JL
J. Li
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This MSc thesis aims to research how to efficiently manage 4D AIS data (Longitude, Latitude, Time, and MMSI (the ID of the vessel)) to do the fast query by using the Space Filling curve in PostgreSQL. The AIS is the Automatic Identification System which is born because the frequent occurrence of maritime accidents has caused casualties and economic losses. AIS is intended to assist a vessel’s watchstanding officers and allow maritime authorities to track and monitor the vessels’ movement. For now, the AIS has been used in various kinds of fields because the AIS data is really important and useful. The AIS data contains lots of useful information such as the dynamic information (including ship location, speed, heading, and so on), the static information (including ship name, ship type...), and some other types of data. Because of the useful information that AIS includes, there a great many useful applications based on the AIS data. For example, the AIS data is used for detecting the vessels’ anomalies motions or tracking the vessels. While the studies mainly focus on the applications of the AIS data, The efficient management of the AIS data is neglected. Hence, I am going to study how to efficiently manage the multidimensional AIS data. Space Filling Curve (SFC) will be used to manage the multidimensional AIS data. The SFC is a great method for indexing the multidimensional data. The SFC can map data in multidimensional space to 1D space. There are lots of kinds of SFC, such as Morton curve, Hilbert curve, Gray curve, and so on. And the Morton curve and Hilbert curve are used in this thesis because of the property of the locality form the nD space is preserved in the location on the curve [Dai and Su, 2003] and both are so-called quadrant recursive curves [Meijers and van Oosterom, 2018] which is the very significant property. In my research, I proposed two kinds of methods to manage 4D AIS data. One is the 4D integrated approach that the 4D AIS data is encoded to SFC together. The other is the 3D integrated approach, only 3D AIS (Longitude, Latitude, and Time) data is encoded. To test the two approaches, bounding box query (to find the vessels in a given space and time range) and trajectory query (to find the position information of a specific vessel in a give time range) will be implemented in the database. To verify the usability and superiority of my approach, the benchmark is set. The comparison between the two approaches I proposed will be done. And results prove that the SFC approach I used to manage the 4D AIS data is great after comparing it with the benchmark I put forward.
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This MSc thesis aims to research how to efficiently manage 4D AIS data (Longitude, Latitude, Time, and MMSI (the ID of the vessel)) to do the fast query by using the Space Filling curve in PostgreSQL. The AIS is the Automatic Identification System which is born because the frequent occurrence of maritime accidents has caused casualties and economic losses. AIS is intended to assist a vessel’s watchstanding officers and allow maritime authorities to track and monitor the vessels’ movement. For now, the AIS has been used in various kinds of fields because the AIS data is really important and useful. The AIS data contains lots of useful information such as the dynamic information (including ship location, speed, heading, and so on), the static information (including ship name, ship type...), and some other types of data. Because of the useful information that AIS includes, there a great many useful applications based on the AIS data. For example, the AIS data is used for detecting the vessels’ anomalies motions or tracking the vessels. While the studies mainly focus on the applications of the AIS data, The efficient management of the AIS data is neglected. Hence, I am going to study how to efficiently manage the multidimensional AIS data. Space Filling Curve (SFC) will be used to manage the multidimensional AIS data. The SFC is a great method for indexing the multidimensional data. The SFC can map data in multidimensional space to 1D space. There are lots of kinds of SFC, such as Morton curve, Hilbert curve, Gray curve, and so on. And the Morton curve and Hilbert curve are used in this thesis because of the property of the locality form the nD space is preserved in the location on the curve [Dai and Su, 2003] and both are so-called quadrant recursive curves [Meijers and van Oosterom, 2018] which is the very significant property. In my research, I proposed two kinds of methods to manage 4D AIS data. One is the 4D integrated approach that the 4D AIS data is encoded to SFC together. The other is the 3D integrated approach, only 3D AIS (Longitude, Latitude, and Time) data is encoded. To test the two approaches, bounding box query (to find the vessels in a given space and time range) and trajectory query (to find the position information of a specific vessel in a give time range) will be implemented in the database. To verify the usability and superiority of my approach, the benchmark is set. The comparison between the two approaches I proposed will be done. And results prove that the SFC approach I used to manage the 4D AIS data is great after comparing it with the benchmark I put forward.
Student report
(2019)
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Imke Lánský, Giulia Ceccarelli, Konstantinos Mastorakis, Wessel de Jongh, Jinglan Li, Clara Garcia Sanchez, Jantien Stoter
In 2021, noise pollution monitoring will be mandatory in the Netherlands, which requires data on traffic that can be re-used for air quality estimation models. One of the important input parameters for the latter is the street type, which is required by the dilution parametrisation used within the air quality model.
The goal of this project is to show whether automatic street classification for air quality estimation is feasible and reliable, considering the geo-spatial data currently available in The Netherlands. The motivation for this project originates from the common data used in noise and air quality monitoring tools by the Dutch National Institute for Public Health and the Environment, (RIVM).
Currently, street classification is performed manually by many municipalities. The larger municipalities are legally obliged to monitor air quality levels, which makes use of the street types. Automating the process by using existing datasets can save a lot of time, costs, and resources, while providing standardised results in comparison to manual classification. In addition, our method is extendable to the whole of the Netherlands. Consequently, our method can have a large societal impact, since it allows the provision of air quality estimations for all municipalities; even those that are not yet required to do so. To our knowledge, no similar work has been conducted in this field, which made it even a bigger challenge.
The implementation of the automatic classification algorithm, which is thoroughly explained in this re- port, shows very promising results. We first tested the approaches in a small area, the Weesperstraat in Amsterdam, where we have success rates from 76.7% to 83.3% for the different classification methods when compared to the NSL classification. After evaluating the performance of each of the methods, the optimal approach has been tested on larger areas where visual inspection shows a priori promising results as well.
In addition to the automatic classification algorithm, air quality measurements with new Flow sensors from Plume Labs were performed in the city of Amsterdam. The goal was to investigate whether different street types can be identified through the use of small air quality sensors. The limited measurements did not provide distinct patterns for the different street types, and therefore identification based on pollutant concentrations was not possible within the project.
We hope that the results of this project will motivate public bodies and agencies in the Netherlands to invest in automated workflows using currently available and high accuracy geo-spatial data. This can potentially improve their efficiency, while creating a more standardised and scalable framework. ...
The goal of this project is to show whether automatic street classification for air quality estimation is feasible and reliable, considering the geo-spatial data currently available in The Netherlands. The motivation for this project originates from the common data used in noise and air quality monitoring tools by the Dutch National Institute for Public Health and the Environment, (RIVM).
Currently, street classification is performed manually by many municipalities. The larger municipalities are legally obliged to monitor air quality levels, which makes use of the street types. Automating the process by using existing datasets can save a lot of time, costs, and resources, while providing standardised results in comparison to manual classification. In addition, our method is extendable to the whole of the Netherlands. Consequently, our method can have a large societal impact, since it allows the provision of air quality estimations for all municipalities; even those that are not yet required to do so. To our knowledge, no similar work has been conducted in this field, which made it even a bigger challenge.
The implementation of the automatic classification algorithm, which is thoroughly explained in this re- port, shows very promising results. We first tested the approaches in a small area, the Weesperstraat in Amsterdam, where we have success rates from 76.7% to 83.3% for the different classification methods when compared to the NSL classification. After evaluating the performance of each of the methods, the optimal approach has been tested on larger areas where visual inspection shows a priori promising results as well.
In addition to the automatic classification algorithm, air quality measurements with new Flow sensors from Plume Labs were performed in the city of Amsterdam. The goal was to investigate whether different street types can be identified through the use of small air quality sensors. The limited measurements did not provide distinct patterns for the different street types, and therefore identification based on pollutant concentrations was not possible within the project.
We hope that the results of this project will motivate public bodies and agencies in the Netherlands to invest in automated workflows using currently available and high accuracy geo-spatial data. This can potentially improve their efficiency, while creating a more standardised and scalable framework. ...
In 2021, noise pollution monitoring will be mandatory in the Netherlands, which requires data on traffic that can be re-used for air quality estimation models. One of the important input parameters for the latter is the street type, which is required by the dilution parametrisation used within the air quality model.
The goal of this project is to show whether automatic street classification for air quality estimation is feasible and reliable, considering the geo-spatial data currently available in The Netherlands. The motivation for this project originates from the common data used in noise and air quality monitoring tools by the Dutch National Institute for Public Health and the Environment, (RIVM).
Currently, street classification is performed manually by many municipalities. The larger municipalities are legally obliged to monitor air quality levels, which makes use of the street types. Automating the process by using existing datasets can save a lot of time, costs, and resources, while providing standardised results in comparison to manual classification. In addition, our method is extendable to the whole of the Netherlands. Consequently, our method can have a large societal impact, since it allows the provision of air quality estimations for all municipalities; even those that are not yet required to do so. To our knowledge, no similar work has been conducted in this field, which made it even a bigger challenge.
The implementation of the automatic classification algorithm, which is thoroughly explained in this re- port, shows very promising results. We first tested the approaches in a small area, the Weesperstraat in Amsterdam, where we have success rates from 76.7% to 83.3% for the different classification methods when compared to the NSL classification. After evaluating the performance of each of the methods, the optimal approach has been tested on larger areas where visual inspection shows a priori promising results as well.
In addition to the automatic classification algorithm, air quality measurements with new Flow sensors from Plume Labs were performed in the city of Amsterdam. The goal was to investigate whether different street types can be identified through the use of small air quality sensors. The limited measurements did not provide distinct patterns for the different street types, and therefore identification based on pollutant concentrations was not possible within the project.
We hope that the results of this project will motivate public bodies and agencies in the Netherlands to invest in automated workflows using currently available and high accuracy geo-spatial data. This can potentially improve their efficiency, while creating a more standardised and scalable framework.
The goal of this project is to show whether automatic street classification for air quality estimation is feasible and reliable, considering the geo-spatial data currently available in The Netherlands. The motivation for this project originates from the common data used in noise and air quality monitoring tools by the Dutch National Institute for Public Health and the Environment, (RIVM).
Currently, street classification is performed manually by many municipalities. The larger municipalities are legally obliged to monitor air quality levels, which makes use of the street types. Automating the process by using existing datasets can save a lot of time, costs, and resources, while providing standardised results in comparison to manual classification. In addition, our method is extendable to the whole of the Netherlands. Consequently, our method can have a large societal impact, since it allows the provision of air quality estimations for all municipalities; even those that are not yet required to do so. To our knowledge, no similar work has been conducted in this field, which made it even a bigger challenge.
The implementation of the automatic classification algorithm, which is thoroughly explained in this re- port, shows very promising results. We first tested the approaches in a small area, the Weesperstraat in Amsterdam, where we have success rates from 76.7% to 83.3% for the different classification methods when compared to the NSL classification. After evaluating the performance of each of the methods, the optimal approach has been tested on larger areas where visual inspection shows a priori promising results as well.
In addition to the automatic classification algorithm, air quality measurements with new Flow sensors from Plume Labs were performed in the city of Amsterdam. The goal was to investigate whether different street types can be identified through the use of small air quality sensors. The limited measurements did not provide distinct patterns for the different street types, and therefore identification based on pollutant concentrations was not possible within the project.
We hope that the results of this project will motivate public bodies and agencies in the Netherlands to invest in automated workflows using currently available and high accuracy geo-spatial data. This can potentially improve their efficiency, while creating a more standardised and scalable framework.