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sustainability
Article
Vector Maps Mobile Application for Sustainable
Eco-Driving Transportation Route Selection
Vahid Balali 1, * , Soheil Fathi 2 and Mehrdad Aliasgari 3
1 Department of Civil Engineering and Construction Engineering Management, California State University,
Long Beach, CA 90840, USA
2 UrbSys Lab, University of Florida, Gainesville, FL 32611, USA; sfathi@ufl.edu
3 Department of Computer Engineering and Computer Science, California State University,
Long Beach, CA 90840, USA; Mehrdad.Aliasgari@csulb.edu
* Correspondence: Vahid.Balali@csulb.edu
Received: 19 June 2020; Accepted: 6 July 2020; Published: 10 July 2020
Abstract: The decisions managing all modes of transportation are currently based on the traffic rate
and travel time. However, other factors such as Green House Gas (GHG) emissions, the sustainability
index, fuel consumption, and travel costs are not considered. Therefore, more comprehensive
methods need to be implemented to improve transportation systems and support users’ decision
making in their daily commute. This paper addresses current challenges by utilizing data analytics
derived from our proposed mobile application. The proposed application quantifies various factors
of each transportation mode including but not limited to the cost, trip duration, fuel consumption,
and Carbon Dioxide (CO2 ) emissions. All calculated travel costs are based on the real-time gas
prices and toll fees. The users are also able to navigate to their destination and update the total
travel costs in real-time. The emissions data per trip basis are aggregated to provide analytics of
emissions usage. The traffic data is collected for the Southern California region and the effectiveness
of the application is evaluated by twenty participants from California State University, Long Beach.
The results demonstrate the application’s impacts on users’ decision-making and the propriety of
the factors used in route selection. The proposed application can foster urban planning and operations
vis-à-vis daily commutes, and as a result improve the citizens’ quality of life in various aspects.
Keywords: smart city; sustainable transportation; route selection; data-driven decision making
1. Introduction
Transportation and logistics form the core of smart city solutions. The advent of various smart
devices has revolutionized both the quality and quantity of the data available from a single commute.
Such data can be used for efficient decision making at various levels. There is a constant need for
enhancing infrastructure performance through leveraging the digital footprint and using data-driven
decision tools [1–5]. The idea of smart cities addresses how the advancement and unavoidable use
of Information and Communication Technology (ICT) can impact urban development in regards
to environmental, financial, and personal satisfaction aspects [6]. Smart cities promise to create an
environment for safer, faster, more economical, and more environmentally friendly travels, especially
in metropoles. Developing and implementing dynamic data collection tools, tailored for specific goals,
can be a better alternative to expanding transportation infrastructure that is costly and time consuming.
Such tools provide city planners with more reliable indicators for journey information, start and
end location and time for each individual journey [7]. Daily commutes increasingly worsen traffic
congestion in big cities. In California, for instance, a typical Los Angeles driver loses approximately
$1774 (and rising) in time and fuel costs annually [8].
Sustainability 2020, 12, 5584; doi:10.3390/su12145584 www.mdpi.com/journal/sustainability
Sustainability 2020, 12, 5584 2 of 17
Playing a significant role in climate change, Carbon Dioxide (CO2 ) emissions have risen globally
at a 1.6% annual rate to reach 36.2 billion tons, though a 2.7% growth rate was predicted for 2018 [9,10].
Eco-driving is the process of driving in a way that minimizes fuel consumption and CO2 emissions [11].
Contrarily, Non-Eco driving accounts for both higher travel costs and CO2 emissions. Studies suggest
that many are aware of the effects of emissions on the environment, yet do not realize how high travel
costs of non-eco driving adversely affect them [12,13].
At the turn of the 21st century, transportation became more complex. Transportation professionals
are asked to meet the goals of providing safe, efficient, and reliable transportation, while minimizing
the impact on the environment and communities. This has turned out to be quite difficult given
the constant increase in travel demand, fueled by economic development, and the ever-growing
demands to do more with less. A partial listing of some of those challenges that transportation
professionals face includes capacity problems, poor safety records, unreliability, environmental
pollution, and wasted energy [14]. Adding to the challenge is the fact that transportation systems are
inherently complex systems involving a very large number of components and different parties, each
having different and often conflicting objectives [14,15]. In recent years, there has been an increased
interest among both transportation researchers and practitioners in exploring the feasibility of applying
Artificial Intelligence (AI) techniques to address some of the aforementioned problems, improving
the efficiency, safety, and environmental compatibility of transportation systems.
This study shows that drivers make more eco-friendly decisions when they are informed of their
contribution to CO2 emissions. This information is conveyed to them via their smartphone, and
the application that is designed for the study.
2. Background
More than half of the world’s population now lives in cities; this share of the population is
expected to increase [16]. Worldwide, cities play a critical social and economic role, while considerably
impacting the environment [17]. Transportation systems have become indispensable parts of daily
human activities. An average of 40% of the world population spends at least one hour on the road every
day [18]. Currently, there is no readily and easily accessible tool that would help the common citizen
to be informed about daily trip costs and the emissions affecting the built environment. Statistics show
that almost 71 percent of the population in the United States uses a smartphone [19]. Since widely used
mobile applications such as Google Maps and Waze do not provide travel costs, fuel consumption, and
emission rates, there is a lack of tools to provide such data dynamically and in real-time [20].
During recent decades, people have depended more on transportation systems, creating new
opportunities as well as various new challenges. Traffic congestion, for example, has become an
increasingly critical issue worldwide, as the number of vehicles on the roads increases. Higher traffic
congestion levels cause more exhaust emissions and more deterioration of the air quality [21]. Such
environmental challenges require rapid actions and effective solutions, among which Intelligent
Transportation Systems (ITS) are particularly promising. ITS have emerged as the symbol of smart
cities [21]. The next generation of transportation networks heavily rely on the intelligent systems that
can deliver reliable, low-cost, energy efficient transportation services.
Considering the improvements in transportation infrastructure and Information Technology
(IT), the relationship between vehicles, road networks, and people need to be reevaluated in a novel
approach. This multifaceted approach will result in improving the order and control of transportation
systems by making the transportation management systems more efficient, convenient, safe, and
intelligent. The ITS-enabled solutions such as traffic management and congestion control can also be
employed for urban energy management.
Environmental changes are affecting cities and their inhabitants more regularly. Therefore, city
planners need to satisfy the need to improve air and water quality, and control noise pollution to create
a healthy and enjoyable environment for city inhabitants [22,23]. During the last decades, climate
change has become a greatly discussed topic. Globally, transportation accounts for 25 percent of
Sustainability 2020, 12, 5584 3 of 17
all black carbon emissions, of which diesel engines account for approximately 70 percent. The U.S.
produces
Sustainability 2020, approximately 6.1 percent
12, x FOR PEER REVIEW of the world’s fossil fuel and biofuel soot, and 3on-road
of 18 vehicle
emissions are expected to decrease by as much as 90 percent as federal fuel efficiency requirements
of allincrease
black carbon
[24].emissions,
Therefore,ofpolicymakers
which diesel engines account pushing
are primarily for approximately
for more70 percent.vehicles,
efficient The U.S. alternative
produces approximately 6.1 percent of the world’s fossil fuel and biofuel soot, and on-road vehicle
fuels, and reducing Vehicle Miles Traveled (VMT) in order to reduce CO2 emissions [25]. Manufacturers
emissions are expected to decrease by as much as 90 percent as federal fuel efficiency requirements
have focused on building vehicles in order to improve powertrain efficiency and introduce alternative
increase [24]. Therefore, policymakers are primarily pushing for more efficient vehicles, alternative
technologies such as hybrid and fuel cell vehicles. Alternative fuel possibilities include many low-carbon
fuels, and reducing Vehicle Miles Traveled (VMT) in order to reduce CO2 emissions [25].
options such as biofuels and synthetic fuels [12,13]. However, less attention is always taken on reducing
Manufacturers have focused on building vehicles in order to improve powertrain efficiency and
CO2 emissions
introduce alternativeby reducing traffic
technologies such ascongestion.
hybrid andFuel consumption
fuel cell and consequently
vehicles. Alternative CO2 emissions are
fuel possibilities
increased as traffic congestion increases [26]. Therefore, congestion mitigation programs
include many low-carbon options such as biofuels and synthetic fuels [12,13]. However, less attention should focus
on reducing
is always taken onCO 2 emissions.
reducing CO2 emissions by reducing traffic congestion. Fuel consumption and
consequently CO2 emissions are increased as traffic congestion increases [26]. Therefore, congestion
2.1. Comprehensive
mitigation Modal
programs should Emissions
focus ModelCO
on reducing (CMEM)
2 emissions.
Since 1996, the Comprehensive Modal Emissions Model (CMEM) has resulted in a variety of
2.1. Comprehensive Modal Emissions Model (CMEM)
vehicle emission and energy studies [27], focusing on fuel consumption. Such microscale modeling
Since 1996,
helps the Comprehensive
in predicting fuel consumptionModal Emissions Model (CMEM)
patterns according has resulted
to various in a variety of
traffic scenarios.
vehicle emission and energy studies [27], focusing on fuel consumption.
The model has been developed to interface with a wide variety of transportation Such microscale modelingmodels and
helpsdatasets
in predicting fuel consumption patterns according to various traffic scenarios.
to provide detailed analysis of fuel consumption and to generate a regional inventory of
The model [28].
emissions has beenOnedeveloped
of the most to interface
important with a wideof
features variety
CMEM of transportation modelsdown
is that it can break and the entire
datasets
fuel to provide detailed
consumption analysis ofprocess
and emission fuel consumption
into components and to generate a regional
that correspond toinventory of
vehicle operation and
emissions [28]. One of the most important features of CMEM is that it can break down the entire fuel
emission production. These components and parameters vary according to vehicle type, engine,
consumption and emission process into components that correspond to vehicle operation and
emission technology, and level of deterioration. A significant advantage of this strategy is that many
emission production. These components and parameters vary according to vehicle type, engine,
of the breakdown components can be modified to predict the energy consumption of future vehicle
emission technology, and level of deterioration. A significant advantage of this strategy is that many
of themodels,
breakdownas well as their emissions
components and new
can be modified technology
to predict applications.
the energy consumption Sinceof2000,
futurethe CMEM method
vehicle
hasas
models, been
welldeveloped and maintained
as their emissions under the
and new technology sponsorship
applications. of2000,
Since the Environment
the CMEM method Protection
has Agency
been(EPA) [29]. and maintained under the sponsorship of the Environment Protection Agency (EPA)
developed
[29]. The CO2 emissions from transportation depend on various factors. Driving habits, such as
the
Thenumber of timesfrom
CO2 emissions a driver decides todepend
transportation accelerate, cruise, and
on various push
factors. the break,
Driving aresuch
habits, among the important
as the
number of times
factors a driver decides
significantly affecting to accelerate, cruise, andEach
the environment. pushtrip
the break,
includes are different
among thestages,
important
depending on
factors
thesignificantly affecting
driver’s behavior, thethe environment.
roadway Eachthe
type, and triplevel
includes different
of traffic stages, depending
congestion. on the the driving
Figure 1a shows
driver’s behavior, the roadway type, and the level of traffic congestion. Figure
speed over time in relation to CO2 emissions. The emitted CO2 can be adequately estimated by 1a shows the driving
speed theover time inprofile
velocity relationof to CO2trip
each emissions. The emitted
and detailed vehicle CO 2 can be adequately estimated by the
information. Figure 1b shows the relationship
velocity
between emissions and speed for typical traffic [26]. The graph can shows
profile of each trip and detailed vehicle information. Figure 1b be usedtheto relationship
examine how different
between emissions and speed for typical traffic [26]. The graph can be used to examine how different
traffic management techniques can affect vehicle CO2 emissions, knowing the vehicle travels at
traffic management techniques can affect vehicle CO2 emissions, knowing the vehicle travels at a
a constant steady-state speed. In addition, the University of California at Riverside has developed
constant steady-state speed. In addition, the University of California at Riverside has developed
emission models for different vehicle types, both in laboratory and in real-world traffic scenarios.
emission models for different vehicle types, both in laboratory and in real-world traffic scenarios.
These data data
These set the
set are are foundation
the foundation for estimating
for estimating the CO the2 CO 2 emissions
emissions for a for a wide
wide variety
variety of vehicles under
of vehicles
various driving conditions
under various driving conditions [26]. [26].
Figure 1. (a) Typical vehicle velocity patterns for different roadway types and conditions; (b) Possible
Figure 1. (a)
use Typicaloperation
of traffic vehicle velocity patterns
strategies for different
in reducing roadway
on-road types and conditions;
CO2 emissions (Barth and(b) Possible
Boriboonsomsin 2009).
use of traffic operation strategies in reducing on-road CO2 emissions (Barth and Boriboonsomsin
2009).
Sustainability 2020, 12, 5584 4 of 17
Sustainability 2020, 12, x FOR PEER REVIEW 4 of 18
TheThe transportation
transportation sector
sector is one
is one of the
of the largest
largest contributors
contributors (29%)
(29%) to anthropogenic
to anthropogenic Green
Green House
House
Gas (GHG) emissions [30]. Cars, trucks, commercial aircrafts, and railroads, among
Gas (GHG) emissions [30]. Cars, trucks, commercial aircrafts, and railroads, among other sources, all other sources,
all contribute
contribute to transportation
to transportation sectorsector end-use
end-use emissions.
emissions. Within
Within the the sector,
sector, the the light-duty
light-duty vehicles
vehicles
category (including passenger cars and light-duty trucks) is responsible for
category (including passenger cars and light-duty trucks) is responsible for 59% of GHG emissions,59% of GHG emissions,
which
which is by
is by farfar
thethe largest
largest amount,
amount, while
while medium
medium andand heavy-duty
heavy-duty trucks
trucks formform
thethe second
second largest
largest
category
category withwith
23%23% of emissions,
of emissions, as shown
as shown in Figure
in Figure 2b. Emissions
2b. Emissions decreased
decreased by 0.5 percent
by 0.5 percent from 2016from
2016 to 2017; this decline was largely driven by a reduction in fossil fuel combustion
to 2017; this decline was largely driven by a reduction in fossil fuel combustion emissions. GHG emissions. GHG
emissions
emissions in 2017
in 2017 werewere
13 13 percent
percent below
below 20052005 levels,
levels, as as shown
shown in in Figure
Figure 2a 2a [31].
[31].
Figure 2. (a) Sources of Green House Gas (GHG) emissions; (b) U.S. Transportation sector GHG
Figure 2. (a) in
emissions Sources
2017. of Green House Gas (GHG) emissions; (b) U.S. Transportation sector GHG
emissions in 2017.
The GHG emissions in the transportation sector increased more in the absolute terms than in
anyThe GHG
other emissions
sector in the transportation
(i.e., electricity sector increased
generation, industry, more
agriculture, in the absolute
residential, terms than
commercial) due toin an
anyincreased
other sector (i.e., electricity
demand for travel generation,
[32]. A typicalindustry, agriculture,
passenger vehicle residential, commercial)
emits approximately 4.6due to an
metric tons
increased
of CO2 demand
per year.for travel
This [32]. is
number A subject
typical topassenger vehicle
change with emits approximately
variations of fuel type and4.6 number
metric tons of
of miles
COdriven
2 per year. This Currently,
per year. number isan subject
average to passenger
change with variations
vehicle on the of fuel
road type about
drives and number
22 milesofper
miles
gallon
driven
and per year.
11,500 Currently,
miles per yearan[32].
average passenger vehicle on the road drives about 22 miles per gallon
and 11,500 miles per is
Eco-driving year [32].
a smarter and more fuel-efficient alternative, focusing on improving driving
Eco-driving is a smarter
habits, and vehicle treatment, use, and moreandfuel-efficient
selection. This alternative,
habituationfocusing
requiresonconsistent
improving driving of
practicing
habits, and vehicle treatment,
recommendations use, and
so that drivers selection.
internalize theThis habituation
eco-driving requiresTypically,
guidelines. consistentanpracticing
eco-driverofcan
recommendations so that drivers
achieve 5 to 33 percent internalize
improvements thefuel
in the eco-driving
economyguidelines.
by followingTypically, an eco-driver
eco-driving guidelinescan[27].
achieve
On the5 to 33 percent
other improvements
hand, eco-driving in theare
guidelines fuelnot
economy by following
easily accessible, eco-driving
and drivers oftenguidelines [27].
ignore its benefits.
OnCurrently,
the other there
hand,is eco-driving guidelines
no easily accessible toolare not easily
informing accessible,
urban citizens and
aboutdrivers oftentrip
their daily ignore
costsitsand
benefits. Currently,
the carbon emissionstherethey
is no easily
leave accessible
behind in thetool informing[27].
environment urban citizens
There aboutsolutions
are other their daily trip
to reduce
costs
COand the carbon
2 emissions, emissions
such they leave
as centralized behind in the
management environment
solutions and AI[27]. There are[33–35].
applications other solutions
There are
to currently
reduce CO emissions,
a 2couple such as centralized
of technologies management
that are used solutions
for determining and AI
emissions asapplications
follows: [33–35].
There are currently a couple of technologies that are used for determining emissions as follows:
2.1.1. Emission Calculations and Reduction
2.1.1. Emission Calculations
The Carbon Footprintand
is aReduction
free add-on application for Google Maps that automatically estimates
the total
The CO2 emissions
Carbon Footprint isresulting from application
a free add-on driving on the route suggested
for Google Maps thatby Google Maps
automatically [36]. This
estimates
theapplication measures the
total CO2 emissions emissions
resulting from in driving
Kilo Grams (kg)route
on the of COsuggested
2 and has notby been updated
Google Mapssince
[36]. October
This
2017 withmeasures
application a databasetheofemissions
8000 users, inwhich indicates
Kilo Grams (kg)weak
of COuser adaptability
2 and and interest.
has not been updated Another
since
form of
October 2017technology is truck of
with a database stop electrification
8000 users, whichforindicates
heavy-duty
weaktrucks. To lower emissions
user adaptability due to
and interest.
engineform
Another idling,
of private companies
technology is truckhave
stopincorporated
electrificationsystems throughout
for heavy-duty the United
trucks. States
To lower known as
emissions
dueElectrified
to engineParking Spaces (EPS).
idling, private The EPS
companies havesystems provide
incorporated access throughout
systems to resourcesthe
such as heating,
United States air
conditioning,
known and power
as Electrified Parkingappliances without
Spaces (EPS). Therequiring the truck
EPS systems to have
provide engine
access idling [37].such as
to resources
heating, air conditioning, and power appliances without requiring the truck to have engine idling
[37].
2.1.2. Travel Costs Calculations
Sustainability 2020, 12, 5584 5 of 17
2.1.2. Travel Costs Calculations
Trip Toll Calculator (Tollguru) is a free mobile application available for both Android and iOS that
provides calculations for tolls and gas costs in various countries such as the United States, Canada,
Mexico, and India. It also provides a Toll Application Programming Interface (API) that can be used
by developers trying to use their services in order to provide trucking freight operations, connected
vehicles, rideshare services, billing, and transportation modeling for toll roads [38]. The cheapest
route option per trip is also provided. However, one of the main problems of this application is that
it does not automatically fetch the local gas price per gallon of gasoline. The gas price needs to be
manually entered.
2.1.3. React Native for Mobile Applications
React Native is a cross-platform framework, developed by Facebook in 2015, to create mobile
applications targeting iOS and Android mobile phone operating systems while attempting to focus
primarily on JavaScript. But JavaScript is not limited to being used exclusively. A native code such as
Objective C and Java can be used for iOS and Android in order to leverage specific use cases such as
accessing the mobile phone’s hardware. The primary reason for selecting React Native is its flexibility
and ease of use.
Although technologies that determine transportation emissions and fuel costs already exist, very
few of them consider mass user adaptability. For instance, mobile and web applications such as Google
Maps and Waze help users to travel from one location to another, using time and distance as the only
optimization factors. Yet, other important factors such as trip cost based on fuel consumption and
CO2 emissions are ignored in both [39]. This encourages the ultimate goal of Vector Maps to achieve
extensive usage. The purpose of this study is to fill this gap. More specifically, this study seeks to
develop an analytic tool that provides users with well-informed choices, based on fuel consumption,
Green House Gas (GHG) emissions, and travel cost (fuel and toll) in addition to time and distance.
3. Methodology
The primary goal of our mobile application is to provide urban citizens with a smart, intuitive,
and effective way to record, monitor, and improve their decision making to select optimized trips.
The application provides knowledge about the impact of emissions on the environment, as well as
the most economic route for the trip. This will be via a cross-platform, iOS- and Android-based mobile
application. The proposed application is accomplished in three major stages of Recommendations,
Logging, and Displaying the best routes available that offer the least emissions intensive and more
optimized route for cost savings.
• Recommendations—This is a vital part of the application whereby the user gives recommendations
based on the searched mapped route. These recommendations include a more optimized route
for trip cost and an emissions reduction friendly route calculated by an algorithm that comes up
with a sustainability index, providing a better way to lower emissions. Specifically, the user is
notified whether the selected route is good (green) in terms of the metrics mentioned above or
red otherwise.
• Logging—During this stage, the application records the user’s route information such as
the starting location, destination route, and time logged which provides the emissions consumed
by the trip as well as the total cost of the trip including toll roads, if applicable.
• Displaying—The user is able to display weekly, monthly, and annual consumption data of both
emissions and trip costs. This part of the application is under the EcoStats tab in which the user
may navigate at any point in time. An aggregated data plotted on a graph helps to visualize
the appropriate emission as well as trip cost data.
User data privacy and protection is considered using Amazon Web Services (AWS), with AWS
Cognito used for the user store, and AWS DynamoDB for storing additional user data. The user
Sustainability 2020, 12, 5584 6 of 17
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Sustainability 2020, 12, x FOR PEER REVIEW 6 of 18
securely
and logs
Access in using the(IAM)
Management AWS Signature
temporaryVersion 4 algorithm
token which expiresand is later
hourly. authorized
Access to thisvia
data anisIdentity
strictly
and Access
enforced to Management
only those (IAM)
parts of temporary
Vector Maps token which
research andexpires hourly.
development. Access to this data is strictly
and Access Management (IAM) temporary token which expires hourly. Access to this data is strictly
enforced
enforced to those
to only only those parts
parts of of Vector
Vector Maps research
Maps research and development.
and development.
3.1. Architecture
3.1. Architecture
3.1. Architecture
This study develops a system for creating a mobile phone application in order to retrieve
This This
study
distance,
study
time,
develops
develops a system
a system
CO2 emissions, for for creating
andcreating
a mobile
toll waya for
mobile phone
phone
various
application
application
suggested
in order
to to
roadinalternatives.
order retrieve distance,
retrieve
Figure 3 shows
time,time,
distance, CO2 emissions, and and
CO2 application
emissions, toll way for various
toll way for suggested roadroad
alternatives. Figure 3 shows the high-level
the high-level architecture ofvarious suggested
the mobile applicationalternatives.
and major Figure 3 shows
components.
application
the high-level architecture
application of the mobile
architecture application
of the mobile and major
application components.
and major components.
Figure
Figure 3. Overall Application
3. Application
Overall Architecture.
Architecture.
Figure 3. Overall Application Architecture.
As shown
As shown in Figure
in Figure 3, the3, mobile
the mobile application
application and theandweb
the web services
services are connected
are connected via simple HTTP
via simple
As
(Hyper shown
Text in
Transfer Figure 3,
Protocol) the mobile application and the web services are connected
downvia simple
HTTP (Hyper Text Transfer Protocol)requests.
requests.TheThereact
reactnative
native mobile
mobile application
application isisbroken
brokendown into three
HTTP
main (Hyper
components:Text Transfer Protocol) requests. The react native mobile application is broken down
into three main components:
into three main components:
• React
• React
Redux:Redux:
A reactA native
react native
frameworkframework
handles handles
the statethe stateapplication.
of the of the application.
It comprises It comprises
of of
• React
reducers and Redux:
actions in A react
order to native
propagateframework
the state tohandles
all the
application state of
screens.
reducers and actions in order to propagate the state to all application screens. the application. It comprises of
reducers
• Axios:
• Axios:and
A react actions
Anative in order
reactcomponent
native to propagate
that
componentprovides the
thatthe state to
ability
provides to all
the application
make HTTP
ability screens.
to requests
make to external
HTTP web to external
requests
• Axios: A react
services. web services. native component that provides the ability to make HTTP requests to external web
• Mobile
services.
phone application logic components: The core logic of the application which comprises of all
• Mobile phone application logic components: The core logic of the application which comprises of all
• and
models screen
Mobile components.
phone applicationInlogic
addition to the high-level
components: The corediagram,
logic of Figure 4 shows the
the application AWS comprises
which used of all
models and screen components. In addition to the high-level diagram, Figure 4 shows the AWS
in the mobile phone application.
models and screen components. In addition to the high-level diagram, Figure 4 shows the AWS used
used in the mobile phone application.
in the mobile phone application.
Figure 4. Amazon Web Services (AWS) used in the mobile phone application.
Figure 4. Amazon Web Services (AWS) used in the mobile phone application.
Figure 4. Amazon Web Services (AWS) used in the mobile phone application.Sustainability 2020, 12, x FOR PEER REVIEW 7 of 18
The primary function of the AWS is to provide services for application’s users to store their
Sustainability 2020, 12, 5584 7 of 17
information such as user profile, logged trips, and survey data. Figure 4 shows how AWS services
work with the mobile phone application. The process includes the following steps:
The primary function of the AWS is to provide services for application’s users to store their
1. The user logs into the mobile phone application. For new users, a registration form is available.
information such as user profile, logged trips, and survey data. Figure 4 shows how AWS services
2. If authentication is successful, AWS Cognito authenticates the user and returns the user with Json
work with the mobile phone application. The process includes the following steps:
Web Tokens (JWT) containing user details such as the username, full name, and vehicle information
1. The user logs into the mobile phone application. For new users, a registration form is available.
(e.g., MPG for gas calculations).
2. If authentication is successful, AWS Cognito authenticates the user and returns the user with
3. In order to access AWS services such as API Gateway, the user needs to obtain IAM credentials.
Json Web Tokens (JWT) containing user details such as the username, full name, and vehicle
So, a request is sent
information to exchange
(e.g., the calculations).
MPG for gas JWT tokens obtained in step 2.
4.
3. IfInauthorization is successful,
order to access the user
AWS services suchisasreturned temporary
API Gateway, IAMneeds
the user credentials to perform
to obtain requests
IAM credentials.
So, a request is sent to exchange the JWT tokens
to AWS API Gateway. The temporary IAM tokens expire in 15 days. obtained in step 2.
4. HTTP
5. If authorization is successful,
requests (e.g., GET/items theand
userPOST/survey)
is returned temporary IAM credentials
can be performed to fetchtoorperform requests
store data such
to AWS API Gateway. The temporary IAM tokens expire in 15 days.
as logged route information as well as survey data that the user has input.
5. HTTP requests (e.g., GET/items and POST/survey) can be performed to fetch or store data such as
6. HTTP response of data is returned to the user in a structured JSON format.
logged route information as well as survey data that the user has input.
6. HTTP response of data is returned to the user in a structured JSON format.
3.2. Development
3.2. Development
The tools used for developing the mobile phone application are:
1. The tools WebStorm
JetBrains used for developing the mobile
IDE 2018—Noted phone
as the application
“smartest are: IDE” by the JetBrains site, this
JavaScript
1. isJetBrains
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Xcode 10.3 IDE—The iOS interactive development kit used to make specificmobile
code applications
targeted for
for Apple phones. It is used to build the source code and other iOS specific code targeted for iOS.
iOS. This is also a requirement to run and virtually simulate the mobile application.
This is also a requirement to run and virtually simulate the mobile application.
3.
3. Android
Android Studio
Studio 3.43.4 IDE—The
IDE—The Android
Android IDEIDE is
is the
the Android Operating System
Android Operating System forfor developing
developing
native
native mobile
mobile applications
applications specifically
specifically designed
designed forfor Android
Android phones.
phones. This
This needs
needs to
to build and
simulate aa virtual
simulate virtual Android
Android device
device to
to run
run the
the mobile
mobile application.
4.
4. React Native
React Native Debugger
Debugger 0.10—Tool,
0.10—Tool, also
also known
known as as Remote
Remote JS JS Debugging,
Debugging, is is used
used to
to debug
debug cross
cross
platform applications. It is a server-like application for Mac OS to listen to the debug traffic on
platform applications. It is a server-like application for Mac OS to listen to the debug traffic on
port 8081, when the mobile application is running.
port 8081, when the mobile application is running.
The flow
The flow diagram
diagram in in Figure
Figure 55 shows
shows the
the sequence
sequence that
that the
the application
application runs
runs on.
on.
Emission
Load Profile
Computations
List Available Render Map
Start Search Direction
Routes with Routes
End
Search Local Travel Cost
Gas Price Calculations
Process Data
General flowchart
Figure 5. General flowchart of application load and directions look up.
3.3. Functional Specification
3.3. Functional Specification
In this research, a comprehensive methodology is developed to measure vehicles CO emissions
In this research, a comprehensive methodology is developed to measure vehicles CO22 emissions
and its relationship with traffic congestion, optimizing route selection as an alternative to Google
and its relationship with traffic congestion, optimizing route selection as an alternative to Google
Maps. With this methodology, we can estimate how congestion mitigation programs can reduce CO
Maps. With this methodology, we can estimate how congestion mitigation programs can reduce CO22
emissions and consequently improve sustainability.
emissions and consequently improve sustainability.Sustainability 2020, 12, 5584 8 of 17
3.3.1. Travel Time and Distance
The Google Maps API gets multiple routes based on the integrated depending factors including
time and distance. Then, a color coding similar to Google Maps’ is used to show the traffic congestion
levels. The red color shows the heavy traffic, orange color shows the light traffic, and blue color shows
no traffic. The pseudo code for extracting time and distance information is shown in Algorithm 1.
Algorithm 1. Pseudo code for extracting time and distance information.
Input: 1. Current user GPS position (latitude, longitude).
2. Destination position searched by place, calculated in coordinates (latitude, longitude).
Output: List of n routes {r0 . . . rn } with route properties (e.g. arrival time, distance in miles).
1 get routes from Google API based on the initial user’s position and destination position.
2 for each route r in routes {r0 . . . rn }
3 set rad the average duration of the route in minutes
4 set rtd traffic_duration of the route in minutes
5 set r gd distance in miles
6 for each encoded polyline of r
7 decode encoded polyline
8 set rdp decoded polyline
9 return list of routes {r0 . . . rn } with the set values.
3.3.2. Travel Cost
In this research, the travel cost is calculated based on the current local gas price in the industry
and travel distance. The current local gas price is calculated by GasBuddy service, based on the user’s
address or Zip code [40]. Hence, the application mainly searches local gas prices based on the user’s
location. Once the list of gas prices is obtained, the application calculates and displays the average gas
price for each trip, according to the pseudo code for extracting cost information shown in Algorithm 2.
Algorithm 2. Pseudo code for extracting cost information.
Input: Distances rd in miles of the route. Local gas price g in dollar amount.
Output: Total trip cost c in the dollar amount of route
1 get rd distance from route and gas price g
2 calculate and set gallons of gas per distance route r gd
3 rc < r gd x g
4 return rc
3.3.3. Gas Emission
Equation (1) shows the core logic for determining CO2 emission calculations in the proposed
mobile application according to the Environmental Protection Agency [31].
CO2 per gallon
CO2 Emissions in grams = × Travelled Distance × Driving Style (1)
MPG
where MPG (Mile per Gallon) is determined by MPG of user’s vehicle based on the user’s selected
profile vehicle. Driving style is either normal driving or aggressive driving with values of 1 and 1.15,
respectively. Traveled distance is the total travel distance in miles. The CO2 emissions per gallon of gasoline
and diesel are reported annually by the EPA. In order to calculate more accurate CO2 emissions in
grams, the developed application updates CO2 emissions from a gallon of gasoline and diesel from
the EPA website directly. Once the emissions value is calculated by grams of CO2 , the next step is to
calculate the sustainability index. The sustainability index used in our application is inspired by [26],
as shown in Figure 6.Sustainability 2020, 12, 5584 9 of 17
Sustainability 2020, 12, x FOR PEER REVIEW 10 of 18
Figure 6.
Figure Emissions vs.
6. Emissions vs. speed
speedplot
plotof
ofindividual
individualtrips
trips [26].
[26].
The sustainability index derived from Figure 6 is then divided into three categories as shown in
The sustainability index derived from Figure 6 is then divided into three categories as shown in
Table 1. In this research, we consider the highway miles of the route as a variable. Hence, the overall
Table 1. In this research, we consider the highway miles of the route as a variable. Hence, the overall
sustainability index can be determined by considering the fact that the trip contains more than 75
sustainability index can be determined by considering the fact that the trip contains more than 75
percent of highways and the speed in MPH (miles per hour) is calculated based on the severity of
percent of highways and the speed in MPH (miles per hour) is calculated based on the severity of the
the traffic status. The overall sustainability factor then is determined based on the traffic status as is
traffic status. The overall sustainability factor then is determined based on the traffic status as is
shown in Table 1. It is important to notice that the traffic severity value is color-coded as red, green
shown in Table 1. It is important to notice that the traffic severity value is color-coded as red, green
and orange for 0–39, 40–70, and +70 mph, respectively, according to the traffic status. An index of (0)
and orange for 0–39, 40–70, and +70 mph, respectively, according to the traffic status. An index of (0)
enhances the chance to promote a lowering of CO emissions.
enhances the chance to promote a lowering of CO22 emissions.
Table 1. Sustainability index based on speed and traffic status.
Table 1. Sustainability index based on speed and traffic status.
Sustainability Index Speed (mph) Traffic Status
Sustainability Index Speed (mph) Traffic Status
−1−1 0–39
0–39 City/Highway
City/Highway Traffic
Traffic
0 40–70 No Highway Traffic
0 40–70 No Highway Traffic
1 More than 70 No Highway Traffic
1 More than 70 No Highway Traffic
The sustainability
The sustainability color
color codes
codes for
for this
this application
application are
are determined
determined in
in order
order to
to compare
compare the
the CO
CO22
emission of each route to the same destination. The green color shows that the route is sustainable,
emission of each route to the same destination. The green color shows that the route is sustainable,
while the
while the red
redcolor
colorshows
showsthat
thatthe
theemissions
emissionsofof
CO CO 2 are
2 are high.
high. Algorithm
Algorithm 3 shows
3 shows the extraction
the extraction gas
gas emissions.
emissions.
Algorithm 3. Pseudo code for extracting gas emissions information.
Input: The route r to be used for calculations.
Output: The sustainability index of 0 and −1 for the best and worst emission factors, respectively.
1 if rhas_hwy then:
2 if rdistance_hwy / rtotal_distance ≥ 0.75 and if rtra f f ic_severity is green (good):
3 return 0
4 otherwise:
5 return −1
6 otherwise (the route comprises of street thus):
7 return −1Sustainability 2020, 12, 5584 10 of 17
3.3.4. Tollway Calculation
TollGuru is a free mobile application available for both Android and iOS that provides calculations
for tolls and gas costs in various countries such as the USA, Canada, Mexico and India. They also
provide a Toll API to be used by third party developers or companies in order to provide trucking
freight operations, connected vehicles, rideshare services, billing, and transportation modeling for toll
roads [38]. They also provide the cheapest route to save money on trips. However, TollGuru does not
automatically fetch the local gas price per gallon of gasoline. The gas price has to be manually typed in
and can give unpredictable results if it is mistakenly input.
Google Maps API is able to determine if there are toll roads of the trip. However, it is not able to
determine the toll cost. Initially it was difficult to determine the toll costs per trip due to the lack of free
resources. Lately, TollGuru API can help to calculate the toll costs per trip. TollGuru API provides toll
road information given a source and destination distance of the trip. If the trip does not contain a toll
road, then it will give an empty response. However, if the trip contains toll roads, it will return a list of
toll prices per each route of the trip. Algorithm 4 shows the process of how the tolls are displayed to
the user.
Algorithm 4. Pseudo code for extracting toll way information.
Input: The route r to be used for calculations.
Output: Calculation of the trip’s toll prices.
1 if rtoll price response_hwy then:
2 if rdistance_hwy /rtotal_distance ≥ 0.75 and if rtra f f ic_severity is green (good):
3 return 0
4 otherwise:
5 return −1
6 otherwise (the route comprises of street thus):
7 return −1
Notice that there are two types of toll to consider: FasTrak and One-Time-Toll. According to
the Toll Roads website, FasTrak is “an electronic tolling account that allows drivers to pay tolls
automatically from a pre-established account that can be prepaid and replenished using a credit card,
cash or check. In this research, both toll types are displayed to the user. While FasTrak may offer
special discounts for registered users, One-Time-Toll is used for anyone willing to pay the toll price as
a one-time deal.
3.4. Application Graphical User Interface (GUI)
As mentioned before, the proposed mobile application is developed in React Native. React Native
is a cross-platform framework developed by [41] to create mobile applications targeting iPhone and
Android mobile phone operating systems while focusing primarily on JavaScript. While JavaScript
is not only limited to being used exclusively, native code such as Objective C and Java for iOS and
Android can be used in order to leverage specific use cases such as accessing a mobile phone’s
hardware. The React Native is chosen because of its flexibility and ease of use to develop and evaluate
this application.
When the mobile application is opened, the Login Screen shows up, asking for users’ login
information. It allows users to create an account as shown in Figure 7a. After each user creates an
account, the main screen labeled ‘Home’ will be shown.phone’s hardware. The React Native is chosen because of its flexibility and ease of use to develop and
evaluate this application.
When the mobile application is opened, the Login Screen shows up, asking for users’ login
information.
Sustainability It 12,
2020, allows
5584 users to create an account as shown in Figure 7a. After each user creates
11 an
of 17
account, the main screen labeled ‘Home’ will be shown.
Figure 7.
Figure Anoverview
7. An overviewofofthe
themobile
mobileapplication
applicationperformance.
performance.
Many users are
Many users are familiar
familiar with
with the
the mobile
mobileversion
versionofofGoogle’s
Google’sand
andApple’s
Apple’smap
mapapplications.
applications.
Therefore, the Home screen as shown in Figure 7 brings them the same experience.
Therefore, the Home screen as shown in Figure 7 brings them the same experience. When theWhen theHome
Home
screen
screen is
is launched, it performs
launched, it performs three
three steps:
steps:
•• If
If application
application isis launched
launched forforthethefirst
firsttime,
time,ititwill
willask
askififthe
theuser
userallows
allows toto access
access current
current location.
location.
Note that this notification is a privacy mandate by mobile application development
Note that this notification is a privacy mandate by mobile application development as it is shown as it is shown
in
in Figure
Figure 7b.
7b.
•• The local
local gas
gas price
price is
is loaded
loaded based
based ononuser’s
user’s current
current location
locationZip Zipcode
codeas asititisisshown
shownininFigure
Figure7c.
• 7c. current user location is automatically loaded.
The
• The current user location is automatically loaded.
By choosing the ‘Profile’ tab in the application, user can navigate to the ‘Profile’ screen as it is shown
By choosing
in Figure the ‘Profile’
7d. The Profile screentab in the
shows application,
details about the user canprofile
user’s navigate to the ‘Profile’
information whereinscreen as it will
the user is
shown
enter in Figure
their 7d.
vehicle The Profile
MPG. screenthe
By choosing shows details
search about
results the user’s
as shown profile7e,
in Figure information
it displayswherein the
the statistics
user
of thewill enter their
application vehicle
general MPG.This
usage. By choosing
includes the aggregation
search results as shown
data in Figure
of the daily 7e, it displays
emissions. Figure 7f
the statistics
shows of the
the Survey application
screen, which general
includesusage. This includes
two options of Pre-drivethe aggregation
and After drive. data Pre-drive
of the daily asks
emissions. Figure 7f shows the Survey screen, which includes two options of
questions only related to before driving in order to measure the level of impact on users experiencing Pre-drive and After
drive.
the Pre-drive
mobile asks questions
application. Afteronly
drive related
doesto sobefore driving
for after using in order to measure the
the application. Theselevelquestions
of impact help
on
understand the users’ experience prior to and post using the application. Figure 7g,h show the vital part
of the application whereby the user is given recommendations based on the searched mapped route.
These recommendations include the most optimized route for travel cost and a lower emission route
calculated by an algorithm following a sustainability index. Furthermore, the user is notified whether
the route user has selected is good (green) or not (red), according to the metrics mentioned earlier.Sustainability 2020, 12, 5584 12 of 17
The participants also have the ability to see all the information and select the most optimized
route based on their own perception. After using the application for at least two weeks, they are given
the second round of the questionnaire with the same questions to assess how they are affected prior to
and after using the application.
4. Case Study
As a case study, the mobile phone application was used as a survey tool for collecting information
and user data. To achieve the research aim and to gain deeper understanding of the mobile phone
application impacts, 20 participants were selected from California State University, Long Beach. Before
the experiment, consent forms were approved by the participants assuring the confidentiality of
their data, using selected AWS services (AWS Cognito for the user store and AWS DynamoDB for
storing additional user data). The user securely logs in using AWS Signature v4 algorithm and
is later authorized via Identity and Access Management (IAM) temporary tokens. Data access is
strictly limited to the researchers and developers of the mobile phone application for application
development purposes.
Participants were asked to provide their personal information as well as their experience with
other navigation apps like Google Maps or Apple Maps. Then they were asked to rank four essential
features studied in this research before using the application. Based on their priority, they ranked
features from 4 to 1 (4 being the most and 1 being the least important).
When participants log into the application, it starts to record the user’s route information such as
departure and arrival locations and login duration, which is later used to calculate CO2 emissions per
trip, as well as the total travel cost including toll roads. Each user was asked to use the application for
at least two weeks in order to make sure that the application is used in various situations such as rush
hours, and under various weather scenarios. The users complete both pre- and post-questionnaires,
and their trip information are stored in Rockset, a third-party cloud tool, for further analysis.
5. Results and Discussion
Data analysis includes two main steps. The first step is to validate the accuracy of CO2 emissions
calculated by the application. The second step is to measure the mobile phone application impact on
users before and after using the application. Further details are discussed in the following sections.
5.1. Modeled Emissions
Validation is crucial in assessing the accuracy of the application input. This is achieved by
calculating the trip CO2 emissions according to Equation (1) and comparing it to the EPA’s public CO2
emissions data for different vehicles [42].
After defining emission factors in the application to measure the CO2 emissions, five cars with
different makes, models, years built, and known MPG were selected to measure the accuracy of CO2
emissions compared to the EPA’s public CO2 data. Table 2 represents this comparison.
Table 2. Comparison of CO2 emissions between the mobile phone application and the EPA’s
CO2 emissions.
Greenhouse Gas Greenhouse Gas Emissions Absolute
Vehicle
Emissions per the EPA per Equation (1) Difference (%)
2019 Honda Civic 248 g/mile 246 g/mile 0.81
2017 Toyota Corolla 263 g/mile 277 g/mile 5.32
2007 Toyota Prius 193 g/mile 211 g/mile 9.32
2018 Honda Civic 247 g/mile 246 g/mile 0.40
2015 Dodge Charger 389 g/mile 386 g/mile 0.77Sustainability 2020, 12, 5584 13 of 17
The results show that there is less than 10% difference between the CO2 emission calculated
by the application comparing to the EPA data. The small difference can be caused by the variance
in vehicles ages and particular condition of their engine, tire, etc. Therefore, it can be concluded
that the application is reliable enough in calculating CO2 emissions, as a decision factor presented to
the users to choose more environmentally friendly trip routes.
5.2. Survey Results
In order to compile and aggregate the results from the application, the data is directly stored
in a third-party cloud tool called Rockset. It helps ingest the AWS data, where all the users’ data is
stored, facilitating the query and analytics. The data includes the users’ trip data (i.e., trip cost and
CO2 emissions) and their questionnaire responses exported as a csv file.
Tables 3 and 4 show the survey results before and after using the application. Including the change
percentages for each transportation factor. In addition, the overall result is shown in Figure 8, comparing
the average ranking of pre-survey and after survey results. Figure 8 illustrates the impact of the mobile
phone application on the users’ decision making. As can be seen, most of the participants still consider
time and traffic as their chief priority in selecting routes. However, we notice that the application has
a positive impact on the other transportation factors. After using the application, participants are more
inclined to consider CO2 emissions, fuel prices, and tollways in selecting their routes. Particularly,
it seems that the application has made the users more environmentally conscious. As can be seen
in Figure 8, after using the application, the travel time and traffic has a reduced priority for some of
the users, which are informed of the slight difference in time and traffic levels for various route options.
The same reasoning can explain the decrease of the number of users who prioritized the fuel price after
using the application. Most of the routes determined to be more fuel efficient are in fact no different
than others with respect to fuel consumption. The application’s impact on the priority of tollway can
be skewed by the number of tolls on the road. Further information on the number of tolls can give us
a better understanding of the application’s impact on prioritizing tollways in decision-making.
Table 3. The survey results before using the mobile phone application.
Ranking 4 3 2 1
15 4 1 0
Time and Traffic
75% 20% 5% 0%
0 2 10 8
CO2
0% 10% 50% 40%
5 12 2 1
Fuel Price
25% 60% 10% 5%
0 2 7 11
Tollway
0% 10% 35% 55%a third-party cloud tool called Rockset. It helps ingest the AWS data, where all the users’ data is
stored, facilitating the query and analytics. The data includes the users’ trip data (i.e., trip cost and
CO2 emissions) and their questionnaire responses exported as a csv file.
Tables 3 and 4 show the survey results before and after using the application. Including the change
percentages for12,each
Sustainability 2020, 5584 transportation factor. In addition, the overall result is shown in Figure 14 of 8,
17
comparing the average ranking of pre-survey and after survey results. Figure 8 illustrates the impact
of the mobile phone application on the users’ decision making. As can be seen, most of the
Table 4. The survey results after using the mobile phone application.
participants still consider time and traffic as their chief priority in selecting routes. However, we
notice that the application
Ranking has a positive 4 impact on the 3 other transportation
2 factors.1 After using the
application, participants are more inclined 12 to consider4 CO 2 emissions, fuel prices, and tollways in
3 1
selecting theirTime and Traffic
routes. Particularly, it seems that the application has made the users more
60% 20% 15% 5%
environmentally conscious. As can be seen in Figure 8, after using the application, the travel time and
3 4
traffic has a reducedCO priority
2 for some of the users, which are informed9 of the slight 4difference in time
and traffic levels for various route options.15% The same20% reasoning can
45% explain the 20%decrease of the
number of users who prioritized the5 fuel price after 9 using the application.
4 Most2 of the routes
Fuel Price
determined to be more fuel efficient 25% are in fact no
45% different than
20% others with
10% respect to fuel
consumption. The application’s impact on the priority of tollway can be skewed by the number of
0 3 4 13
Tollway information on the number of tolls can give us a better understanding of
tolls on the road. Further
0% 15% 20% 65%
the application’s impact on prioritizing tollways in decision-making.
40%
BEFORE AFTER
35%
Average Users' Priority
30%
25%
20%
15%
10%
5%
0%
Time & Traffic CO2 Fuel Price Toll Road
Figure 8. Results before and after using the application.
Furthermore, the users of the proposed application can make their own choice of route selection
criteria from the available alternatives. For instance, if the user chooses time as the sole selection
criterion, only faster route alternatives are given, neglecting the other two factors. On the other hand,
if the user is concerned about time and fuel cost as a sustainable alternative, the user has the option to
choose the route with the combination of less time and lower costs.
6. Conclusions
This research and proposed vector maps application show the advantage of discharging open
information on adaptive transportation alternatives in urban areas. By providing a more clear vision
of travel route alternatives, users can better decide which route to choose according to their own
preferences on travel time, fuel consumption, CO2 emissions, and tolls. Users that participate in
route selection based on their own preferences, rather than traveling on a given route, help to make
urban transportation smarter. Finally, it is shown that the developed mobile phone application has
the ability to provide a new dimension in transportation route selection, while addressing the need for
more effective decision factors in smart cities transportation. This application can also be an effective
tool in route planning to provide an efficient and economical transportation system if utilized by
the Department of Transportation and engineers. The purpose of this research is developing the mobile
phone application is to help urban citizens in their daily transportation. Although the development
scope is limited (i.e., there are navigation restrictions in Google Maps API), below are a list of objectives
that can be achieved by continuing this research:
1. Adding emissions/trip cost graphs for daily, monthly, and annual time increments.You can also read
