Towards an Affordable GIS for Analysing Public Transport Mobility

Data: A Preliminary File Naming Convention for

Avoiding Duplication of Efforts

Benito Zaragoz

1 a

, Aaron Guti

errez

1 b

and Sergio Trilles

2 c

Departament de Geograﬁa, Universitat Rovira i Virgili, C/Joanot Martorell, Vilaseca, Spain

Institute of New Imaging Technologies, Universitat Jaume I, Av. Vicente Sos Baynat s/n, Castell

on de la Plana, Spain

Keywords:

Smart Card Data, Public Transportation, Domain-speciﬁc Language, File Naming Convention, Medium-sized

Data.

Abstract:

Automated fare collection systems for public transport generate a large volume of information on the mobil-

ity of people in urban environments. New technologies associated with Big Data can facilitate the analysis

of these data. However, the application of these technologies can be expensive and resource-demanding, es-

pecially in medium and small cities. This paper presents the case of the metropolitan transport authority of

Tarragona, for which an affordable and extensible analysis system has been developed, based on relational

databases and custom scripts. Among the technical problems that have had to be overcome, one of the ﬁrst has

been the unambiguous deﬁnition of the numerous queries required by mobility experts. For different reasons,

mobility researchers request aggregate data queries from smart transport cards logs (e.g. providing a descrip-

tive statement) and expect manageable tables to be analysed in a spreadsheet. To standardise the deﬁnition

of queries, a domain-speciﬁc language as a ﬁle naming convention has been proposed with which database

managers and mobility experts can communicate efﬁciently, avoiding confusion, duplication of efforts and

other problems detected. The ﬁle naming convention has been applied as an early version within the deﬁned

use case to verify the viability of this idea.

1 INTRODUCTION

Data generated from Automated Fare Collection Sys-

tems (AFCS) using smart transport cards is charac-

terised by its dynamism, as each time a user gets on

a public transport vehicle, the system collects many

data from its validation. As a result, millions of ob-

servations have been collected that provide valuable

information for understanding passenger behaviours

and can also help improve service quality (Kurauchi

and Schm

ocker, 2017). Since all transactions are

gathered, there is great ﬂexibility for studying any

temporal and geographical extent (Morency et al.,

2007). The analysis of such data is very common in

the scientiﬁc literature (Pelletier et al., 2011; Bagchi

and White, 2005). For example, smart travel card data

has been used to identify different proﬁles of pub-

lic transport users (Ma et al., 2013), to reconstruct

https://orcid.org/0000-0003-2501-484X

https://orcid.org/0000-0003-0557-6319

https://orcid.org/0000-0002-9304-0719

source-destination matrices (Alsger et al., 2015) or to

analyse tourist mobility patterns (Lu et al., 2019).

Smart cards can gather all transactions, so the size

of data might become so large that after a short pe-

riod of time, it is difﬁcult to handle. From this point

of view, smart card data can be seen as one sort of Big

Data, and it is considered that, only by using speciﬁc

technology, we can consider nearly the whole popu-

lation data for analysing passengers behaviour. While

in traditional data analysis, data sampling is required

to select a small portion of data, data mining meth-

ods – such as clustering analysis – are often adopted

to understand the global dataset characteristics. How-

ever, these procedures are far more complicated when

data size increases. Generally, to apply big data anal-

ysis, some key points need to be considered, but the

most crucial one is to decide how to store that data.

Various types of databases could be employed to save

that kind of data, including Hadoop Distributed File

System (HDFS), Hbase, Apache Cassandra and Re-

dis, just to name a few.

During the last years, many research works have

302

Zaragozí, B., Gutiérrez, A. and Trilles, S.

Towards an Affordable GIS for Analysing Public Transport Mobility Data: A Preliminar y File Naming Convention for Avoiding Duplication of Efforts.

DOI: 10.5220/0009766303020309

In Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2020), pages 302-309

ISBN: 978-989-758-425-1

applied the above mentioned technologies to analyse

different amounts of data generated by smart cards

systems. As an example, Apache Spark and HDFS

were used to analyse 160 million of records from

20 months of transactions, between June 2014 and

January 2016, generated by the Jakarta’s Bus Rapid

Transit in Indonesia (Prakasa et al., 2017). The

same big data technologies were used in combina-

tion with Apache Kafka to support real-time analy-

sis (Barth and Galante, 2016), building a so called

Data Mining Framework for Bus Service Manage-

ment (DMBSM). This system was tested using a re-

duced dataset of 3,186 records of smart cards in Shen-

zhen city (China), between October 22 and October

23, 2013. Another approach used SQL to extract pas-

senger origin information and SPSS and Microsoft

Excel to highlight connections among different trans-

portation data sets, including the data generated by

the smart cards (Istanbulkart) of the Bus Rapid Tran-

sit system (BRT) in Istanbul (Turkey) (Gokasar and

Simsek, 2014). The same authors utilised a MySQL

open-source database, MS Excel CSV ﬁle format and

Rstudio software to study passenger origin informa-

tion from the Istanbulkart data (Gokasar et al., 2015).

This time the authors used a one-day dataset with

800,000 journey records. Another study analysed six

months of smart card data from Brisbane — the third

largest Australian city (Tao et al., 2014). The authors

did not indicate the use of any speciﬁc technology to

perform the analysis. In 2015, another study of the

city of Montevideo (Uruguay) analysed smart card

data logs comprising nearly 200 GB of data (Fabbiani

et al., 2016). In this case, the authors used the dispy

framework for creating and distributing parallel tasks

among several computer nodes and QGIS software to

manage geographic information and perform spatial

analysis.

As can be seen, there are experiences in applying

different robust systems in large and medium-sized

cities, but there are also experiences in small cities

or metropolitan areas. Obviously, larger cities have

more resources to manage and analyse this data, so it

is more viable to take advantage of the logging sys-

tem to its full potential. However, in smaller cities,

there are problems in adopting these new technolo-

gies. For these cases, collaborations between public

transport authorities and research institutions have be-

come a common strategy for analysing this speciﬁc

type of data (Wu et al., 2015). This research work

is an example of such type of collaboration, as it is

the result of a research project carried out jointly by a

research group of the Rovira i Virgili University, the

Territorial Mobility Authority of Camp de Tarragona

(ATMCdT), and the ATM in the Lleida area. In this

research project, experts from different areas collabo-

rate, including experts in mobility and transport stud-

ies, database managers, GIS analysts and technicians

from the ATMCdT. As we explain later, good com-

munication is essential in such a multidisciplinary re-

search environment. In this context, the key objec-

tives of this research are summarised are:

1. To study the main characteristics and the potential

of the log data gathered by the ATMCdT.

2. To analyse the requirements for managing these

data and the workﬂows between the different ac-

tors that need to cooperate to exploit these data.

3. To propose a Domain-Speciﬁc Language (DSL)

for communicating ATMCdT technicians,

database managers and mobility researchers that

enhances code re-utilisation, saving time and

effort for understanding the data. This DSL will

be used as a ﬁle naming convention and can

describe database queries in the context of public

transport mobility.

4. To test and evaluate the proposed DSL in the con-

text of a research project.

The remainder of this paper is organised as follows.

The next section (Section 2 describes our case study,

the main characteristics of the ATMCdT log data and

the issues affecting its analysis. Section 3 proposes

a deﬁnition of a DSL. This approach enables better

communication between different actors, minimising

the need for investing in new tools and avoiding the

duplication of efforts. Section 4 draws conclusions

and outlines further research directions for this afford-

able system.

2 CASE STUDY

The smart card data collected by the ATMCdT is of

great value, as previously demonstrated in studies that

analyzed the effectiveness and spatial coverage of the

public transport system (Dom

enech and Guti

errez,

2017) or other studies that focused on the use of

public transport by tourists (Guti

errez and Miravet,

2016a; Guti

errez and Miravet, 2016b; Dom

enech

et al., 2020). However, although these previous stud-

ies faced data management problems similar to those

we will explain in this section, these issues tend to go

unnoticed despite slowing down or making data anal-

ysis difﬁcult.

2.1 Data Preprocessing

In this work, the Territorial Mobility Authority of

Camp de Tarragona (ATMCdT), which is shown in

Towards an Affordable GIS for Analysing Public Transport Mobility Data: A Preliminary File Naming Convention for Avoiding

Duplication of Efforts

303

Figure 1, and ATM Lleida facilitate the log databases

generated by their fare collection systems. This joint

system is known as Fare Integration Management

System (SGIT, according to its acronym in Catalan).

This information system collects data for accounting

purposes, so it should be understood that mobility

studies were not contemplated in its initial design.

The data collected includes –but it is not limited to–

the exact day and time of travel, the stop where the

passenger boarded, the company and the carrier that

operates the transport, the municipality and the type

of fare used in each transaction. Sometimes the des-

tination stop can also be registered. The main advan-

tage of the resulting databases is that the information

is dynamic when it acquires a temporal dimension,

and it allows to consult any registered time period.

By contrast, these databases do not store data on the

socio-economic proﬁle of travellers, or these data can

not be accessed due to legal restrictions.

Figure 1: Location of the the Territorial Mobility Authority

of Camp de Tarragona (ATMCdT) service area.

In the analysis of automated fare collection systems,

one of the ﬁrst steps after data collection is the data

cleaning phase. There are different reasons for clean-

ing the dataset and improving the quality of data col-

lection (Chandesris and Nazem, 2018). In this case,

the log ﬁles analysed in this study were provided on

a portable hard drive by ATMCdT. These records in-

clude all activity in 2018 for 135,365 different smart

cards – 133,079 have been used more than once –

and all single-trip tickets sold in the study area. This

dataset weighs 6.1 GB in plain ASCII text format.

During this period, the system collected 7,393,654

smart card travel transactions (rows) with 60 different

attributes (columns) and 14,006,212 single-trip trans-

actions with 22 attributes. This dataset did not include

other operations that are also systematically recorded,

such as card sales, recharges or cancellations, among

others. The ﬁles generated by the ATMCdT platform

are not standardised in terms of the type of informa-

tion. We have reduced and cleaned all no relevant

data for this study, performing a pre-processing step

to create a relational database (SQLlite) and facilitate

the ﬁrst steps of analysis. This workﬂow, based on

SQL databases, is very common in previous studies

on transport smart cards, and it is very convenient to

analyse datasets of a medium to big size (Li et al.,

2018).

ATMCdT records have a very basic structure; they

are sorted by date and time, the code of the smart card

that performs the transaction, the type of transaction

performed and other similar parameters. Given the

research objectives, we excluded urban bus transac-

tions from Tarragona and Reus and only ﬁltered in-

terurban transactions (5,414,028 transactions), select-

ing 9 out of 60 available attributes. Thanks to this

selection and standardization process, we have signif-

icantly reduced the size of the database – from 9.1 GB

to 1.5 GB, including indexes and support tables. More

speciﬁcally, we discarded single-value columns, du-

plicated columns – due to legacy system changes

– and derived columns (that is, aggregates that can

be calculated from other attributes). The resulting

database divides the records into different tables fol-

lowing a simple relational structure (see Figure 2),

with the main transactions table linked to other ta-

bles built for normalisation purposes (agencies, fares,

stops, municipalities, and routes). For clarity, the ta-

bles and columns were named following the General

Trafﬁc Feed Speciﬁcation (GTFS), which is a well-

known standard format for publishing public tran-

sit schedules. In addition to the ATMCdT data, the

database was enriched with some layers of geograph-

ical information: (1) the municipalities, roads and

shoreline were downloaded from the Cartographic

and Geological Institute of Catalonia (www.icgc.cat)

and adapted to the needs of the project, (2) the stops

and routes were digitised manually, (3) ofﬁcial pop-

ulation data (https://www.idescat.cat/) and (4) ATM-

CdT zones.

2.2 Problem Statement

The design of the database is quite generic and is

given by the characteristics of the raw data (see Fig-

ure 2). However, after the preprocessing step, there

is a context that needs from a multidisciplinary ap-

proach. The research group is composed by two dif-

ferent types of proﬁles described as:

1. Mobility experts and researchers. The main ob-

jective of this proﬁle is to interrogate the data

management system (SQLite) by performing dif-

ferent types of queries, which include temporal

and spatial dimensions. This proﬁle has an inter-

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

304

Figure 2: Entity relationship database model for storing ATMCdT Smart Card log data. We distinguish between regular

attribute tables (T) and spatial tables (S).

est in the query results in order to perform fur-

ther mobility analysis and participate in decision-

making processes.

2. Spatial database manager. This proﬁle is in charge

of interacting with the data management system,

coding SQL queries which satisfy the requests

made by a mobility expert and execute them to

get the results in a convenient data format (e.g.

Spreadsheets).

This team structure presents a communication prob-

lem, between the mobility expert and the database

administrator, which we discovered during the ﬁrst

phases of work. Both roles have expertise in its

own domain but lack cross-domain knowledge to in-

tuitively understand the objectives or the difﬁculties

of a particular query. On the one hand, the mobility

expert does not have the experience of working with

a relational database, so when requesting a new query

from the database manager, it may be difﬁcult to ex-

press it in the most direct way to deﬁne an accurate

SQL query. On the other hand, the database man-

ager may have difﬁculties in understanding the spe-

ciﬁc purpose of a query. In order to obtain the correct

result, a certain iterative process between both actors

is usually necessary. This process is shown in Figure

3. On the left hand side, Figure 3 A) is a sequence dia-

gram that formally describes the process. The mobil-

ity researcher must describe the query to the database

manager actor as many times as necessary until ob-

taining the correct result. This process can be very

tedious, repetitive or redundant, especially when the

number of different queries increases.

In addition to the time spent in understanding the

project needs, both actors (database manager and mo-

bility researcher) can lose time in executing queries

that are not interesting for the project. As in any

analysis, experts in the ﬁeld need to generate a lot

of queries on databases, and not always is expected

to get a beneﬁcial outcome for decision making.

Sometimes these queries do not vary substantially, so

from the database manager’s point of view, previous

queries can be easily re-used to create new ones if

they are documented correctly.

3 PROPOSED SOLUTION

According to the issues described in the previous sec-

tion, the main bottleneck in the process appears when

the quality of deﬁnition of the queries is poor or the

target result is not correctly visualised. For this kind

of problems, it would be possible to apply a techno-

logically based solution. However, due to the limita-

tions described above, developing or introducing new

tools is not an option, but an undesired dependence.

As a solution to this problem, we propose the deﬁ-

nition of a Domain-Speciﬁc Language (DSL) adapted

to the needs of this type of applications. From here

we will refer to this speciﬁcation as the Mobility File

Naming Convention (MobilityFNC). This approach

which will be used as a convention to describe and

identify the queries properly. Each query will be

named using this convention and both ﬁles — the

SQL query deﬁned by the database experts and the re-

sults ﬁle — will be inseparable. This solution allows

us to build a catalogue of already executed queries.

Thus, before developing a new query, it will be pos-

sible to verify if this query has been previously coded

or if there is a similar one to use as a model.

Figure 3.B presents the new workﬂow enhanced

by MobilityFNC. As previously mentioned, the main

advantage of MobilityFNC is to allow the possibility

of building a query repository, with SQL scripts and

their derived results (e.g. spreadsheets). Having the

Towards an Affordable GIS for Analysing Public Transport Mobility Data: A Preliminary File Naming Convention for Avoiding

Duplication of Efforts

305

Figure 3: A) Sequence diagram showing the workﬂow between a mobility researcher and database manager in the context of

a public mobility project; B) Sequence diagram showing the workﬂow between a mobility researcher and database manager

using the MobilityFNC aproach.

access to that repository granted, there are three pos-

sible situations:

1. Self-sufﬁciency. If the query description already

exists in the catalogue, mobility researchers could

directly access it in the form of a spreadsheet (e.g.

another researcher asked for the same query).

2. Feedback. If the query was not previously exe-

cuted but there was any other similar query, in that

case it would be easier to the database expert to

adapt it (e.g. the new query refers to a different

date, but apart from that it is identical).

3. Finally, If the query does not exist, the database

manager will use the query description encoded

with MobilityFNC and generate the SQL query,

execute and incorporate it into the catalogue.

This interactive process between mobility researcher

and database manager should not be tedious, repeti-

tive or redundant, since the database manager would

write the SQL expression following the proposed

DSL, which should be always more concrete than a

semi-structured text. MobilityFNC should be easily

understood by researchers with no advanced experi-

ence in SQL. It is motivated to improve understanding

between a mobility expert and a database manager.

This speciﬁc language deﬁnes an structured and un-

derstandable way for mobility experts with which to

consult information from a database without the need

of using an advanced query language.

In the literature, there are many DSLs for differ-

ent application domains. Among the best known we

can highlight HTML, Unix shell scripts or GraphViz,

among others. The main advantage of DSL over Gen-

eral Purpose Languages (GPL) is that DSLs are more

attractive due to the proximity to the context by adopt-

ing more sensible programming features and system-

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

306

atic reuse (Van Deursen et al., 2000). However, de-

signing and developing a DSL is not always mean-

ingful, for example, SQL can be considered as a DSL

that focuses on the domain of the database, but it is

still too general and more complex than some script-

ing languages (Hudak, 1997).

MobilityFNC is used to name the ﬁles of the SQL

queries with which it is translated. In this way, we can

quickly identify if the SQL query has been previously

encoded or may be helpful to encode a new one (e.g.

if there are more queries involving the same informa-

tion). Although it could be extended to different types

of public mobility, such as car, train or plane, due to

the selected use case, we base MobilityFNC on pub-

lic bus mobility. Arguably similar to SQL, we will

use MobilityFNC as a way for describing the shape

and main elements of a table resulting from a query.

Thus, we intend to maximise its compatibility and im-

prove this proposal so it could be translated into valid

SQL, at least for a previously known database model.

3.1 Lexicon

As already explained, a MobilityFNC expression will

be used to name a ﬁle that stores a SQL query. Of

course, depending on the operating system, certain

characters are not allowed to be used for ﬁle nam-

ing. Following these requirements, MobilityFNC al-

lows any byte except NULL, \, /, :, %, ?, ∗, ”, <, >, |.

Another limitation is not to exceed 255 characters per

query description, so it is important to avoid unneces-

sary redundancies. Based on this character restriction,

a list of operators has been deﬁned for MobilityFNC:

• Separate main blocks (source, ﬁlter, dimension

and Operations) with “+”

• Add a new element at a same level with “−”

• Start a new level and add an element with “ ”

• Separate rows and columns with “∼”

• Deﬁne a range in canonical form with “[ ]”

• Set a function or method with “{ }”

• Determine an array of variables with “[ , ]”

In addition to the operators, there is also a selection

of restricted words to encode queries. We distinguish

6 categories with selection of restricted words to be

used as vocabulary to encode queries.:

• Aggregation. E.g. count, totals, subtotals, top.N,

htotal, among others.

• Attributes. SQL table attributes shown in the ER

model (Figure 2).

• Boolean. E.g. pop.between, pop.over, pop.less,

pop.equal or applied to other associated attributes.

• Ranges. We use the -ly termination to refer to a

known ranging (eg. monthly, yearly, etc), sum-

merly or nonsummerly.

• Sources. ATM smart cards and TP (single tickets).

• Spatial. Coastal and ATMCdT zoning, but it could

be exended with more spatial layers.

3.2 Syntax and Semantics

After deﬁning all available pieces in the language, we

establish the MobilityFNC grammar (Grammar 1) fol-

lowing the extended Backus-Naur (Wirth, 1996).

Grammar 1: Extended Backus-Naur grammar of Mobili-

tyFNC.

Filenane ::= Source list,” + ”, [(Filters, ” + ”)], Dimension,[(” +

”,Operations list)], ”.sql”

Source list ::= ”log[”,Sources,”]”

Sources ::= source|(source, ”,”, source)

Filters ::= Filter|(Filter,” − ”, Filter)

Filter ::= (”date[”,TempCardTypes, ”]”)|(”cards[”, TempCardTypes,”]”)|

(”municipality[”,DemSpatial, ”]”)

TempCardTypes ::= ranges|(ranges, ”,”, ranges)

DemSpatial ::= DemSpatialType|(DemSpatialType,”, ”,DemS patialType)

DemSpatialType ::= (spatial)|(boolean)

Dimension ::= Rows,” ”,Colums

Rows ::= (Row,” − ”, Row)|Row

Row ::= (ElementType, ”[ ”, atributeFeature,”]”)|ElementType|Filter

Columns ::= (Column,” − ”,Column)|Column

Column ::= (ElementType,”[ ”, atributeFeature,”]”)|ElementType|Filter

ElementType ::= attribute|aggregation

Operations list ::= attribute,”{”,Operations, ”}”

Operations ::= aggregation|(aggregation, ”,”, aggregation)

An encoded ﬁlename using MobilityFNC has the

structure shown in two examples deﬁned in Figure

4. The query description following this convention

is divided into four different parts separated by sum

signs. The ﬁrst one deﬁnes the source(s) of interest

to query. In the current project, only two different

sources are available (ATM cards and TP single-ride

tickets). The second block comprises the main ﬁlters

to apply. It supports temporal ﬁlters, select types of

smart cards and some spatial ﬁlters (depending on the

spatial layers available). In the third block, the dimen-

sions of the resulting table will be indicated, dividing

into rows and columns. It will contain attributes of

the table or aggregates. Considering the limited num-

ber of characters of a ﬁlename, this third block will

support to apply ﬁlters on the attributes to avoid any

redundany in the ﬁlters block. Finally, the last block

Towards an Affordable GIS for Analysing Public Transport Mobility Data: A Preliminary File Naming Convention for Avoiding

Duplication of Efforts

307

Figure 4: Examples of two ﬁlenames of queries encoded with MobilityFNC. .

will keep the aggregation operations to be applied to

the body of the resulting table.

3.3 MobilityFNC Examples

In our project we were able to deﬁne 56 different

query names using MobilityFNC, which helped us to

better deﬁne our proposal. In this subsection, we il-

lustrate two different examples of queries deﬁnitions,

their structures and the intended results (Figure 4):

• Query A. The ﬁrst example of a description query

(Figure 4, Query A) shows a more straightforward

query example where lists the number of ATM-

CdT cards that were only used in the summer of

2018, grouped by fare. This query is useful to

study those mobilities that only take place during

the summer season. Inn some cases it could be a

proxy to identify tourist mobilities.

• Query B. Another more complex example is

shown in second place. It extracts the number

of TP transactions (single-ride tickets) that were

used in 2018, distinguishing if they were done in

summer or the rest of the year, in the main mu-

nicipalities of the study area. The use of Boolean

ﬁlters in the dimension block is to avoid redun-

dancy as municipality should be duplicated in the

ﬁlters block.

4 CONCLUSIONS AND FUTURE

WORK

We have proposed a solution to a communication is-

sue that we detected in our multidisciplinary research

group. The solution consists in a ﬁle naming con-

vention that attempts to optimise the communication

workﬂow between researchers in a project analysing

public transportation smart card data. Our proposal,

MobilityFNC, is currently in an early stage, it already

supports mobility concepts and can apply temporal

and geospatial ﬁlters. MobitityFNC is designed for

mobility experts to be useful in the process of gener-

ating SQL queries by database administrators or de-

velopers. The SQL query is stored as content in the

same ﬁle. This feature allows database managers to

create SQL queries unambiguously, as well as to as-

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

308

sist in query cataloguing and reuse.

As future work, we are aware of the limitations

of the deﬁned convention in its current state, so that

our ﬁrst intention is to extend the DSL deﬁnition to

generalise it within the domain of mobility, and to be

applied in more use cases. Finally, the last next step

is the functionality to automatically generate SQL

queries from the MobilityFNC format.

ACKNOWLEDGEMENTS

Research funded by the Spanish Ministerio de

Ciencia e Innovaci

on [grant number CSO2017-

82156-R], the AEI/FEDER,UE, the Departament

d’Innovaci

o, Universitats i Empresa, Generalitat de

Catalunya [grant number 2017SGR22] and the Es-

cola d’Administraci

o Publica de Catalunya, Gen-

eralitat de Catalunya [grant number 2018 EAPC

00002]. Sergio Trilles has been funded by the post-

doctoral programme PINV2018-Universitat Jaume I

(POSDOC-B/2018/12) and research stays programme

PINV2019-Universitat Jaume I (E-2019-31).

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