Algorithmic Analysis of GTFS-RT vehicle position accuracy

TL;DR

This paper introduces three new algorithms for geodesic intersection calculations and applies them to analyze real-time transit data, revealing persistent large-scale positional discrepancies and proposing practical solutions for improvement.

Contribution

The paper presents novel algorithms for geodesic intersections and applies them to real-time transit data analysis, highlighting data anomalies and proposing practical accuracy improvements.

Findings

Certain data anomalies can be corrected

Large-scale discrepancies persist in vehicle positions

Practical solutions can improve positional accuracy

Abstract

This paper presents three novel algorithms for calculating geodesic intersections on an ellipsoid. These algorithms are applied in a case study analyzing real-time transit data in California to assess vehicle position drift. The analysis reveals that while certain data anomalies can be corrected, large-scale discrepancies persist. The paper concludes by proposing a set of practical solutions that can be implemented by either data producers or consumers to significantly improve positional accuracy.