This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
|
teaching:projh402 [2020/10/12 19:11] ezimanyi [Map-matching as a Service] |
teaching:projh402 [2020/10/22 14:18] ezimanyi [Map-matching as a Service] |
||
|---|---|---|---|
| Line 27: | Line 27: | ||
| However, these platforms are used for static spatial data and are unable to cope with moving objects. The goal of the project is to extend one of these platforms with spatio-temporal data types in order to be able to display animated maps. | However, these platforms are used for static spatial data and are unable to cope with moving objects. The goal of the project is to extend one of these platforms with spatio-temporal data types in order to be able to display animated maps. | ||
| + | {{:teaching:trips2.gif?direct|}} | ||
| + | |||
| + | Animated visualization of car trajectories | ||
| + | |||
| + | **Status**: taken | ||
| ===== Implementing TSBS on MobilityDB ===== | ===== Implementing TSBS on MobilityDB ===== | ||
| Line 34: | Line 39: | ||
| The project consists in implementing a multidimensional generalization of the time_bucket function that allows the user to partition the spatial and/or temporal domain of a table in units (or tiles) that can be used for aggregating data. Then, the project consists of performing a benchmark comparison of TimescaleDB and MobilityDB. | The project consists in implementing a multidimensional generalization of the time_bucket function that allows the user to partition the spatial and/or temporal domain of a table in units (or tiles) that can be used for aggregating data. Then, the project consists of performing a benchmark comparison of TimescaleDB and MobilityDB. | ||
| + | |||
| + | **Status**: taken | ||
| Line 51: | Line 58: | ||
| GPS location tracks typically contain errors, as the GPS points will normally be some meters away from the true position. If we know that the movement happened on a street network, e.g., a bus or a car, then we can correct this back by putting the points on the street. Luckily there are Algorithms for this, called Map-Matching. There are also a handful of open source systems that do map matching. It remains however difficult to end users to use them, because they involve non-trivial installation and configuration effort. Preparing the base map, which will be used in the matching is also an issue to users. | GPS location tracks typically contain errors, as the GPS points will normally be some meters away from the true position. If we know that the movement happened on a street network, e.g., a bus or a car, then we can correct this back by putting the points on the street. Luckily there are Algorithms for this, called Map-Matching. There are also a handful of open source systems that do map matching. It remains however difficult to end users to use them, because they involve non-trivial installation and configuration effort. Preparing the base map, which will be used in the matching is also an issue to users. | ||
| - | {{:teaching:original.png?direct&400|}} | + | {{:teaching:original.png?direct&400|}} |
| Original trajectory | Original trajectory | ||
| - | {{:teaching:mapmatched.png?400|}} | + | {{:teaching:mapmatched.png?direct&400|}} |
| Map-matched trajectory | Map-matched trajectory | ||
| Line 65: | Line 74: | ||
| * [[https://github.com/cyang-kth/fmm|Fast Map Matching]] | * [[https://github.com/cyang-kth/fmm|Fast Map Matching]] | ||
| + | **Status**: taken | ||
| ===== Geospatial Trajectory Data Cleaning ===== | ===== Geospatial Trajectory Data Cleaning ===== | ||