Mobile Application
Chapter 10of Multimodal Transport
Transport sustainability and its
evaluation are essential for urban
transport planners and policymakers.
In developing counties like India,
transportation system improvement,
including public transportation,is crucial for economic growth.
These countries are facing rapid
economic growth, especially forthe transportation of human beingsand commercial goods. Moreover,due to the limited improvement ofroad infrastructure development
because of unbalancing speed
between the demand of goods andmaterial delivery and infrastructure
improvement. Their economic
growth generates more personalvehicle demand which results intraffic congestion, high energy
consumption, proliferate delays andincreased pollution levels in cities.
Public transportation has more roleof transportation to address thisissue for more sustainable and safe
transportation.
In terms of public transportationsuch as BRT, Light Rail Transit (LRT),and METRO, it is more critical theirconnectivity for smooth transit. Thisconnectivity generates Multimodal
Transport capability. This Multimodal
Transport is capable of providing
seamless travel to the commuter.
This will help the commuter to decide
to use the public transport network
as a matter of choice. It is equallyimportant that the public transportsystem be user-friendly so that thecommuter uses it voluntarily. The
most crucial aspect is multimodalintegration; Physical integration,
network integration, fare integration,
Information integration, andinstitutional integration. In general,
there is no timetable and punctual
operation in developing countries.

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Last One-Mile Problem

1: Overview

Public transit local station stops
are sometimes far from travelers’destinations, and they may wish touse private vehicles. Therefore, theﬁrst and last one-mile problems are
critical for travelers choosing theirvehicle or public transportation. As
several studies in other countries
like Canada, Australia, and New
Zealand, most people in the U.S. are
not comfortable walking more than
one-quarter mile. Transit experts
and consultants believe that micromobility is the solution that canbridge this symbolic distance. Thismessage gives us a transportation
connectivity design.

A smartphone-based navigation
app named “M2SmartNavi”
for Ahmedabad was created.
Since Android is the mainstream
smartphone market in India, wechose an app for Android. The mainfunctions are as follows.

Our program makes it
understandable that even onequarter mile walks hard underhigh-temperature conditions. On theother hand, India has good shorttransit capability like rickshaws at anaffordable cost. This short-distancetransportation capability is also a keyrole in multimodal transportation.

Multimodal Application
To create smooth transport
connectivity, ICT or information
communication technology can help
provide appropriate transportation
information. In India, ICT, such as
smartphones, is a popular personal
tool for receiving information as amultimodal application.
Figure 10-1 shows the overview ofthe multimodal APP system. As forMultimodal application, the following
function is key.
• Data acquisition• Navigation function• Provide modal selection

2: Data acquisition
Obtain GPS location information fromthe user’s smartphone. However,location information can only beobtained while the app runs and thenavigation is used. Also, considering
the battery consumption of thesmartphone, we decided to acquireit every second.
By continuously acquiring location
information, we aimed to collect itas the user’s travel route dataand utilize it to eliminate traffic
congestion.
In that case, if the user’s attributeinformation is also added, it will be anadvantage for the user by providing
the route according to the individual
attribute. On the other hand, it will
be helpful for city planning on theadministrative side by analyzing
the traffic behavior history in detail.
Therefore, it is a troublesome
operation for the user, but only when
the application is started for the ﬁrst
time, the following registration screen
displayed, age, whether or not you
own a private car, frequency of useof public transportation, etc.
• About Personal attributes:
Nickname, Gender, Age,Profession, Income, Family
structure, Educational
background• About owning a private car /Motor cycle:

Chapter 10
Mobile Application of Multimodal Transport

Figure 10-1 Overview of Multimodal APP System

Figure 10-2 Example of Multimodal App(a) Main screen•Own or not, Use or not,Vintage, Displacement
About frequently usedtransportation:
Usually, Commuting, Traveling,Shopping, Business, Pickup,and Others.

(b) Input destination

3: Navigation function
The main feature of the Navigation
function is to provide route selection
to users based on their requirements.
The navigation function of thisapplication was developed basedon the development environment(c) Enable to navigateof HERE’s “HERE SDK for Android(Premium).”
The main screen image of the app isshown in the ﬁgure (Figure 10-2 (a),(b), and (c)).
The navigation user ﬁrst inputs thedestination to the destination.

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Handbook of Multimodal Transport for Smart City
Application of Sensing, Networking and Big Data Analysis

Figure 10-3 Display of eco-index inroute selection

Figure 10-4 Examples of navigation function in multimodal App

As shown in Figure 10-3, any of thefollowing input methods can be usedas the destination input method.
• Selection by free text search• Select from facility genres• Select from history• Selection by tapping directly on
the map
Users can now choose the mode oftransportation and route from privatecars, Auto rickshaws, city buses,
BRTs, and a combination of these.
As a feature of this application,
it is connected to the BRT operation
management system, and it ispossible to continuously calculate
the route based on the real-time
driving position information for theBRT.
Multiple calculated routes (up toabout 10) are presented on thenavigation screen, with the distancefor each route, estimated arrival time,cost, and eco-index.
The eco-index was determined byan original calculation method fromthe estimated fuel consumption ineach mode and the index value ofIndia. To make it easier for users tounderstand visually, we decided todisplay the number of leaf marks

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according to the degree of CO2
reduction.
By switching tabs at the top of
the screen, the preferential routecan be selected from generalroad precedence, expressway
precedence, and distanceprecedence by a user.
Figure 10-4 shows an example ofusing M2SmartNavi. Once selectednavigation is selected, the user cansee the total transit mode to thedestination, the real navigation guideon the map screen, and the feedback
screen at the end of navigation.

Data Analysis
So far, about ten people haveconducted ﬁve ﬁeld experiments
for two weeks separately, and morethan 1,000 travel data have beenobtained. We tried to visualize andanalyze GPS data acquired from theuser’s smartphone, so here is anexample.

1: Visualization ofdata using GIS
ArcGIS Pro from ESRI (EnvironmentalSystems Research Institute) indicates

all personal travel records on a map
and each person’s record by color.
An example of each person’s trip isshown in Figure 10-5.

2: Matching withactual behavior
In December 2021, a three-week
ﬁeld experiment was conducted withthe cooperation of a local university.
At this time, the user was asked torecord the following items as anactivity diary (movement history)
during the experiment period, bothfor personal attributes.
• Actual time
• Point of departure• Destination• Purpose• Transportation use• Actual degree of congestion
Figure 10-6 shows the OD(Origin-Destination) arrow graphdisplayed on GIS for 170 trips
obtained in this experiment.
By collating with the activity diarymentioned above, it is possible tovisualize by purpose or means oftransportation.
• Actual time
• Point of departure• Destination

Chapter 10
Mobile Application of Multimodal Transport

Figure 10-5 User movement history obtained from Multimodal App (each color represents an individual)

Figure 10-6 Origin-Destination arrow graph(a) By purpose(b) By means of transportation

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Handbook of Multimodal Transport for Smart CityApplication of Sensing, Networking and Big Data Analysis

Figure 10-7 Relationship between trips obtained by the app and traffic volume obtained by the traffic sensor
3530

Number2520
1510
500 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23Hour(a) Number of trips obtained by the appTraĸc_volume

2000

Occupancy

70

50
40

1000

3020500

Occupancy [%]Traĸc volume60

1500

1000024681012Hour

1416182022(b) Traffic volume obtained by the traffic sensor

Figure 10-8 Congestion status due to feedback from users(a) At all times•••

PurposeTransportation use
Actual degree of congestion

3: Traffic behavior analysis
This section describes an exampleof traffic behavior analysis using
these data.

60(b) At 9:00 in the morning1) Congestion by time zone:
In Figure10-7 (a), a number of the total
trips is shown, and traffic conditionis shown in Figure 10-7 (b) by traffic
volume and occupancy. Thesegraphs show two peaks of trips in
the morning and the evening. This ismainly for commuting from home tothe office or school in the morning.
And there is traffic congestion occurs
by occupancy above 30% level.

(c) At 18:00 in the evening

In the evening, more congestion
occurs by more than 40% occupancy.

2) Congestion status due
to feedback from users:
This is unique data in this program
which comes from each user’s
feedback about their feeling oftraffic congestion condition image
by personal judgment level
between 1 through 5 (Figure 10-8).

Chapter 10
Mobile Application of Multimodal Transport

Figure 10-9 Origin to destination arrow graph (in each feedback at all times)(a) Heavy congestion and congestionLevel 1 means heavy congestion,level 2 indicates congestion,level 3 represents a fair condition,level 4 means smooth traffic, andlevel 5 means very smooth traffic.
From this feedback, travelers feel
heavy traffic congestion in themorning while commuting, and eventraffic condition in the evening isworse.
Figure 10-9 (a) shows the heavily
congested and slightly congested
trip mapping. One typical heavy
crowded trips occur at trips crossing
over the river. The gray shadow
area is the Sabarmati River inAhmedabad. This means traffic
congestion occurs at each end of
the river junction because both sidesof the intersection become traffic
congestion in each direction forvehicles that cross the bridge.
Figure 10-9 (b) shows that smoothtraffic travel occurs on one side of
the area where the trip does notcross the river. This leads to traffic
congestion occurring at the junctionof each end of the river, as previouslymentioned.

(b) Very smooth traffic and Fair smooth traffic

Conclusion
This chapter shows multimodal
application validness for analyzing
traffic conditions and travelers’
feedback for their daily trips.
From this experience, users’
feedback function throughsmartphones provides real users’
messages about how they feeldaily travel, travel conditions, androad conditions. This informationis helpful for civil design andpoints of improvement for urban
transportation.
Commercial smartphone-basednavigation systems such as Google
navigation are becoming very popularthese days. One advantage ofM2SmartNavi tells us transportation
connectivity capability by using BRT
operation timing in this case. And
there is a CO2 emission reduction
indication by the green leaf log
in M2SmartNavi, which will haveseveral future capabilities to countCO2 emission reduction efforts fromresidents and potential incentives
when the green leaf count will be
used as eco-money in the city. This
eco-money has potential for smart

parking, public transportation fee, andor goods purchased in the future.
To make M2SmartNavi at thecommercial level, it is necessary
to software maintenance, update
the smartphone operation system,
expanding other transportation
support such as city buses,
auto-rickshaw, or taxis. Thoseoperations and maintenanceshould be handled under a speciﬁccity-level organization such as smart
city development companies orsome transportation consortiums,including transportation stakeholders.

Future prospects andchallenges
This section describes the possibilityof utilizing multimodal apps inemerging countries and problems interms of operation and gives a future
outlook.

1: Challenges for
expanding app users
Task for expansion:• Use of incentive• Feedback to users• Long-term use merit61

Handbook of Multimodal Transport for Smart City
Application of Sensing, Networking and Big Data Analysis

Figure 10-10 Visualization with GIS dashboard tools

Task for operation:• Support smartphone OS• Software update and version
up maintenance• Server support• Running cost support

2: Future outlook
Alignment with other system:• Vehicle dispatch support

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•
•

Parking reservations
Payment function

Merit for users:• Last one-mile support• Seamless connectivity
Merit for municipality:• Collect user data
• Real time visualization• Modal shift and CO2 reduction

Figure 10-10 shows one of example
for visualization of real timetransportation condition by using
dashboard of GIS, which has beendeveloped in this program.