Spatial t hinking and learnings u sing AIS data

methods
A pilot study has been done
Next phase is implementation

Partly funded by Vinnova ( Swedens Innovation Agency)
Joint venture between Transport Analysis and Statistics Sweden (with a bit different objectives)

The Project – what are we doing?

calculations by:
Domestic/international/inland waters
ship type
List of vessels entered in Swedish ports
Passage

lines
Traffic in county regions

AIS data for Baltic Sea, years 2013-2015

Statistical results

Input

Output

be reduced
We will in the end only use a small

part of total data
Part of filtering the data must be done geographicaly
We must identify transports between Swedish ports
Lines must be created from points
Ports have to be created – using AIS data

lines between ports:
Ship id (MMSI)
Position
Time
Ports
This is not available to us

so must be created

data (10m resolution)
Low speed areas

areas created for all countries around Baltic Sea
Good port areas

created for Swedish ports

index a line can be created that represents the transports

between ports (or within regions)
Some advantages of this:
We only create the lines we are interested in, e.g. transports between Swedish ports
It makes us flexible and can create ad hoc tranport lines
Saves us data storage space

And we use this as input for the distance matrix model!

perfomance (tonne kilometers)
Distance between ports should equal distance of the

most common route.
A transportation network is created
Built from the line we created earlier, converted to raster with 1 km resolution.
Additionial weights are added to the network:
Destination
density
Most common route is calculated using shortest path analysis (the resulting route= route with least accumulated cost)

perfomance (tonne kilometers)
Distance between ports should equal distance of the

most common route.
A transportation network is created
Built from the line we created earlier, converted to raster with 1 km resolution.
Additionial weights are added to the network:
Destination
density
Most common route is calculated using shortest path analysis (the resulting route= route with least accumulated cost)

perfomance (tonne kilometers)
Distance between ports should equal distance of the

most common route.
A transportation network is created
Built from the line we created earlier, converted to raster with 1 km resolution.
Additionial weights are added to the network:
Destination
density
Most common route is calculated using shortest path analysis (the resulting route= route with least accumulated cost)

Real routes to Norrköping

perfomance (tonne kilometers)
Distance between ports should equal distance of the

most common route.
A transportation network is created
Built from the line we created earlier, converted to raster with 1 km resolution.
Additionial weights are added to the network:
Destination
density
Most common route is calculated using shortest path analysis (the resulting route= route with least accumulated cost)

Real routes to Norrköping