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Configuration File Overview | config.toml
This configuration controls a trajectory processing pipeline that combines GPS clustering, path simplification, and map-matched routing. The three sections run sequentially: raw GPS points are first clustered into stops/movement segments, simplified geometrically, then aligned to the actual road network.
[T-DBSCAN] — Stay-Point Detection
T-DBSCAN (Time-based Density-Based Spatial Clustering) identifies stay points (locations where the user paused for a significant time) versus movement segments in a raw GPS trajectory.
| Parameter | Value | Description |
|---|---|---|
eps_time |
60 |
Temporal threshold in seconds. GPS points are considered part of the same cluster if they are within this time window of each other. |
k |
3 |
Spatial epsilon multiplier or spatial threshold scaling factor. Defines how close points must be spatially to belong to the same cluster. |
min_pts |
5 |
Minimum points required to form a valid cluster. Fewer than 5 points within the eps_time window are treated as noise/transit rather than a stay point. |
How it works: The algorithm scans the trajectory and groups consecutive GPS readings that are close together both in space (scaled by k) and time (within 60 seconds). Clusters with at least 5 points are labeled as stay points (e.g., a coffee break or delivery stop). Everything else is labeled as movement.
[Douglas] — Trajectory Simplification
This section configures the Douglas-Peucker polyline simplification algorithm, which reduces the number of GPS points in movement segments while preserving the overall shape.
| Parameter | Value | Description |
|---|---|---|
eps |
-1 |
Simplification tolerance. A value of -1 enables adaptive/auto mode, meaning the tolerance is calculated dynamically rather than using a fixed distance. |
base |
3 |
Base value for the adaptive tolerance formula. Serves as the starting reference distance (often in meters or a unit relative to the coordinate system). |
k |
1.5 |
Adaptive multiplier. The final tolerance is derived as something like base × k (or a similar function), producing an adaptive threshold of roughly 4.5 units in this case. |
How it works: After T-DBSCAN extracts movement segments, Douglas-Peucker removes redundant GPS points from those segments. Because eps = -1, the simplification strength automatically scales based on the base and k values rather than requiring a hard-coded meter value.
[OSRM] — Map Matching & Routing
This section points to the pre-processed road network data used to snap the simplified GPS trajectory onto actual drivable roads.
| Parameter | Value | Description |
|---|---|---|
path |
C:\Users\TRIZ\clones\TrackEngine\data\algeria-260108.osrm |
Absolute path to the OSRM dataset file. |
About the file:
algeria-260108.osrmis a pre-processed OpenStreetMap extract for Algeria, built using OSRM's toolchain (osrm-extract+osrm-contractorosrm-customize).- The filename likely encodes the region and extract date/version (e.g., Algeria from 26/01/2008 or a similar versioning scheme).
- This
.osrmfile is not the raw OSM XML; it is a binary, memory-mapped routing graph optimized for fast queries.
How it fits in the pipeline:
- T-DBSCAN finds stops and movement phases.
- Douglas-Peucker cleans up the movement phases by dropping unnecessary points.
- OSRM takes the cleaned polyline and performs map matching, snapping the GPS trace to the most likely road path in Algeria's road network.
Pipeline Summary
Raw GPS Trace
↓
[T-DBSCAN] → Stay Points + Movement Segments
↓
[Douglas] → Simplified Movement Segments
↓
[OSRM] → Map-Matched Road Paths
This three-stage design is common in trajectory mining and vehicle telematics: it separates meaningful stops from travel, compresses the data for efficiency, and finally grounds the geometry to real-world roads.