Methodology for Creating a Geographic Information System for Transport Infrastructure Facilities
Corresponding email: talipova_lv@spbstu.ru
Published at : 29 Dec 2023
Volume : IJtech
Vol 14, No 8 (2023)
DOI : https://doi.org/10.14716/ijtech.v14i8.6849
Talipova, L., Morozova, E., Pestova, D., Skhvediani, A., 2023. Methodology for Creating a Geographic Information System for Transport Infrastructure Facilities. International Journal of Technology. Volume 14(8), pp. 1831-1840
| Liliia Talipova | Department of Industrial, Civil and Road Construction, Faculty of Civil Engineering, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, 195251, Russia |
| Evangelina Morozova | Department of Industrial, Civil and Road Construction, Faculty of Civil Engineering, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, 195251, Russia |
| Daria Pestova | Department of Industrial, Civil and Road Construction, Faculty of Civil Engineering, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, 195251, Russia |
| Angi Skhvediani | Department of Industrial, Civil and Road Construction, Faculty of Civil Engineering, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, 195251, Russia |
This
study examines the methodology for creating a geographic information system of
transport infrastructure objects - from obtaining initial data to storing the
data itself. The article defines the classification of objects in the
transportation system, the procedure for collecting and processing initial
data, as well as the sequence of data processing for transmission to the GIS
environment. A study was conducted on the dependence of response time on the
current number of elements in a database table with indexed and un-indexed
data. Until about the 1000th element, it was determined that the
sampling rate is higher by "direct search", but after that, a smooth
linear growth begins to a sharp peak and a nonlinear increase in search time.
Thus, the support of database management system functions for working with
spatial data is important for its functioning in the context of geometric data
aggregation tasks.
Data base; GIS; Postgis; Postgresql; Transport infrastructure
A Geographic
Information System (GIS) is a system designed for the collection, storage,
analysis, and graphical visualization of spatial (geographic) data along with
pertinent information about essential object. A GIS may include spatial
databases (including those managed by universal DBMS), editors for raster and
vector graphics, and various tools for spatial data analysis. They are used in
cartography, geology, meteorology, land management, ecology, municipal
management, transportation, economics, defense, and many other fields.
Scientific, technical, technological, and applied aspects of designing,
creating, and using GIS are studied by geoinformatics. The aim of the research
is to determine the methodology of applying GIS for comprehensive modeling of road
transport system factors, as well as to determine the dependence of the
response time of the response (a request to search for individual characters
with key parameters) on the current number of elements in the database table
with indexing enabled and disabled.
Akpan et al. (2022) consider the possibility of using GIS for timely response to the growth of diseases in specific regions, contributing to preparedness for natural disasters and efficient use of healthcare resources. Maina et al. (2019) investigate the need for developing lists of medical facilities and a census of service provision to ensure universal coverage of healthcare services and support sustainable development related to health. The study by Maina et al. (2019) identifies several shortcomings, including the absence of a geographic component for determining the shortest and most accessible route to the required facility. Additionally, the study notes challenges related to updating data to maintain constantly up-to-date information.
Pontin
et al. (2022) and Wang and Yang (2019) have identified links between the
urban environment and the physical activity of different population groups. A
geographic information system was used to obtain characteristics of the impact
of the chosen location as the most convenient tool for collecting and
processing relevant data. Firouraghi et al.
(2022) studied the role of GIS in dementia treatment, examining the
ability of geographic information systems to combine data obtained from
different sources, such as public and individual databases, as well as the
ability to identify and visualize geographic differences in disease structure
over time and space The work by Snyder et al.
(2021) explores methods to broaden access to CAR T-cell therapy
destinations through the application of geographic information systems. The
result of the study was the identification of optimal routes, which led to a
reduction in travel time to medical facilities.
Ji et al. (2019) discuss the use of GIS technologies to create a structure for the
spatiotemporal distribution of infectious diseases in different regions of the
world and assess the risk of importation and spread of these diseases in China.
As a result of the study, a geographic information system was developed that
meets the previously set goals, with the aim of facilitating users to conduct
personal epidemiological investigations with an assessment of the risk of
illness, as well as improving the system for detecting, warning, and timely
responding to the dangers of importing infectious diseases from abroad. Works
by Boulos and Geraghty (2020) and Zhou
et al. (2020) examine the utilization of geographic information
systems as crucial tools for tracking and timely response, illustrated through
the example of the COVID-19 outbreak in 2020. The Medical Geographic
Information System (Medical GIS) application during the COVID-19 pandemic
crisis has become influential in communicating disease surveillance for health
practitioners and society (Supriatna et al.,
2022).
The study by Pomortseva et
al. (2020) focuses on
identifying and monitoring the negative impact of human activity on the
environment using geoinformation systems. The result of the study was a
geospatial analysis, which led to the development of interactive models and
maps of urban pollution, allowing for the identification and systematization of
areas with high concentrations of pollution and comparison with regulatory
values. The articles by Halchenko et al.
(2021) and Sivkov and Sannikov
(2018) are
dedicated to the development of geoinformation databases for monitoring the
status of specially protected natural objects. The obtained geoinformation
databases are relational, meaning that complex queries can be made and fast
spatial statistics can be calculated for thematic groups of objects.
The study by
Belozerov et al. (2019) focuses on
the use of geoinformation technologies in studying migration and demographic
research. The result of the study was analytical and cartographic reports on
migration and demographic changes. Research Conducted by Talipova et al. (2020) and Zhitova and Shlempa (2018) describes the process of forming
a geoinformation database for monitoring the status and use in the tourism
industry of objects of historical and cultural heritage.
In studies
by Lyashkov, Slabunova and
Ariskina (2021)
and Shedrin et al. (2020), examples
are provided to illustrate the creation and utilization of agricultural
geoinformation databases. These databases are designed to enhance the supply
system of government and subordinate organizations by providing them with
up-to-date, accurate, detailed, and comprehensive information regarding the use
of agricultural land. Jeppesen et al. (2018)
discuss the potential of using geoinformation systems in agriculture for
monitoring and controlling the spatial-temporal variability of field
conditions. The result of the work was a geospatial data infrastructure based
on industry requirements and existing standards. Zudilin
and Iralieva (2021) examines the implementation of automated
design of land plots for agricultural purposes based on geoinformation systems
in problem areas, for which a geographic information model was developed. Kudryavtsev (2021) studies the increase in the
efficiency of agriculture using GIS, as well as finding ways to create new
convenient methods for managing both production technologies and natural
resources, taking into account the peculiarities of soil fertility and environmental
factors.
Yunusa, Saidu and Mohammed (2021) analyze possible ways to
implement the new capabilities of geoinformation systems for companies engaged
in management activities. The conclusions drawn from the work were about
increased efficiency and reduced service cost due to lower human and time resource
costs. Girya et al. (2019)
investigate the requirements for implementing geoinformation databases to track
technical maintenance conditions due to the rapid growth of commercial real
estate. The analysis of the required functionality for managing technical
maintenance showed the need to form modules in which information should be
collected in the developed geoinformation database.
The article
by Zaytseva and Taylakov (2021) and Rozhina
et al. (2020) describes methods for managing and analyzing the
functioning of the transportation system. These methods are based on the
consideration of spatially-coordinated data in geoinformation systems. In the
article by Yakovets (2019), the Analytical
Center of the State Traffic Inspectorate deals with management issues to ensure
road safety on the roads of the Russian Federation, using GIS to monitor
accidents and the activities of the State Traffic Inspectorate units. Yuanyuan and Xiaomin (2019) describe the process of building
a geoinformation database for public transport by integrating spatial and
non-spatial information, which is the basis for smart planning and efficient
management of urban public transport. Ergin and Erenoglu
(2018) studied
urban transport problems and ways to solve them through the creation of GIS,
for which information was collected through social surveys and data collection
from transport organizations. As a result, thanks to the obtained GIS, the
results were analyzed and visualized, and optimization of existing solutions
was carried out. The article by Khriplivaya (2021)
provides examples of creating drawings for the construction of linear objects
using GIS technologies. The relevance of using geoinformation systems for
designing, constructing, and operating transport facilities is also raised.
However, there is a problem with linking CAD programs with GIS systems. This
aspect is considered by Talipova et al.
(2022) in their study, where it is noted that there is currently no
universal solution due to some drawbacks in the IFC format, supported by almost
all CAD systems, for GIS.
The article
by Xu, Luis, and Yuce (2023), aimed to address the gap by
introducing a hybrid method that combined the BinCSA with an exact method to
solve a CLSC problem, including location-allocation, transportation, and
supplier selection challenges. Article by Sa'd, Saad and Abd
Wahab (2021) proposes
two algorithms for generating codes for any value of.
The
discussed scientific studies describe the methodology of creating and applying
GIS in various industries. The most studied industries are healthcare and
natural resource management, while the least studied industry is
transportation.
Figure 1 Classification of objects in the transportation system
Currently,
GNSS receivers and mobile laser scanning are used to obtain data on transport infrastructure
objects.
One of the ways to obtain data on the location of objects is through
surveying using a GNSS receiver. This method is most commonly used in the
initial stages of construction, land management, engineering surveys, or
navigation, as the device determines the coordinates of a specific point based
on information from global navigation satellite systems (GNSS) such as GLONASS.
The principle of operation of the receiver is as follows: the device
receives a signal from one or several satellites and calculates the distance to
the specified object on the planetary orbit, considering the speed of radio
wave propagation and the time it takes for the signal to reflect from the
satellite. To determine the most accurate coordinates, the system utilizes data
obtained from multiple GNSS receivers. A simplified diagram illustrating its
operation is presented in Figure 2.
Figure 2 Operating principle of GNSS receivers
The
GNSS receiver is suitable for determining the positioning of point elements of
transport infrastructure objects. Mobile laser scanning is used to obtain
complete information about the technical means of traffic management.
Mobile laser scanning is currently the most promising method for
conducting measurements due to its high performance. The scanning system, which
is a high-speed laser rangefinder, is mounted on a rotating base on a vehicle
(in this study, a car was used, but in general, the system can be installed on
ships or railways). While moving, the rangefinder makes over a thousand
measurements. Thanks to the scanner's 360-degree rotation, a "slice"
of the surrounding space is obtained in one plane.
The study examines a scanning system. A Trimble MX9 (Dual Head, AP60,
Spherical+3x5MP) was used along with the additional Trimble MX GAMS antenna.
Using a mobile laser scanner mounted on the roof of a vehicle, a survey
is conducted along a predetermined route. The route is determined by preparing
a KML file. A file with the extension .kml (Keyhole Markup Language) contains
geospatial data (information about latitude, longitude, and elevation above sea
level) about a specified object or object. These objects can be text
annotations on a map, 2D graphics (polygon, line, or image), or 3D models. This
extension also allows working with both raster and vector graphics, which
together serve as the basis for a cartographic layer. These layers are
subsequently used in applications for working with cartographic data, with the
most commonly used utility being Google Earth.
In the study, KML files created in Google Earth are used as a route for subsequent mobile laser scanning of road objects in the Petrogradsky district of St. Petersburg. The overall view of the uploaded KML file containing information about all the roads in the Petrogradsky district is shown in Figure 3.
Figure 3 Displaying a KML file of the Petrogradsky District in St. Petersburg
The
main parameters incorporated in the foundation used for the survey are the
geographical location of the road, its code, and its name in accordance with
Resolution ? 300 of the Government of St. Petersburg.
Laser
scanning is then performed based on the trajectories obtained from KML files.
The selection of this method for spatial data collection is driven by a high
degree of automation and the ability to use a contactless measurement approach.
The results of laser scanning include point clouds in Autodesk ReCap
project format, panoramic photos, and the vehicle's scanner movement
trajectories in .csv format. After the scans, the scanning data is transferred
to a computer for further processing of the results. Additionally, data from
reference stations, specifically continuously operating EFT reference stations,
are obtained in this study Subsequently, the acquired trajectories are
processed in Applanix POSPac software using data from the reference station
with a one-second recording interval. This operation allows for the generation
of georeferenced trajectories, based on which point clouds are created in
Trimble Business Center software for all completed scans. At the final stage,
the results of mobile laser scanning are processed in specialized software
systems according to the algorithm presented in Figure 4.
Figure 4 The sequence of data processing for transmission to the GIS environment
To
create a geoinformation database, it is necessary to present project data in
the form of an XML representation. The study uses the PostgreSQL database
environment (with the PostGIS extension for extended formats of geometric data
types and, most importantly, functions for working with geodata).
When working with geometry objects (Traffic Control Devices), the
following geometric types were used to represent objects: point, mpoint,
linestring, multilinestring, geometrycollection.
Let's highlight the problems that can be encountered when building the
logic of storing items in the database and updating the data:
- Since the basic geometric data types are limited
to GIS primitives (lines, points), all elements of the road infrastructure need
to be "simplified" to the basic geometry, and, therefore, the objects
that depend on the data – for example, signs on an L-shaped support hanging at
its end – should be taken at the actual point of installation of the support
and at the same time store information about how they look in reality – for
interoperability with CAD;
- The lack of a common standard for coding unique
road signs with variable information leads to the need to store in the
attribute fields the local path to the drawing of individually designed signs
or their icons;
- The dependence of individual transport
infrastructure facilities on other elements (for the purpose of traffic
control) and the lack of hardware ability to unload them from CAD as related
structures leads to the need for subsequent corrective requests (or manual
editing) of elements;
- The presence of a "Geometrycollection"
table for the geometry prevents the layer from being directly loaded into the
QGIS environment for object editing.
Here are some options for how you can work with your data:
- Ability to integrate with other databases
without data loss;
- Ability to add a graphical interface for working
with data, including a web interface;
- Ability to export the contents of the current
TSODD database into graphic files (dwg/other formats) for transfer to CAD;
- Availability of automated data uploading to the
database via plug-ins over CAD or a separate data format.
While the above points, in addition to the last one, already have some
implementation among open-source applications, the implementation of the latter
is complicated by the presence of a proprietary dwg file format. The problem of
plug-in development is also related to different hardware interfaces and the
order of internal data storage in the final CAD system, so there will be no
universal solution, and you will need to develop several separate solutions.
The task of centralizing data in a single environment is not so much the
requirements for the data structure but for the tools for obtaining and
importing them into separate CAD systems used in the design to speed up data
exchange processes, as well as the development of their own format based on the
GML specification for the data exchange, which the team of authors plans to do
in the future.
When
it comes to updating data in a database table, there is always a question of
searching for elements with a set of key parameters, based on which a
conclusion is made – to update/skip/delete an element existing in the database
(DB). Classical databases designed for text/numerical parameters conduct
searches through direct string searches, which, in the case of geometric data
types stored in binary form, results in extended waiting times for the desired
outcome. The fundamental position of using database management systems with
basic support for geometry elements is explained by the built–in search
refinement tools based on the spatial position criterion - that is, all data
table elements can additionally have spatial indexing, which allows you to
search for similar elements by attributes much faster if you know the position
of the current element in its coordinate system.
Speaking about the tools of the database used – PostGIS (over
PostgreSQL), we mean the so-called spatial indexes that are automatically
calculated when adding new elements to the table with indexing enabled for
selected fields of geometric data.
Spatial index is one of the three key
features of a spatial database. Indexes make using a spatial database for large
data sets possible. Without indexing, any search for a feature would require a
“sequential scan” of every record in the database. Indexing speeds up searching
by organizing the data into a search tree, which can be quickly traversed to
find a particular record. Figure 5 shows graphs of the dependence of the
response time of the response (a request to search for individual characters
with key parameters) on the current number of elements in the database table
with indexing enabled and disabled.
Figure 5 Response rate, ms per query with geometry elements With and Without spatial index for the road signs table in PostGIS (PostreSQL)
On
the graph (Figure 5), the upper range of the value is limited to 30 ms; in
contrast to research Nguyen (2009), the original function had
values of up to 160 ms. Until about the 1000th element, the sampling rate is
higher by "direct search," but after that, a smooth linear growth
begins to a sharp peak and a nonlinear increase in search time. Thus, the
support of DBMS functions for working with spatial data is important for its
functioning in the context of geometric data aggregation tasks.
DBMS support for spatial data manipulation functions is important for
its functioning in the context of geometric data aggregation tasks.
The above-mentioned scheme of work, "search for existing table records with key parameters for a specific coordinate/polygon," allows you to quickly decide whether the content of the query is up-to-date/new/outdated information. An example of a conflict situation where such logical inferences are relevant is the merger of databases, where, due to the different coordinate system transformation keys used, there are precedents of "duplication" of elements, and the adopted logic allows you to identify such cases at the query stage. The diagram below (Figure 6) illustrates the process described.
Figure 6 Diagram of working with new data management data in PostgreSQL
This study examines the methodology for creating a GIS of transport
infrastructure objects - from obtaining initial data to storing the data
itself. The article defines the classification of objects in
the transportation system, the procedure for collecting and processing initial
data, as well as the sequence of data processing for transmission to the GIS
environment. A study was conducted on the dependence of response time on the
current number of elements in a database table with indexed and un-indexed data.
Until about the 1000th element, it was determined that the sampling
rate is higher by "direct search", but after that, a smooth linear
growth begins to a sharp peak and a nonlinear increase in search time. Thus,
the support of database management system functions for working with spatial
data is important for its functioning in the context of geometric data
aggregation tasks. Possible
ways to work with data could be: possibility of integration with other
databases without data loss; possibility of adding a graphical interface for
working with data, including a web interface; possibility of downloading the
contents of current elements of the transport infrastructure of the database
into graphic files for transfer to cad; availability of automated data upload
to the database through plug-ins over cad or a separate data format.
This
research was funded by the Russian Science Foundation (project No. 23-78-10176,
https://rscf.ru/en/project/23-78-10176/).
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