CycleTrajectory: An End-to-End Pipeline for Enriching and Analyzing GPS Trajectories to Understand Cycling Behavior and Environment

TL;DR

An end-to-end pipeline named CycleTrajectory is proposed for processing high-sampling rate GPS trajectory data from cyclists' action cameras, leveraging OpenStreetMap (OSM) for semantic enrichment and facilitates a deeper understanding of cycling behavior and the cycling environment.

Abstract

Global positioning system (GPS) trajectories recorded by mobile phones or action cameras offer valuable insights into sustainable mobility, as they provide fine-scale spatial and temporal characteristics of individual travel. However, the high volume, noise, and lack of semantic information in this data poses challenges for storage, analysis, and applications. To address these issues, we propose an end-to-end pipeline named CycleTrajectory for processing high-sampling rate GPS trajectory data from action cameras, leveraging OpenStreetMap (OSM) for semantic enrichment. The methodology includes (1) Data Preparation, which includes filtration, noise removal, and resampling; (2) Map Matching, which accurately aligns GPS points with road segments using the OSRM API; (3) OSM Data integration to enrich trajectories with road infrastructure details; and (4) Variable Calculation to derive metrics like distance, speed, and infrastructure usage. Validation of the map matching results shows an error rate of 5.64%, indicating the reliability of this pipeline. This approach enhances efficient GPS data preparation and facilitates a deeper understanding of cycling behavior and the cycling environment.

Figures (3)

• Figure 1: Error measurement illustration (Newson and Krumm, 2009).
• Figure 2: Cycling speed distribution.
• Figure 3: Time spent on different cycling infrastructure.