Abhijna M, PhD Scholar, Department of Civil Engineering
Vidhulekha Tiwari, PhD Scholar, Centre for Urban Science and Engineering
Abhishek Kochure, Masters Student, Centre for Urban Science and Engineering
Ebrahim Saria, Masters Student, Centre for Urban Science and EngineeringDescriptive
Analysis• Graphs• MapsMultinomial
Logit• Significant
determinants ofmode choice• Sensitivity
Analysis

MachineLearning• Variables
selectedthrough MNL• ANN, Random
Forest, XGB

2

Mode Share for Scheduled Trips
2018
70.0%
60.0%

58.1%

2019

2020

64.7%

50.0%

40.5%

40.0%

26.7%

30.0%

19.7%

20.0%

16.9%

16.9%
7.0% 6.2%

10.0%

8.9%

5.9%

10.4%

3.3% 5.5% 2.1%

2.9% 0.8% 3.4%

Bus

Others

0.0%

Train

Walk

Bike

Car

Inferences• Use of public transport has decreased, and that of private transport has increased in 2020 due to the effect of COVID-19
3

Car

Rail

2019

2020

Bus

4

Explanatory variables

Co-efficientt-statistics

Goodness of fit statistics

In-vehicle travel time-.03950***-18.37

LL (at convergence)-2708.91595

Access time-.07589***-11.51

LL (constants only model)-3652.66450

Egress time-.06767***-9.95

Rho-square

Number of transfers-.40493***-3.97

Note: ***  Significance at 1% level

Departure timeCarBus

-1.00699***1.26001***

-7.37
6.64

Sample Size: 5261, Cali. = 85%, Vali. = 15%

OD Distance
Car
Bike
Bus

Train

282.47

5.69

11.53

8.00

26.31

-.03571***.33544***-.10638**-4.16-11.45
-2.09

Bike

5.55

7.66

28.12

2.00

3.67

Walk

10.40

40.83

162.88

2.59

17.30

Bus

7.28

3.39

10.46

33.55

2.32

Car

50.07

11.10

38.00

6.79

11.04

Constant
Walk
Bus
Train
Car

1.48730***
2.01397***
1.88212***-.69619***

20.71
8.58
10.96
-6.11

0.26

Train

Bike

Walk

Bus

Car

Prediction Accuracy = 0.630
5Explanatory variables

Co-efficientt-statistics

Goodness of fit statistics

In-vehicle travel time-.03132***-17.90

LL (at convergence)-3089.17180

Access time-.05723***-7.23

LL (constants only model)-3656.31847

Egress time-.07137***-8.69

Rho-square-.15154-1.26

Note: ***, **  Significance at 1%, 5% level

Departure timeCar
Bus-.92148***.86494***

-7.83
3.60

Sample Size: 3959, Cali. = 85%, Vali. = 15%

OD Distance
Car
Bike
Bus

Train

158.5

9.5

14.9

2.1

40.1

-.01963**-.25350***-.34364***-2.40-12.18
-6.39

Bike

15.5

29.5

62.9

11.9

18.1

Walk

14.3

30.2

71.2

4.2

16.2

Bus

11.0

7.4

12.0

11.8

4.8

Car

6.5

9.6

20.3

0.5

11.1

Number of transfers

Constant
Walk
Bus
Train
Car

.97645***.76312***.73832***-.55806***

14.66
2.73
4.03
-6.24

0.16

Train

Bike

Walk

Bus

Car

Prediction Accuracy = 0.474
62019 Scheduled: Sample Size = 5261, Train = 85%, Test = 15% | 2020 Scheduled: Sample Size = 3959, Train = 85%, Test = 15%

XGB

RF

Accuracy: 0.767

Accuracy: 0.816

Accuracy: 0.815

Accuracy: 0.725

Accuracy: 0.725

Accuracy: 0.705

2020

2019

ANN

7

Elasticity Effects of Transportation System Attributes on Mode Shares
Attribute

Bus

2019

Inferences

Train

2020

2019

•

2020

Travel time

Car

0.03

0.05

Bus

-0.34

-0.35

Train

0

0

-0.11

-0.15

-0.13

-0.15

Access time
Bus
Train

-0.21

-0.41

•
•

The demand of bus issensitive to travel
time of car
The demand of train
is not sensitive totravel time of car
The demand of Buses
and Trains issensitive to theirrespective travel andaccess times

8

Share of public transport has declined in 2020
Average distance travelled increases from buses tocars to trains
Significant variables influencing the mode choice are
In Vehicle Travel Time, Access Time, Egress Time,
Number of Transfers, Departure Time, OD Distance

ML shows better prediction accuracy
Demand of bus can be increased by increasing thetravel time of car and decreasing the travel time of
bus

9

•

Case studies show that people are more likely to travel through buses than trains during pandemic•

People find it hard to maintain social distancing in underground closed spaces in train

Increase Accessibility of Buses• Improve the last mile connectivity of buses
Reallocation of Road Spaces to Buses• Dedicated bus lanes

10

11

1.
2.
3.
4.
5.
6.
7.
8.

Driving on Japan’s Highways and Toll Roads, Wednesday, February 12, 2020, https://www.realestate-tokyo.com/living-intokyo/driving/driving-japan-highways/
Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach, Eui-Jin Kim,
https://www.hindawi.com/journals/jat/2021/6685004/#abstract
Choice modelling in the age of machine learning, CranenburghI et al, https://arxiv.org/ftp/arxiv/papers/2101/2101.11948.pdf
Applications of Neural Networks in Mode Choice Modelling for Second Order Metropolitan Cities of India, Chalumuri et al,
https://www.jstage.jst.go.jp/article/eastpro/2009/0/2009_0_134/_pdf
Mode Choice Model for Public Transport with Categorized Latent Variables, Jian Chen and Shoujie Li,
https://www.hindawi.com/journals/mpe/2017/7861945/
Modeling the choices of individual decision-makers by combining efficient choice experiment designs with extra preference
Information, Journal of Choice Modelling, 1(1), pp. 128-163, Louviere et al,
https://www.econstor.eu/obitstream/10419/66823/1/717179079.pdf
Discrete Choice Model and Analysis, https://www.publichealth.columbia.edu/research/population-health-methods/discretechoice-model-and-analysis
Share of consumers in Japan owning a car as of June 2019, Statista, https://www.statista.com/statistics/1027885/japanshare-car-ownership/

12

13

Introduction and Elementary Objectives

1
14Profile•••••

Created on reclaimed land in 1937.
Proximity to central Tokyo makes it valuable for real estatedevelopment.
Was preparing for the 2020 Summer Olympics in Tokyo
The Toyosu Market (Toyosu Shijō) is a wholesale market for seafood,
fruits and vegetables.
Toyosu MiCHi no Eki will be the first Urban Roadside Station in Japan.

Requirements••

Development of efficient transportation system to cater the needs of thebusinesses
The emergence of COVID-19 Pandemic has created several hurdles indevelopment, and has caused several behavioural changes amongpeopleSources:•
https://www.google.com/maps/place/Toyosu,+Koto+City,+Tokyo+135-0061,+Japan/@35.6509201,139.7858718,14.43z/data=!4m5!3m4!1s0x60188999157b9949:0xbb11b36766253d26!8m2!3d35.6545886!4d139.7965201•
https://www.shimz.co.jp/toyosu/index2.html

15

PP Data > Toyosu > Trip Data
(2018, 2019 and 2020)
•
•
•
•
•
•
•
•
•
•

Purpose of Trip
Departure and Arrival Time
Trip Duration
Main and First Transportation Mode
Subsequent Transportation Modes
OD Direct DistanceLongitude and Latitude of OD
Shikucode of OD
If alternatives are available for: Car, Train,
Walk, Bike and Bus
Travel Time, Fare/Cost, Distance, AccessTime, Egress Time for available modes• To understand the impact of COVID-19 Pandemic on mode choice
behaviour of people• Do a preliminary analysis of data to understand trip patterns• Do modelling through MNL and ANN to understand which factors
influence the mode choices• Refine variable choices on the basis of MNL model and then do a
prediction through ANN• Understand the differences between trip patterns of scheduled andnon-scheduled trips• Understand the effect of COVID-19 pandemic on behavioural changes• Derive suitable policy interventions based on sensitivity analysis

16

Detailed Methodology and Assumptions

2
17

Preliminary Analysis

Initial Analysis

Final Analysis

Policy Interventions(On Raw Data)(On Re-Classified Data)(On Modified Data)(By Overall Inferences)• Inferences throughgraphs• Re-classification ofdata• Inferences throughgraphs and maps
• Modelling through
MNL and ANN• Modification ofclasses andindependent
variables• Modelling through
MNL and ANN• Final inferences
through modelling
results• Overallconclusions andInferences• Possible scenarios• SWOT analysis• Policy
Interventions

18

• Socio-Economic characteristics and built environment characteristics have not been
considered• Influence of Perception of safety on mode choice have not been considered

19All trips are independent• The characteristics of any one trip do not depend on, or affect, any other trip

Each day of departure is an independent, uniform and normal day• Trips on any one day do not depend on, or affect, trips on any other day• Trips on each day have uniform characteristics• Each of these uniform days are normal or weekdays, holidays or weekly off days are not considered

If a route is available for walk then it is available for bike as well
Buses require no trip changeCars, Walk and Bikes Require no Trip Change
Cycling Speed is assumed as 22kmph
Cost of travelling through car is considered as 25 yen per km, plus 150 yen in fees
20

Detailed Analysis and Inferences

3
21

2.A

(On Raw Data)

22

Purpose of Trip

2020

•

Work/School

Business

AfterWork/School

home

Shopping

EatingLessonHospital

Amusement

Sightseeing

Walking

PickupWaiting

Other

Unknown•

6.6% 2.1%3.1% 4.1%3.0%

•

14.2%

6.5% 2.1%

2019

22.6%

2018

25.7%

34.0%

7.8% 2.4%

7.7% 2.7%

18.6%

33.7%

33.1%

10.7%

9.4%

6.5% 1.8%3.5%2.6%
2.5%

6.7%

1.9%
2.9%1.2%
1.6%•

Trips to work and schools
have reduced in 2020 due
to COVID-19 Pandemic
Consequently the share totrips to shopping has
increased
The share of back tohome trips has remained
uniform, and its value hasremained around 33%
This suggests that on anaverage people still take
two trips in a tour before
reaching home23

Scheduled

Unscheduled

Home

Other

Work/School

Shopping

After Work/School

PickupWorking/Business

EatingHome

Waiting

LessonAmusement

Other

Sightseeing

Unknown
Hospital
Walking/Strolling24

Main Mode Share for Scheduled Trips

•

train

SubwayMonorail

Tram

Bus

Car

Freight car

Taxi

motorcycle

Bike

Sharebike

Velotaxi

Walk

Airplane

Ship

Other

Unknown

•
2020

14.5%

12.2%

2.1%

9.6%

16.4%

0.5%

40.5%

•
2019

35.8%

2018

35.6%

28.9%

22.5%

5.5% 4.6%

3.3% 7.8%

4.3%1.9%

6.3%0.7%

16.9%

Respectively much lesser
people are using public
transport for scheduled
trips in 2020 due to
COVID-19 Pandemic
Consequently the share ofpeople walking has
increased
The number of peopleusing private cars andbikes has increased
subsequently in 2020

19.7%

25

Main Mode Share for Non-Scheduled Trips

2020

train

SubwayMonorail

Tram

Bus

Car

Freight car

Taxi

motorcycle

Bike

Sharebike

Velotaxi

Walk

Airplane

Ship

Other

Unknown

12.4%

13.2%

2.8% 6.6%

17.7%

•

42.8%

•
2019

2018

13.7%

15.9%

27.8%

3.6% 7.5% 1.0%

19.3%

12.9%

3.4% 6.3%0.5% 8.0%

40.7%

As data from 2019
includes entries frominitial months of 2020, theeffect of COVOD_19
Pandemic can be seen in
the use of public
transport for nonscheduled trips
The increase in the use ofprivate vehicles such cars
and bikes can beobserved here as well

30.7%

26

TrainTrain

BusShopping

Walk

Walk

Subway

Bike

Bike

Car

Car

Sharebike

Other• Monorail, Tram, Freight Car, Taxi, Motorcycle, Velotaxi, Walk, Airplane, Ship, Other, Unknown

27

Reclassified Purpose of Trip
Scheduled

2020

2019

2018

•
•

22.1%

Non Scheduled

32.8%

32.0%

35.3%

Others

45.1%

23.8%

20.7%

44.1%

43.9%

Scheduled trips have decreased
People have stopped visiting to offices and schools but they are still taking trips for leisure, shopping, strolling etc.

28

2.B

(On Re-Classified Data)

29

Revised Main Mode Share for Scheduled Trips
Train
2020

Walk

26.7%

Bike

Car

Bus

Others

40.5%

2019

16.9%

64.7%

2018

10.4% 2.1%

16.9%

58.1%

19.7%

6.2% 5.9% 5.5%
7.0%

8.9% 3.3%

Revised Main Mode Share for Non-Scheduled Trips
Train
2020
2019
2018

25.6%

Walk

Bike

Car

Bus

42.8%

29.6%

40.7%
47.1%

30.7%

Others19.2%

7.2% 2.8%

15.4%

8.5% 3.6%
9.6%

6.8% 3.4%

Inferences• The share of people using
public transport has been
decreasing due to safety issues
because the decrease can beobserved both in scheduled andnon scheduled trips
Requirements• The safety of public transport
must be increased• Consequences of increase in
private transportation modes
must be considered for future
developments

30

Scheduled trips in 2018
bike

bus

car

rail

Scheduled trips in 2019

walk

900

bike

bus

car

rail

walk

2500

bike

bus

car

rail

walk

1400

800

1200

2000

700

Scheduled trips in 2020

1000

600

1500

500
400

800
600

1000

300

400

200

500

200

100
0

0

•
•
•
•

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Hours of the day

Hours of the day

Hours of the day

Scheduled trips have a peak travel time from 07:00am to 11:00am in the morning
Only one peak is observed for scheduled trips
Major share of scheduled trips are done by trains
Use of bike has increased in 202031

Non-Scheduled trips in 2018
bike

bus

car

rail

Non-Scheduled trips in 2019

walk

250

bike

bus

car

rail

walk

900

600

300

200

100

0

•
•
•
•

walk

400

200

0

rail

600

400

50

car

800

500

100

bus

1000

700

150

bike
1200

800
200

Non-Scheduled trips in 20200

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hours of the day

Hours of the day

Hours of the day

Non-scheduled trips have a peak travel time from 07:00am to 11:00am in the morning and 13:00pm to 17:00pm in the evening
Two peaks are observed for non-scheduled trips
Major share of non-scheduled trips are done by bikes or by walk
Use of rail has decreased in 2020

32

Bus

Car

Rail

Walk

2018

2019

2020

Bike

33

Bus

Car

Rail

Walk

2018

2019

2020

Bike

34

MNL
ANN

Main Mode Choice

Departure Time

Departure Time Classified

Number of Mode Changes

OD Direct Distance

Distance Category

Availability

Time

Fare/Cost

Number of Transfers

Access Time

Egress Time

MNL

Y

X1

X2

X3

X4

X5

X6

X7

X8

X9

X10

X11

The data was converted into
multiple line format and eachchoice was regarded as a separate
entity, with the probability of thechosen choice as 1

ANN
•

•

The data was used for training
without classifying thedeparture time and OD
distances
Since the mode characteristics
of only the available choice isgiven, the characteristics were
not considered35Scheduled

Accuracy Score: 0.777

Accuracy Score: 0.760

Accuracy Score: 0.565

Scheduled

2019

Non-Scheduled

Accuracy Score: 0.697

Accuracy Score: 0.700

Accuracy Score: 0.528

Non-Scheduled

2018
2020
2018
2019
2020

Rho
square=Rhosquare= 0.14Rhosquare= 0.23Rhosquare=Rhosquare=Rhosquare=36

2.C(On Modified Data)

37

Selection of Columns• On the basis of availability and uniformity of data
• On the basis of variables which showed significant results
Selection of Rows
• Removal of error values

38

IVTT

Continuous

In vehicle travel time (minutes)

AT

Continuous

Access time (minutes)

ET

Continuous

Egress time (minutes)

NT

Discrete

Number of transfers (numbers)

DT1

Categorical

Departure time (1=Peak hour=1700to1900 and 0700to0900, 0=Off peak=other)

DIST1

Continuous

OD Distance (straight line distance between Origin and Destination, km)

TC

Continuous

Travel cost

Mode

Categorical

Bike=1, Bus=2, Car=3, Walk=4, Rail=5

39

Explanatory
variables

Coefficientt-statistics

Inferences

Goodness of fit statistics
LL (at convergence)-2708.91595-18.37

LL (constants only model)-3652.66450-.07589***-11.51

Rho-square

Egress time-.06767***-9.95

Note: ***  Significance at 1% level

Number oftransfers-.40493***-3.97

In-vehicle traveltime-.03950***

Access time

• Egress time is not asignificant variable inscheduled trips

Utility

Departure timeCarBus

-1.00699***1.26001***

-7.37
6.64

OD Distance
Car
Bike
Bus

-.03571***.33544***-.10638**-4.16-11.45-2.09

Constant
Walk
Bus
Train
Car

0.26

• Cost of trip is not ansignificant variable inscheduled trips

1.48730***
2.01397***
1.88212***-.69619***

20.71
8.58
10.96
-6.11

U(TRAIN)= -0.0395* IVTT -0.40493*NT -0.07589*AT -0.06767*ET +1.88212
U(BIKE)= -0.0395* IVTT -0.40493*NT -0.07589*AT -0.06767*ET -0.33544*DISTU(Walk)= -0.0395* IVTT -0.40493*NT -0.07589*AT -0.06767*ET +1.4873
U(BUS)= -0.0395* IVTT -0.40493*NT -0.07589*AT -0.06767*ET +1.26001*DT-0.10638+DIST +2.01397

U(CAR)= -0.0395* IVTT -0.40493*NT -0.07589*AT -0.06767*ET -1.00699*DT-0.03571+DIST -0.69619

40

Explanatory
variables

Coefficientt-statistics

Goodness of fit statistics

Inferences

LL (at convergence)-3089.17180-17.90

LL (constants only model)-3656.31847-.05723***-7.23

Rho-square

Egress time-.07137***-8.69

Note: ***, **  Significance at 1%, 5% level

Number oftransfers-.15154-1.26

In-vehicle traveltime-.03132***

Access time

Departure time
Car
Bus

0.16

• Perception of safety could be
an important factor, influenceof which which is not reflectedin the model

Utility-.92148***.86494***

-7.83
3.60

U(WALK)= -.03132*IVTT-.07137*ET-.05723*AT1+.97645-.15154*NTU(BIKE)= -.03132*IVTT-.07137*ET-.05723*AT1-.25350*DIST1-.15154*NTOD Distance
Car
Bike
Bus

-.01963**-.25350***-.34364***-2.40-12.18-6.39

Constant
Walk
Bus
Train
Car

.97645***.76312***.73832***-.55806***

14.66
2.73
4.03
-6.24

U(BUS)= -.03132*IVTT-.07137*ET-.05723*AT1-.34364*DIST1+.76312+.86494*DT1-.15154*NTU(TRAIN)= -.03132*IVTT-.07137*ET-.05723*AT1+.73832-.15154*NTU(CAR)= -.03132*IVTT-.07137*ET-.05723*AT1-.15154*NT-.92148*DT1-.01963*DIST1-.55806

41D:\pythonbehavioral\hyperlink_mnl_docs

42Past Experiences and Curent Scenario

443Changes in transport behaviour during the Covid-19 crisis

Covid19 may cause permanent change in travel choice behavior andgovernment must prepare for that•
People do not want to travel through public transport and its demand has
considerably decreased•
Previous crises have spurred long-term shifts in transport preferences
when supported by other factors•
Governments can influence which transport behaviours are morepermanent after the crisis•
Infrastructure investments can be crucial for building trust in public andactive transport•
Pricing and regulatory policies can help incentivise less energy-intensive
transport behaviours when the crisis ends•
Public behaviour change campaigns can work under the right conditions•
Trust in government can esure that demand for public transport increases
again•
Direct stimulus which can have positive economic spill over effects:
spending in public transport creates more economic benefits•.issues of public health post pandemic can be tacked by using sustainable
modes such as bikes or share-bikes, which produce long term positive
economic development, by reducing congestion and maintaining health
Source: https://www.iea.org/articles/changes-in-transport-behaviour-duringthe-covid-19-crisis•

Beyond the immediate crisis: The SARS-CoV-2 pandemic and public transport
strategy
People prefer buses over trains as they find it difficult to maintain social distances inunderground spaces•
While the actual risk of infection is one thing, perceived risk and behavior is quite another.
Fear-induced public transport avoidance decreases over time.
•
With respect to modal choice, public transport may continue to suffer.
•
Trust reinstating behaviour must be promoted to ensure rebound to pre-pandemictransportation patterns•
Unemployment and digitalization may however restrict the rebound to pre-pandemic pattern,particularly in the area of work and education•
While use of bike may increase, the use of car may remain constant in Germany. However theuse of public transport will surely decrease.
•
Expansion of Public Transport makes urban mobility more accessible and sustainable•
Capacity must be increased to ensure social distancing in public transport•
Expanding public transport can function as direct and city-specific economic stimuli.
•
Multi-modal integration can help public transport to generate customer loyalty by keeping
them within their own public-transport centric ecosystem/app even in volatile times.
•
Incentives for using public transport is helpful•
Data analytics and AI tools, furthermore, can help generating systematic and near-real-time
information about occupancy, travel flows and system bottlenecks.
Source: https://mobilityinstitute.com/wp-content/uploads/2020/04/Beyond-the-immediate-crisisThe-SARS-CoV-2-pandemic-and-public-transport-strategy_mib_v1.03.pdf•

44

Transport policymaking that accounts for COVID-19 and future public health threats: A
PASS approach

Mass transit ridership didn’t snap back after the
2003 SARS outbreaks

Public transport firms receive more subsidies in the USA and Europe than in JapanPASS approach is proposed for systematically designing policies that address concerns during COVID-19
and future pandemics, it was initially named by the present author after a review of railway transport
policy measures by The Ministry of Land, Infrastructure, Transport and Tourism of Japan (2020)•
PROTECT employees and users by wearing masks, washing hands, installing antiseptic solution dispensers,
and monitoring the body temperature of employees. AVOID trip making, SHIFT departure time forcommuting, STOP making unnecessary or non-urgent trips•
P: Prepare–Protect–Provide: prepare well for pandemics, preparations before a pandemic starts in acountry/region, and preparations before the pandemic starts in a city/town of the country/region, provide
public goods and services that cannot be provided by individuals or firms•
A: Avoid–Adjust: . Avoidance allows transport users to keep away from the virus and transport operators toprevent transport users from being infected when using transport services, transport users need to adjust
their activities and schedules as well as trip timings to reduce opportunities of being infected ortransmitting virus to others•
S: Shift–Share: people who have to make trips need to shift their modes to reduce their infection risk,
Sharing of information and resources is critical to collective decisions and actions, because such sharing
can fill knowledge gaps and further enhance the feasibility and transparency of collective decisions andactions•
S: Substitute–Stop : When the pandemic becomes more serious, people must substitute or stop activities
involving trips
Source: https://www.sciencedirect.com/science/article/pii/S0967070X20306181• The peak of the epidemic in Taipei and Hong Kong lasted
about a month, but ridership lagged significantly for half ayear• Planning and transportation experts say that a number of
habits acquired by both passengers and agencies had serious
staying power after the SARS outbreak• The measures city governments implemented to attractriders back to mass transit, some of them quite expensive,
also persisted. More intensive cleaning regimens were
implemented, better ventilation was installed, and trains
were run more frequently to ensure they weren’t
overcrowded.
• Riders may eventually fully return to transit, especially aftercongestion and parking gets bad in central cities again, butthat could take years, system may not exist till then.
• Public transit is an essential service that must be provided if
their most economically productive cities are to remainfunctional• Subsidies are a way to ensure they are functional
Source: https://citymonitor.ai/transport/mass-transit/transitridership-didnt-snap-back-after-the-2003-sars-outbreaks

•
•

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Goal

PolicyStrengths

Weaknesses

Opportunities

Challenges

1. Accommodate touse of privatemodes

a. Develop infrastructure
wrt Private Modes

High vehicle
ownership rate

Increase in use of
motorcycles andcars will increase
pollution,congestions andmore accidents

Increase in use of
bike improves air
quality, decreases
congestion, andimproves publichealthModification of
Infrastructure
required

2. Shift back topublic transporta. Increasing Safety wrt
COVID-19
b. Dis-incentivise Private
Modes
c. Increase speed of Buses
d. Increase connectivity of
public transport

Existence ofefficient public
transportation
systemOn-going pandemic,
subsequent waves

Pandemic may end
soon, helps inboosting/maintainin
g overall long-term
economyRe-developing trust
among people

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Cost does not affect mode choice for scheduled trips in Toyosu• Majority of scheduled trips are through trains, and for offices and schools, the fare is either reimbursed or discounted
The demand of train is not sensitive to travel time of car• In practical usage, with respect to last mile connectivity, cars are more similar to buses, and not similar to trains
The use of bikes has also increased• But since the bikes are not used for long distance travelling, and they are sustainable modes, our focus is on
decreasing the use of cars
The share of train has also decreased• But since studies show that people still prefer buses than trains in pandemic, the focus is on buses• Since the average distances covered by buses is significantly low, there is an opportunity to increase its connectivity

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Accumulation of more detailed data related to age and gender of respondents is required forachieving better results
Details like occupancy play a vital role in deciding the demand of public vehicles and thus
corresponding study must be carried out
Technological interventions like real time access to occupancy data can be a decisive factor formode choice
Social campaigns have proved to be effective in making the public consider certain modal choices

The perceived risk and actual risk in using public vehicles with respect to COVID-19 must be
studied48

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