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transbigdata-traj

Clean GPS trajectory data by removing drift, smoothing, segmenting, and detecting stay points.

4|2|Updated Feb 4, 2026
Search Tags:#data-cleaning#gps#trajectory-analysis#transbigdata#trajectory-processing#map-matching#spatiotemporal
One-click install
npx skills add https://github.com/ni1o1/claude-skill-transbigdata --skill transbigdata-traj
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: transbigdata-traj
Source: https://github.com/ni1o1/claude-skill-transbigdata/tree/main/skills/transbigdata-traj
Command: npx skills add https://github.com/ni1o1/claude-skill-transbigdata --skill transbigdata-traj

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a repeatable workflow to clean noisy GPS/trajectory data, detect and remove drift and redundant points, smooth and segment trips, identify stay and move segments, and prepare trajectories for map-matching and analysis so that urban mobility datasets become reliable for downstream analytics.

Core Features & Use Cases

  • Drift and Redundancy Removal: Remove speed/distance/angle outliers and duplicate points to improve data quality.
  • Smoothing & Segmentation: Apply Kalman smoothing and time-gap based segmentation to reconstruct continuous trips.
  • Stay/Move Detection & Trajectory Densification: Identify stay points, densify or sparsify samples, and convert points to linestrings for spatial analysis.
  • Map Matching & Length Calculation: Integrate with OSMnx road graphs for map-matching and compute trajectory lengths using Haversine or Euclidean methods.
  • Use Case: Clean taxi GPS logs, detect passenger pick-up/drop-off stays, densify traces for routing, and map-match to road networks for travel behavior studies.

Quick Start

Use the transbigdata-traj skill to clean a taxi GPS CSV, detect stay points with a 30-minute threshold, and convert results into trajectory lines for visualization.

Frequently Asked Questions about transbigdata-traj

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I remove GPS drift and redundant points from vehicle trajectory data?▼

GPS trajectory drift and redundant points are removed by filtering speed, distance, and angle outliers alongside duplicate point elimination to improve raw data quality for urban mobility analysis.

What is the best way to segment a continuous trip from noisy GPS logs?▼

Trip segmentation from noisy GPS logs is best performed using time-gap based segmentation combined with Kalman smoothing to reconstruct continuous, reliable trips from fragmented vehicle traces.

How does stay point detection work for taxi GPS datasets?▼

Stay point detection for taxi GPS datasets works by identifying stationary segments within configurable time and distance thresholds, separating passenger pick-up and drop-off stays from movement traces.

Can I perform map matching with OSMnx road graphs for GPS trajectories?▼

Map matching with OSMnx is supported by integrating trajectory data with OSMnx road graphs, enabling accurate alignment of GPS points to urban road networks for travel behavior studies.

How do I calculate trajectory length from GPS coordinates?▼

Trajectory length is calculated by converting GPS point sequences into linestrings and computing distances using either the Haversine method for geographic accuracy or Euclidean methods for planar data.

Does trajectory densification help with routing analysis?▼

Trajectory densification helps with routing analysis by interpolating additional points between sparse GPS samples, creating denser traces that align more reliably to road network paths.

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