23
SURVEY CONDUCTED
https://docs.google.com/forms/d/1r7m8fub2av7JAW_OvYDV8
3OtgrK9lBgyrkxbg-
5hD5U/viewform?ts=62c2f5d5&edit_requested=true

• We did survey for workers also, selected area mainly in majestic and other near by areas
• we have collected 400 responses from workers
24
SURVEY CONDUCTED
https://docs.google.com/forms/d/e/1FAIpQLScMqcZjsv69yGYK
rK5BjOsa_HuxjszE90Zn-Yd1TXhL8E_64w/viewform

• As we said we are using power bi for plotting graph
• Power bi: It is a Microsoft latest BI tool
• It is mainly aimed to help everyone analyze and visualize their data
• Steps to plot a graph in power bi:
1 Import data from google form to excel sheet and from excel sheet to power bi by using option
‘get data’
2 It has an option called load or edit data in query editor
3 Create and modify the a simple visuals
4 save your report
25
POWER BI ANALYSIS

• Data visualization : pictorial or graphical representation of information or data ,it provides
insight into complex data sheets by communicating the key aspects in a more intuitive and
meaningful way
• Components of power bi
1 power query
2 power pivot
3 power view
4 power map
5 power bi services
6 power bi work book
26
POWER BI ANALYSIS

• Power pivot: Use this for data modelling for instance of Microsoft analyst a services tabular that is
embedded directly into your workbook it enables to import millions of rows of data from multiple
data sources into a single
• Power bi work book :it helps to create relationships between data and create calculated columns
and measures using build pivot tables and pivot charts and further analyze the data
• Power view : It is a data visualization technology that lets you to create interactive charts ,graphs
maps and other visuals that bring your data to life now power view is available in power bi, excel
and other analyst services from Microsoft
27
POWER BI ANALYSIS

• Power map : It is also another feature in excel it is for exploring map and time based data in it
• Power bi services : It helps for collection of apps dashboards and reports built to deliver
• Dashboards: it is single page interface that uses the most important elements of a report to tell a
story
• Tiles: A tile is a single visualization found in a report or on a dashboards
28
POWER BI ANALYSIS

ANALYSIS AND RESULTS
• We have collected 927 responses from students through both online and offline platform, in this we
have considered 893 responses from students and we have collected 400 responses from workers
like bus drivers, auto drivers etc. In that we considered 377 responses of workers.
29

30
Based on the graph it was observed that
64.5% of the people were used to travel more
then 5 days a week around 11.76% of people
were used to travel once or thrice a month
,9.74% of the people travelled once or twice a
week and same percentage of people were
used to travel 3 to 4 days a week and
remaining 4.26% of people haven’t travelled.
Based on student survey graphs were done by using power bi:
1a. Number of samples versus frequency travel before covid-19

1b. Number of samples versus frequency of
travel during covid-19
31
Based on graph it was observed that
34.15% of the people were used to travel 1
to 3 times a month, 24.75% of people were
used travel 1 or 2 days a week, 22.84% of
people never travelled ,22.84% of people
were used travel 3 to 4 days a week and
6.49% of people were used travel 5 or more
days a week.

32
• .
• Based on graph 41.88% of people were used
to travel 5 or more days a week, 20.49% of
people were used to travel 3 or 4 days a week
,15.23% of people were used travel once or
thrice a month,12.77% of people were used to
travel 1or 2 days a week, and remaining
9.63% of people never travelled.
1c. Number of samples versus frequency of travel after covid- 19

33
• .
1d. Number of samples versus frequency of travel before, during, after covid- 19

34
Based on the graph before covid-19, 272 of
people were used to travel through own
car/bike, 190 people were used to prefer public
bus, 182 were prefer metro, 114 were used to
travel through bicycle ,62 people were used to
prefer taxis, 32 people were used to travel
through train, and remaining 41 people were
preferring for walking.
2a. Number of samples versus mode of transport before covid-19

35
Based on the graph during covid-19 ,407 people
were used to prefer own car/bike ,187 people
were used travel through bicycle, 120 people
were used prefer walking ,81 people were used to
travel through taxis, 43 people were used to
travel through metro, 42 people were used to
travel through bus ,13 people were used to travel
through train.
2b. Number of samples versus mode of transport during covid-19

36
Based on the graph after covid-19 ,321
people are preferring to travel through a
mode of own car/bike, 144 people were
used travel through bus, same people were
used travel through bicycle, 88 people used
to travel through taxis, 30 people were used
to travel through, 42 people were used
prefer for walking
2c. Number of samples versus mode of transport after covid-19

37
2d. Number of samples versus mode of transport before, during, after covid-19

38
3a. Number of samples versus mode of
transport before covid-19
3b. Number of samples versus mode of
transport during covid-19

39
3c. number of samples by mode of transport after covid-19
 Based on comparing the before, during and
after covid-19 graphs, more than 200 people
were used prefer own car/bike -before covid-
19, more than 400 people were used own
car/bike- during covid 19, and after covid-19
more than 300 people were using own
car/bike, so here we can observe that due the
pandemic most amount of the people is
shifting to use of private vehicles.
 Bus, metro is public transport, based on
graphs we can observe that before covid-19
more than 150 people, less than 50 people
during covid-19 and less than 150 people were
used travelled through public transport

40
4a. Number of samples versus frequency of travel before covid-19
Based on graph before covid-19
,576 people used to travel 5 or more
days a week, 105 used travelled
once or thrice in a month, 87 people
were travelled 1or 2 days a week,
same number of were travelled 3-
or 4-days week and remaining 38
people never travelled

41
4b. Number of samples versus frequency of travel during covid-19
Based on graph during covid-19 ,305
people used travelled once or thrice in a
month,221 people used to travel 1or 2 days
a week, 204 people never travelled, 105
people were travelled 3 or 4 days a week
,58 people were travelled 5 or more days a
week

42
42
42
4c. Number of samples versus frequency of travel after covid-19
Based on graph after covid-19, 374 people
were travelled 5 or more days a week,183
people used travel 3 or 4 days a week, 136
people used travel once or thrice in a
month, 114 people were travelled 1or 2
days a week, and remaining 86 people
never travelled.

43
43
43
43
• Based on graph before covid-19 the
monthly income of 277 people was
high, for 68 people is low, and 32
people don’t had any changes.
 Based on workers survey graphs were done by using power bi:
5a. class of vehicles versus monthly income per week before covid-19

44
44
5b. class of vehicles versus monthly income per week during covid-19
Based on graph monthly income of 339
people were decreased, 33 people don’t
have any change in income, remaining 5
people monthly income is increased during
covid-19.

45
45
• Based on graph monthly income of
190 people were decreased ,124
people monthly income increased,
monthly income of 63 people
doesn’t have any changes after
covid-19.
5c. class of vehicles versus monthly income after covid-19

46
46
• Based on graph before covid-19 ,69.76%
of ordering food through online is high
,16.18% is less, and remaining 14.06% of
ordering food through online is neither
high nor low means doesn't have any
changes
6a. monthly income per week before covid-19 versus ordering food online before covid-19

47
6b. Monthly income per week versus ordering food through online during covid-19
Based on graph during covid-19 ,89.66%
people ordering food through online is
low,6.9% of is no change , and remaining
3.45% of people ordering food through
online is high

48
6c. Monthly income versus ordering food through online after covid-19
Based on graph after covid-19
,48.01% of ordering food through
online was high ,34.48% was low
and remaining 17.51% was neither
high nor low means doesn't have
any changes in ordering food
through online.

49
7a. count of class of vehicle versus monthly income per week before covid-19
Based on funnel graph in pre covid-19 monthly
income of vehicle is high for 277 samples, low
for 68 samples, and remaining 32 have no
change in monthly income.

50
7b. count of class of vehicle versus monthly income per week during covid-19
Based on funnel graph during covid-19 the
monthly income of vehicles is low for 339
samples, high for samples, no change for 33
samples.

51
7b. count of class of vehicle versus monthly income per week after covid-19
Based on funnel graph the monthly income
of vehicles is high for 190 sample, low for
124 samples, no change for 63 samples.

52
7d. count of class of vehicle versus monthly income per week before, during, after covid-19

53
8a. Frequency of travel before covid-19 versus count class of vehicle
• Before covid-19,the frequency of travel of 94
vehicle is o to 100 km/day , for 132 vehicle the
frequency is 101 t0 200 km/day, for 89 vehicles
the frequency is 201 to 300 km/day, for 63
vehicles the frequency of travel is 301 to 400
km/day, for 20 vehicle the frequency of travel is
401 to 500 km/day.

54
8b. Frequency of travel during covid-19 versus count class of vehicles
During covid-19,the frequency of travel of 342
vehicle is o to 100 km/day , for 55 vehicle the
frequency is 101 t0 200 km/day, for 1 vehicles
the frequency is 201 to 300 km/day, no
vehicles were travelled at the frequency 301
to 400 km/day, no vehicles were travelled at
the frequency of 401 to 500 km/day.

55
8c. Frequency of travel after covid-19 versus count of class of vehicle
After covid-19,the frequency of travel of
96 vehicle is o to 100 km/day , for 146
vehicle the frequency is 101 t0 200
km/day, for 89 vehicles the frequency is
201 to 300 km/day, for 51 vehicles the
frequency of travel is 301 to 400 km/day,
for 16 vehicle the frequency of travel is
401 to 500 km/day.

56
CONCLUSION
1.To study and compare the impact caused on micro mobility pattern.
 In the micro mobility pattern the use of own cars/bike, taxis, bicycles. before covid-19 the 272 people
used to travel through own cars/bike, during pandemic 407 people, and after pandemic 321 people
prefer for own car/bike
 Before pandemic 62 people used to travel through taxis, during pandemic 81 people and after
pandemic 88 people used travel through taxis. This indicates that there were increase in use of own
personalized vehicle after pandemic because of safety purpose.
 Bus and metro come under public transport, before pandemic 190 people were use travel through bus,
during pandemic 42 people, after pandemic 144 people used to travel through bus.
 Before pandemic 182 people used travel through metro, during pandemic 43 people and after
pandemic 144 people were travel through metro. this indicates that people are shifting from public
transport to personalized own vehicles after pandemic.

57
 Before covid-19,the frequency of travel of 94 vehicle is o to 100 km/day , for 132 vehicle the frequency
is 101 t0 200 km/day, for 89 vehicles the frequency is 201 to 300 km/day, for 63 vehicles the frequency
of travel is 301 to 400 km/day, for 20 vehicle the frequency of travel is 401 to 500 km/day.
 During covid-19,the frequency of travel of 342 vehicle is o to 100 km/day , for 55 vehicle the
frequency is 101 t0 200 km/day, for 1 vehicles the frequency is 201 to 300 km/day, no vehicles were
travelled at the frequency 301 to 400 km/day, no vehicles were travelled at the frequency of 401 to 500
km/day.
 After covid-19,the frequency of travel of 96 vehicle is o to 100 km/day , for 146 vehicle the frequency
is 101 t0 200 km/day, for 89 vehicles the frequency is 201 to 300 km/day, for 51 vehicles the frequency
of travel is 301 to 400 km/day, for 16 vehicle the frequency of travel is 401 to 500 km/day.
 This indicates nor of trips or frequency of travel for goods reaching to the boarding point is high in pre
covid-19 but during pandemic number of trips were reduced so income of workers is also reduced,
number trips is high compare to during pandemic but lower than the pre covid-19.
2.To study and compare the impact caused on supply and chain management network.
CONCLUSION

58
3.To study and compare the impact on change of transit behavior pattern.
 Based on the graph it was observed that 64.5% of the people were used to travel more then 5
days a week around 11.76% of people were used to travel once or thrice a month ,9.74% of the
people travelled once or twice a week and same percentage of people were used to travel 3 to 4
days a week and remaining 4.26% of people haven’t travelled.
 Based on graph it was observed that 34.15% of the people were used to travel 1 to 3 times a
month, 24.75% of people were used travel 1 or 2 days a week, 22.84% of people never travelled
,22.84% of people were used travel 3 to 4 days a week and 6.49% of people were used travel 5
or more days a week.
 Based on graph 41.88% of people were used to travel 5 or more days a week, 20.49% of people
were used to travel 3 or 4 days a week ,15.23% of people were used travel once or thrice a
month,12.77% of people were used to travel 1or 2 days a week, and remaining 9.63% of people
never travelled.
 It indicates that travelling pattern of people is changed after covid-19,because before pandemic
most of the people like workers and students travel everyday but after pandemic most of them
working through online so travelling days of people is reduced compare to precovid-19
CONCLUSION

59
CONCLUSION
4.To study and compare the mobility behavior on supply chain management FMCG products
based on income.(fast moving consumers goods)
 Based on graph before covid-19 ,69.76% of ordering food through online is high ,16.18% is less,
and remaining 14.06% of ordering food through online is neither high nor low means doesn't have
any changes.
 Based on graph during covid-19 ,89.66% people ordering food through online is low,6.9% of is no
change, and remaining 3.45% of people ordering food through online is high
 Based on graph after covid-19 ,48.01% of ordering food through online was high ,34.48% was low
and remaining 17.51% was neither high nor low means doesn't have any changes in ordering food
through online.
 This indicates that supply of goods like milk, chocolate ,biscuits etc .ordering and supplying of
goods is high in pre covid-19,relatively less in after covid-19,but it is too much reduced during
pandemic and in precovid-19 the frequency of mobility is high so income is also high

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THANK YOU
Guide:
Mr. VISHWAS JS
Asst Professor
Dept of Civil Engineering,
PES University
PARVATI P NAYKODI ALLE VENUGOPAL REDDY
B. Tech. Civil Engineering, B. Tech. Civil Engineering,
Mobile: 8088381272 Mobile:7095605142
Email:parunaykodi@gmail.com Email:venugopalr36@gmail.com
VENKATESH TUMKURKAR PRATHIK GOWDA KS
B. Tech. Civil Engineering, B. Tech. Civil Engineering,
Mobile: 9945341159 Mobile: 9550097714
Email:venkateshtumkurkar@gmail.com Email:goedaprathik015@gmail.com
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