Processing

Scripts to process MATSim events and network files are available in this repository. The output is movement information in SIM format that can by visualized in Metacity and MATSim network converted to ShapeFile.

SIM files

The .sim format is our dedicated file format which stores movement information and metadata about dynamic subjects.

Each file contains a list of dynamic subjects. Continouous movement for each subject is stored as a MultiTimePoint - moovement is defined in time by "start" in seconds, and for each next second are stored the subject coordinates in "geometry". The list of coordinates is encoded as base64 string.

Format structure example:

[
    {   "meta": {
            "start": 32093, 
            "subject_id": "18", 
            "passengers": [9], 
            "veh_type": "bike", 
            "vehicle_id": "veh_1_bike"
            },
        "geometry": "ratdTnuaJsF5Vndbnv4vwa2rXU57mibBeVZ3W57+L8EMYQypvJomwQrrzmjV/i/B"
   },
    {   "meta": {
            "start": 35642, 
            "subject_id": "264", 
            "passengers": [245,12], 
            "veh_type": "car", 
            "vehicle_id": "veh_78_car"
            },
        "geometry": "1xWWAs2aJsFL1EX94v4vwaHKH1zdmibBUB6zI+9MO8F2pjMm/v4vwaOb1HwCmybBk/13jQ//L8F1UR1OCpsmwWKCO8oU/y/BgBL6FBWbJsG+YB6XI/8vwZTAB0MdmybBJWDPfDP/L8E="
   }, ...
]

If you need to further process the SIM format for your own purposes, you may find the examples below helpful.

Decoding geometry string back to coordinates

import base64
import numpy as 

def base64_to_float64(b64data):
    bdata = base64.b64decode(b64data)
    data = np.frombuffer(bdata, dtype=np.float64)
    return data

geometry_string = "ratdTnuaJsF5Vndbnv4vwa2rXU57mibBeVZ3W57+L8EMYQypvJomwQrrzmjV/i/B"
coordinates = base64_to_float64(geometry_string)

print("Coordinates decoded:", coordinates)
#output: Coordinates decoded: [ -740669.6530584  -1048399.17864485  -740669.6530584  -1048399.17864485  -740702.33017257 -1048426.70470366]

Projection transformation example

Considering the encoded geometry information uses Krovak projection epsg:5514, you may want to also transform the decoded coordinates under different projection to serve your purposes. Shown is a minimal example:

from pyproj import Transformer
def coord_transform(data = [], epsg_in = 'epsg:5514', epsg_out = 'epsg:4326'):
    transformer = Transformer.from_crs(epsg_in, epsg_out)

    transformed = []
    for x1, y1 in zip(data[0::2],data[1::2]): # data as a list / numpy array of coordinates in Krovak
        transformed.append((transformer.transform(x1, y1)))

    return transformed # as a list of tuples in WGS84

#using coordinates variable from previous example 
print("Coordinates transformed", coord_transform(coordinates)) 
#output: Coordinates transformed [(50.04210434386703, 14.461103605875014), (50.04210434386703, 14.461103605875014), (50.04181924386716, 14.460703805874642)]