A methodology for calculating the latency of GPS-probe data

TL;DR

This paper introduces a flexible methodology to measure the latency of crowdsourced GPS probe data against ground-truth sources, crucial for real-time traffic management and emergency response.

Contribution

It presents a novel maximum pattern matching algorithm for latency measurement applicable to various ground-truth data sources.

Findings

GPS probe data latency varies with time of day and traffic conditions.

Road segmentation scheme influences latency measurements.

Latency sensitivity to traffic slowdowns and recoveries is analyzed.

Abstract

Crowdsourced GPS probe data has been gaining popularity in recent years as a source for real-time traffic information. Efforts have been made to evaluate the quality of such data from different perspectives. A quality indicator of any traffic data source is latency that describes the punctuality of data, which is critical for real-time operations, emergency response, and traveler information systems. This paper offers a methodology for measuring the probe data latency, with respect to a selected reference source. Although Bluetooth re-identification data is used as the reference source, the methodology can be applied to any other ground-truth data source of choice (i.e. Automatic License Plate Readers, Electronic Toll Tag). The core of the methodology is a maximum pattern matching algorithm that works with three different fitness objectives. To test the methodology, sample field reference data were collected on multiple freeways segments for a two-week period using portable Bluetooth sensors as ground-truth. Equivalent GPS probe data was obtained from a private vendor, and its latency was evaluated. Latency at different times of the day, the impact of road segmentation scheme on latency, and sensitivity of the latency to both speed slowdown, and recovery from slowdown episodes are also discussed.