Analyzing Urban Mobility Carbon Footprint with

Large-scale, Agent-based Simulation

Eduardo Felipe Zambom Santana

, Lucas Kanashiro

, Diego Bogado Tomasiello

Fabio Kon

and Mariana Giannotti

Department of Computer Science, University of S

ao Paulo, Brazil

Department of Transportation Engineering, Polytechnic School, University of S

ao Paulo, Brazil

Keywords:

Smart Cities, Urban Mobility, Simulation, Carbon Footprint.

Abstract:

The growth of cities around the world bring new challenges to urban management and planning. Tools, such

as simulators, can help the decision-making process by enabling the understanding of the current situation of

the city and comparison of multiple scenarios with regard to changes in the urban infrastructure and in public

policy. This paper presents an analysis of mobility parameters, such as distance, cost, travel time, and carbon

footprint, for different simulated scenarios in a large metropolis in a developing country. We simulated the

scenarios using an open source, large-scale, agent-based Smart City simulator that we developed.

1 INTRODUCTION

Most large cities around the world, especially in de-

veloping countries, have signiﬁcant problems with re-

gard to the mobility of their inhabitants; normally,

the low-income populations in underprivileged neigh-

bourhoods are the ones that suffer the most. A valid

approach to tackle this problem is the idea of Smart

Cities, that proposes, among other things, the use of

Information and Communication Technologies (ICT)

to improve the quality of life and sustainability in

cities.

On the one hand, intelligent information systems

working in conjunction with the city infrastructure

can provide applications based on the collection and

analysis of real-time data offering services to the pop-

ulation and city servants to mitigate mobility difﬁ-

culties. On the other hand, planning and decision-

support tools can help city managers take better deci-

sions in the long run and design more effective public

policies. Simulators can be a valuable tool for un-

derstanding the behavior of the city and analyzing the

impact of changes in the city infrastructure and public

policies.

A simulation can show to city planners the be-

haviour and dynamics of the city in different hypo-

thetical scenarios. For example, a servant working in

the Transportation Secretariat could simulate the im-

pact of building different subway lines across the city

or the impact of locating subway stations in different

places. A servant working in the Health Secretariat

could simulate the impact of changing the medical

specialties offered in a certain city hospital. Well-

designed simulations can be instrumental in planning

changes in the mobility infrastructure and in govern-

ment actions, enabling informed decision-making.

The work presented here involves a case study in

using a large-scale, agent-based trafﬁc simulator to

analyze the impact of a new subway line under con-

struction in S

ao Paulo, Brazil. We examined four sim-

ulated scenarios based on an origin-destination survey

and compared their travel time, ﬁnancial cost, and car-

bon footprint of the simulated population. We based

all the scenarios on realistic changes that might occur

with the new subway line. In this study, we consid-

ered the inhabitants of a large slum in the city that

will be potentially beneﬁted by this new subway line.

We chose to compare the travel time and cost,

which can impact positively on the population qual-

ity of life. The carbon footprint can help the an-

alyzes of the city sustainability, measuring the im-

pact of each transportation mode. Normally, the car-

bon footprint is measured by the traveled distance

multiplied by a constant number based on the trans-

portation mode. After studying several carbon foot-

print models (Kenny and Gray, 2009), we choose one

that is well-detailed (Chester and Horvath, 2008) and

have already an implemented version (Shankari et al.,

Santana, E., Kanashiro, L., Bogado Tomasiello, D., Kon, F. and Giannotti, M.

Analyzing Urban Mobility Carbon Footprint with Large-scale, Agent-based Simulation.

DOI: 10.5220/0006662201430150

In Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2018), pages 143-150

ISBN: 978-989-758-292-9

143

2014).

This paper is organized as follows. Section 2

presents related work in Smart Cities, Transportation,

and Agent-Based Simulators. Section 3 describes the

simulated scenarios and the data used in their model-

ing. Section 4 describes the tool used to simulate the

scenarios. Section 5 presents the data collected and

analyzed from the simulation, it also discusses the re-

sults of the analyses. Finally, Section 6 addresses our

conclusions and future work.

2 RELATED WORK

We now present some of the most relevant work re-

lated to our research, separating them in the Smart

Cities, Transportation, and Agent-Based Simulation

areas.

2.1 Smart Cities

“Smart City” has been widely and variously deﬁned.

Some deﬁnitions exceed the software context, focus-

ing only on social or business aspects. Regarding soft-

ware systems, many authors describe a Smart City as

the integration of social, physical, and IT infrastruc-

tures to improve the quality of city services (Caragliu

et al., 2011). Other authors focus on a set of Infor-

mation and Communication Technology (ICT) tools

used to create an integrated Smart City environment

(Santana et al., 2017a; Washburn et al., 2009).

(Gifﬁnger et al., 2007) assert that a Smart City has

six main dimensions: smart economy, smart people,

smart governance, smart mobility, smart environment,

and smart living. Many authors adopt this deﬁnition

(Hern

andez-Mu

noz et al., 2011). In our work, we fo-

cus on two dimensions: smart governance and smart

mobility.

Smart Governance is related to a better manage-

ment of cities by using tools to improve the planning

of governmental initiatives such as modiﬁcations in

the infrastructure and public policies. Smart Mobility

is related to actions that facilitate the movement of the

population within the city. Both dimensions can ben-

eﬁt from the use of simulators. For example, allowing

the understanding of the current trafﬁc conditions and

the impacts of changes in the infrastructure and the

behavior of citizens.

There are many works proposing tools to facili-

tate the management of Smart Mobility actions. For

example, (Schnemann, 2011) presents a platform to

simulate vehicular networks with Intelligent Trans-

portation Systems (ITS), testing and experimenting

with current and future mobility scenarios. (Benevolo

et al., 2016) relate different smart mobility initiatives,

including the use of tools to facilitate the planning,

implementation, and evaluation of integrated mobil-

ity initiatives.

2.2 Transportation

The transportation systems in developing countries

metropolis share several problems. Most of them are

related to the sudden increase in the urban popula-

tion, poor land use and transport policy, and relatively

small investments in transport infrastructure (Pucher

et al., 2005).

A common consequence of the poor public trans-

port infrastructure is the mass migration to the motor-

ized private transport, which is the root of other prob-

lems like trafﬁc congestion, air pollution, and trafﬁc

accidents (Salon and Gulyani, 2010).

When it comes to slums in developing countries,

all the problems are enlarged. According to (Car-

ruthers et al., 2005), the main issues faced by low-

income populations in developing countries are the

long trips and travel times, lack of fare system integra-

tion, and lack of public transit supply in the outskirts

of municipalities.

Mass public transport infrastructure like metro

systems is rarely designed to serve low-income neigh-

borhoods due to the need for subsidies to make fares

cost-effective (Gwilliam, 2003). However, studies

conducted by (Zegras, 2010), identiﬁed that higher

urban density areas located close to metro stations are

related to a lower number of kilometers traveled by

vehicles, decreasing air pollution and, consequently,

the carbon footprint.

The analysis of the impact of mass public trans-

port infrastructure for the population with respect to

travel distance, travel time, and air pollution is vital

to anticipate if the investments are going to be effec-

tive, bringing real beneﬁts to citizens. Agent-based

simulation is one of the tools to help decision makers

in this matter.

2.3 Agent-based Trafﬁc Simulation

Agent-Based simulation is widely used to model traf-

ﬁc scenarios (Bazzan and Kl

ugl, 2014). An example

is MATSim (Horni et al., 2016), a mesoscopic multi-

agent trafﬁc simulator. In this simulator, each person

is modeled as an agent that can move around the city.

MATSim uses a queue model to simulate the trafﬁc

using the ﬂow and storage capacity of each link to cal-

culate the speed of the vehicles. MATSim was used

to simulate many city scenarios such as taxi optimiza-

tion (Maciejewski and Nagel, 2013), freight trafﬁc

SMARTGREENS 2018 - 7th International Conference on Smart Cities and Green ICT Systems

144

(Zilske et al., 2012), and autonomous cars (Bischoff

and Maciejewski, 2016).

SUMO (Behrisch et al., 2011) is a microscopic

trafﬁc simulator that also simulates each agent indi-

vidually. The difference is that SUMO uses a Car-

Following model (Tang et al., 2014) to simulate the

trafﬁc ﬂow. In this simulation type, car speed is calcu-

lated considering the vehicles ahead. Usually, micro-

scopic simulators are more detailed. However, they

have a high computational cost and are not suitable

for the simulation of a large metropolitan area with

millions of agents.

(Song et al., 2017) present a mesoscopic traf-

ﬁc simulator based for GPUs (Graphical Processing

Unit). Its aim is to use the processing power of GPUs

to speed up the execution of large-scale trafﬁc scenar-

ios. The results presented in the paper showed a two

times improvement in the execution time of the sim-

ulation compared to a standard C++ implementation

of the simulator. However, the authors describe two

main problems in using GPUs: the communication

of the CPU and the GPU is a bottleneck to the sim-

ulation and the amount of memory of the GPU can

signiﬁcantly limit the size of the simulated scenario.

Many academic and city management projects use

the simulators mentioned in this section. However,

none of them is suitable for the simulation of an entire

day of a large metropolis with several million users in

multiple modes of transportation such as cars, sub-

ways, trains, buses, bicycles, and pedestrian. There-

fore, in our work, we implemented a ﬂexible and pow-

erful smart city simulator, capable of simulating more

than ten million agents using multimodal trips. This

simulator will be presented in Section 4.

3 SCENARIO DESCRIPTION

The area of interest is Parais

opolis, a large slum in

ao Paulo with a population of approximately 50

thousand people. Besides several speciﬁc character-

istics, this slum differs from others mainly because

of its location. While most of the slums of the city

are on the periphery, this one is located in a central

area, making access to transport and other services

less costly.

The mobility characteristics of the analyzed pop-

ulation is changing dramatically in the last decades.

The motorization ratio is increasing rapidly. In 2007,

the number of motorized trips was only 10%, while in

2011 it raised to 33%.

In the future, the mobility of the slum region might

change with the governmental plan to build two new

subway stations that are going to connect the slum to

other four metro lines and several bus corridors of the

city.

Due to the changes in the public transport offer

in the next years, the importance of simulating trans-

port scenarios considering different modal splits and

its impacts in carbon footprint, travel time, and travel

cost is essential to understand the implications of the

new transport infrastructure.

3.1 Collected Data

We based our simulation in real data collected from

different sources. The databases considered in the pa-

per are:

• Origin-Destination (OD) Matrix derived from a

survey conducted by the city subway company for

the year 2007

• Shapeﬁle of the planned Subway Lines

• Map of the city based on OpenStreetMaps

• Transit lines and stops for S

ao Paulo

The origin-destination database provided the in-

formation of all the trips originated in the slum. Each

trip has the origin, destination, travel mode, and start

time. As the trips are the most important data for the

simulation, we made a exploratory analyses of this

data.

Figure 1: Travel Start Time.

Figure 1 presents the distribution of the trips by

the start time across the day. The ﬁgure shows that

most of the trips occur in the morning, when people

leave to work. Changing the morning trips from cars

Origin-Destination Survay - http://goo.gl/Te2SX7

OpenStreetMap - https://www.openstreetmap.org

Analyzing Urban Mobility Carbon Footprint with Large-scale, Agent-based Simulation

145

or buses to subway can improve the trafﬁc and avoid

the overcrowded buses. Also, there is a signiﬁcant

number of late night travels, the new line can beneﬁt

the bus users because the interval between the buses

in the night are much larger than during the day.

3.2 Data Preparation

Based on the panorama described in the last sec-

tion, we created four simulation scenarios to com-

pare changes in the modal split considering the new

proposed subway line. To create these scenarios, we

changed the travel mode of the people that work and

live near a metro station from bus or car to subway.

The description of the new scenarios is the following:

Current Scenario: we used the current data of the

OD matrix considering the travel mode, the start

time of the travel, the origin, and the destination of

each person. We used the current subway network

of the city.

Replace Buses: we added the new line in the sub-

way network of the city and changed the travel

mode of the population that used buses in the OD

matrix to subway if the person lives and works

less than 500 meters from a subway station.

Replace Cars: we added the new line in the sub-

way network of the city and changed the mode of

transportation of all trips that use cars to subway

if the person lives and works less than 500 meters

from a subway station.

Replace Both: we combined the scenario 2 and 3,

changing all the trips to subway if the person lives

and works less than 500 meters from a subway

station.

In all scenarios, we did not change the mode of

the trips with less than 2 kilometers because it is un-

likely that a person that has the origin and destination

very close will use the subway. Table 1 presents the

travel mode of all the simulated population. All the

scenarios have the same population. Hence the to-

tal population is the same in all simulations, only the

travel mode of a subset of the people changes from

one scenario to the other.

Table 1 shows that, with the new line, 9877 peo-

ple can change their transportation mode, 20% of the

total. Of this people, 5004 are car users, indicating a

potential to improve the trafﬁc in the region and de-

crease the pollution emission. The other 4873 people

are bus users, which can potentially reﬂect in a reduc-

tion of travel time and improvement of quality of life.

From Scenario 1 to Scenario 3 the number of bus

lines required to serve the simulated population also

reduced from 54 lines to 45, what can indicate that

Table 1: Modal-split of the simulated population.

Current Scenario

Replace Buses

Replace Cars

Replace Both

Car 8,867 8,867 3,863 3,863

Walk 21,373 21,373 21,373 21,373

Subway 168 5,041 5,172 10,045

Bus 17,785 12,912 17,785 12,912

Total 48,193 48,193 48,193 48,193

some lines can have their route changed or eliminated.

We created this four scenarios to compare the impact

of changing each travel mode and then the impact of

changing all the potential population.

3.3 Simulation Research Questions

With the data collected from the simulation of the four

scenarios, we can compare the different situations and

compute several metrics to evaluate the impacts of

changes in the modal split of the population. The

research questions explored in the study are related

to the impact of the new subway stations and sub-

way line for the analyzed population with regard to

travel time and travel cost and the impact in the city

measured in terms of carbon footprint. The research

questions are:

RQ1: “What is the impact of a new subway line in

the travel time of its potential users?”

RQ2: “How the new line will impact the cost of the

transportation to the population?”

RQ3: “If the potential users change its transportation

mode, will it have an environmental impact?”

Section 5 will present the answers to these re-

search questions based on the data from the simula-

tion of the scenarios.

4 SIMULATION

In this section, we present our agent-based, smart city

simulator, the execution of the scenarios presented in

the previous section and a brief description of the re-

sults of the simulation.

SMARTGREENS 2018 - 7th International Conference on Smart Cities and Green ICT Systems

146

4.1 Large-Scale Smart City Simulation

We developed InterSCSimulator, an open-source,

scalable, mesoscopic, agent-based Smart City simu-

lator capable of simulating millions of actors faster

than real-time (Santana et al., 2017b). In previous

experiments, we could simulate more than four mil-

lion agents in a 24 hours simulation in approximately

3 hours. The current version of the simulator is capa-

ble of running single and multi-modal trips using cars,

buses, subway, and pedestrian as the travel mode.

The simulator expects, as input, four XML ﬁles

describing the simulated scenario. We create these

ﬁles based on the databases described in Section 3.1,

and we describe them in the following:

subway.xml deﬁnes the city subway system. In this

ﬁle, each station is a vertex, and the connection of

two stations are the links of the graph.

map.xml describes the city road network graph. In

this ﬁle, each road stretch is a link, and the corners

are the vertices of a graph. We used the map from

Open Street Maps to generate this ﬁle.

buses.xml lists all the bus lines of the city. Each line

must have a code, the time in which the service

starts and ends, all its bus stops, and the inter-bus

interval of the line.

trips.xml contains all the trips that must be simu-

lated. The trips must have the time that it will

start, the transportation mode, the origin, and the

destination.

Listing 1 presents, as an example, a ﬁle with two

trips that will be simulated, each one with a different

transportation mode. In the ﬁle, there are two trips; in

the ﬁrst one, the travel mode is car, in the second, bus.

When the travel mode is subway or bus, the person

must walk from the origin to a bus stop or a subway

station and then walk from another bus or subway sta-

tion to the ﬁnal destination.

Listing 1: XML ﬁle with examples of trips.

< t r i p o r i g i n = ” 4197294783 ”

d e s t i n a t i o n =” 28637975 ”

s t a r t =” 27601 ” mode=” c a r ” />

< t r i p o r i g i n = ” 2197654483 ”

d e s t i n a t i o n =” 284356975 ”

s t a r t =” 27651 ” mode=” walk ” />

< t r i p o r i g i n = ” 1740921857 ”

d e s t i n a t i o n =” 1107272621 ”

l i n e =” 8020−10−0 ” mode=” bus ” />

< t r i p o r i g i n = ” 1107272621 ”

d e s t i n a t i o n =” 304693626 ”

mode=” walk ” />

< / m u l t i t r i p>

In Listing 1, origin and destination are nodes in the

city graph, start is the time that the agent must start its

travel, mode is the travel mode of the agent, and line

is the bus line that the agent will use. The simulator

is generic and can work with the infrastructure of any

city in the world, just requiring the generation of all

input ﬁles of the simulator.

4.2 Scenario Execution

To analyze the impact of changes in the city infras-

tructure, we executed the four scenarios described in

Section 3. We also created trafﬁc in the city graph

based on the complete OD matrix of the city; we sim-

ulated 1.2 million cars, which lead to realistic trafﬁc

conditions and, thus, realistic travel times for the car

trips. We executed all the scenarios in a 24-core ma-

chine with 54 GB of memory in the Google Comput-

ing Engine

, and all of them took less than one hour

to execute an entire day in the city.

The agent execution depends on its mode of trans-

portation. We describe the types of execution of each

mode in the following:

Car: we start by computing the shortest path be-

tween the origin and destination and then make

the agent traverse the city graph visiting all links

of the calculated path. A density function is used

to calculate the speed of the car in each link (Song

et al., 2017) based on the number of vehicles (cars

and buses) in the link in each instant.

Bus: traveling by bus, the agent walks to a bus stop

and can take one or more buses until a bus stop

close to its destination.

Subway: the agent walks to the nearest subway sta-

tion; the subway trip duration is computed with

the best path algorithm considering the travel time

between the stations. After leaving the subway,

the agent proceeds its journey by bus or walking.

Walk: the agent uses the same idea of the car trips,

the difference is that the speed is approximately 4

Km/h.

The agents can also make multimodal trips, com-

bining walking, bus, and subway. Using a car and an-

other modal in a single trip is indeed possible. How-

ever, in the OD data that we have, there were no such

cases. In addition to computing speciﬁc metrics de-

ﬁned by the user, the simulator can also present a

graphical visualization of the simulation with the help

of the MATSim OTFVis tool

. Figure 2 presents the

Google Computing Engine – http://cloud.google.com/

compute/

OTFVis – http://goo.gl/U8a87g

Analyzing Urban Mobility Carbon Footprint with Large-scale, Agent-based Simulation

147

execution of the simulation in the city graph. The

green points are agents moving across the city.

Figure 2: Simulation visualization.

4.3 Results

The simulator saves an XML output ﬁle with all the

agent actions. There are four possible actions: 1)

When the agent starts its trip, 2) when the agent leaves

a link, 3) when the agent enters a link, and 4) when

the agent arrives to its ﬁnal destination. All actions

have the time, the location where it occurred, and the

mode of transportation that the agent used. Listing 2

presents a sample output of the simulator with arrivals

actions.

Listing 2: XML ﬁle with the output of the simulator.

< e v e n t t i m e =” 22400 ” t y p e =” a r r i v a l ”

p e r s o n = ” 120 ” legMode=” walk ”

t r i p t i m e =” 420 ” d i s t =” 450 ”

c o s t =” 0 ” />

< e v e n t t i m e =” 27860 ” t y p e =” a r r i v a l ”

p e r s o n = ” 122 ” legMode=” c a r ”

t r i p t i m e =” 1820 ” d i s t =” 4210 ”

c o s t =” 6 . 8 ” />

< e v e n t t i m e =” 29504 ” t y p e =” a r r i v a l ”

p e r s o n = ” 123 ” legMode=” bus ”

t r i p t i m e =” 1828 ” d i s t =” 3500 ”

c o s t =” 3 . 8 ” />

When the agent arrives to its destination, the sim-

ulator computes the attributes of the trip such as total

time, distance, and cost. This output can be used to

generate the visualization of the simulation presented

in Figure 2 and to make analyzes in a statistical tool

such as R.

5 ANALYSIS AND DISCUSSION

We analyzed the change impact in three different per-

spectives: ﬁnancial cost for the users, travel duration,

and the carbon footprint. The objective of this anal-

ysis is to answer the research questions presented in

Section 3.3.

To answer RQ 1, “What is the impact of a new

subway line in the travel time of its potential users?”,

we calculated the travel time for each person in all

scenarios presented in Section 3. The time users

spend on a trip directly impacts the users’ perception

of the quality of the transportation system and qual-

ity of life. As Figure 3 presents, the travel time of

most of the population decreased, mainly for the bus

users (depicted in orange in Figure 3). For 4500 peo-

ple, i.e., 90% of the people who changed from bus to

subway, their travel time reduced signiﬁcantly. For in-

stance, 30.84% of buses users that started to use sub-

way had an improvement up to 30 minutes. Moreover,

7.14% of these buses users improved their travel time

for more than 2 hours.

From the population that used cars in the original

scenario, approximately 1,500 had their travel times

decreased and 4,000 had their travel times increased.

The people that had their travel time increased by

more than 30 minutes (around 2,000 people) are un-

likely to change their travel mode. However, since the

cost reduction can be very substantial (mainly when

taking into consideration parking fees), even some of

them might prefer the subway.

Figure 3: Travel time improvement.

To answer RQ 2, “How the new line will impact

the cost of the transportation to the population?”, We

calculated the trip ﬁnancial cost for each person in all

scenarios presented in Section 3 since users usually

want the cheapest option with a good travel condition.

In our analysis, we took into account the car trip costs,

as well as bus and subway fares. Using the OECD

Purchasing power parities (PPP), the cost of the bus

and subway fares was 1.9 USD and the car cost was

0.35 USD per kilometer.

As Figure 4 shows, the creation of this new sub-

way line would reduce the cost for more than 2,500

SMARTGREENS 2018 - 7th International Conference on Smart Cities and Green ICT Systems

148

people in the neighborhood that changed the mode

of transportation from car to the subway. This is es-

pecially important because we are simulating a low-

income community. The ﬁnancial costs of trips made

by bus or by subway did not change because the price

of the two systems is the same in the analyzed city.

Figure 4: Financial cost beneﬁt.

To answer RQ 3, “If the potential users change

its transportation mode, will it have an environmental

impact?” we calculated the carbon footprint of each

person in all scenarios presented in Section 3. The

common baseline is that the carbon footprint stands

for a certain amount of gaseous emissions that are rel-

evant to climate change and associated with human

production or consumption activities (Wiedmann and

Minx, 2007). By analyzing Figure 5, we can notice

that in the “Replace Both” scenario the carbon foot-

print would be reduced from 30,992 tons of CO

per

year to 22,790, i.e., 26%. The reason that the change

to the subway decreases the carbon footprint is that

cars and buses emit over three times more CO

to the

atmosphere than the subway.

In short, if the bus users start to use this new sub-

way line, they would reduce their travel time and car-

bon footprint, while still paying the same fare. How-

ever, the car users would reduce their ﬁnancial cost

and carbon footprint but increase their travel time.

Thus, with this new subway line, some of the bus

users would deﬁnitely migrate to the subway because

of the reduction in travel time. However, most car

users would need to choose between a shorter travel

time or a reduced ﬁnancial cost. Indeed, decision

makers should promote the usage of this new subway

line, since it would decrease both the carbon footprint

and the economic cost for the population as well as

improve trafﬁc in the region.

Figure 5: Accumulated Carbon Footprint.

6 CONCLUSIONS AND FUTURE

WORK

The growth of the cities around the world requires bet-

ter planning and informed decisions to improve the

citizen quality of life and optimize cities’ infrastruc-

ture. To achieve this, it is mandatory the use of tools,

such as simulators, that facilitate the analysis and

comparison of different alternative scenarios, leading

to more effective public policies and governmental ac-

tions.

With the open source simulator we are now mak-

ing available, a researcher or urban planner is ca-

pable of simulating millions of agents in an entire

metropolitan area with multiple modal-splits. This

simulator can now be applied to various ﬁelds such

as mobility planning, comparison of potential inter-

ventions in the trafﬁc, and measuring the impact of

changes in the city infrastructure.

This paper showed that with a large-scale, smart

city simulator, it is possible to analyze the impact of

changes in the infrastructure of a large metropolis,

with over 10 million people. We compared param-

eters, such as travel distance, time, and carbon foot-

print from the population of a slum using the current

mobility infrastructure and possible scenarios with

the new lines planned for the next years. The compar-

ison of the simulation results showed many potential

beneﬁts from these modiﬁcations in the city infras-

tructure.

As future work, we plan to implement new trans-

port modes in the simulator such as bicycle, car shar-

ing, and taxis. We also intend to include new Smart

City scenarios such as smart parking and garbage col-

lection. Regarding the analyzes, we will analyze other

Analyzing Urban Mobility Carbon Footprint with Large-scale, Agent-based Simulation

149

areas in the city that also have planned very signiﬁcant

modiﬁcations in their infrastructure and compare the

beneﬁts of these adjustments.

To assure reproducibility of our results, the

experimental package, including all source code,

datasets, and scripts used in this paper is available at

http://interscity.org/software/interscsimulator.

ACKNOWLEDGEMENTS

This work is part of the INCT of the Future Inter-

net for Smart Cities (CNPq 465446/2014-0, CAPES

88887.136422/2017-00 and FAPESP 2014/50937-1)

and CNPq grant 420907/2016-5.

The authors also acknowledge the Coordination

for the Improvement of Higher Education Personnel

(CAPES) for scholarships ﬁnancial support.

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