Methodology for Calculating Latency of GPS Probe Data

Abstract

Crowdsourced GPS probe data has been gaining popularity in recent years as a source for realtime 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.Keywords: Latency, GPS-probe data, Bluetooth INTRODUCTIONAccurate and timely data is a vital component of any Intelligent Transportation System. In recent years, proliferation of location-aware internet connected devices has enabled private sector to use crowd sourcing technics for providing network wide real-time travel time and speed data for traffic management applications. This has resulted in traffic data services that report speed and travel time in real-time. This data in turn is used by private industry for traveler information and routing, and increasingly by public entities as a replacement for field data collection and to expand observability of roadway conditions network wide. The I-95 Corridor Coalition's Vehicle Probe Project has successfully integrated third party data of this nature, commonly referred to as probe data, for a number of public agency applications. Initial concerns about accuracy were addressed by a comprehensive validation program that compared probe industry reported speeds and travel times with those from a sensor-based reference source. Real-time applications are also sensitive to the latency, that is the time delay between actual field conditions, such as a major slowdown, and when it is reflected in the traffic data stream. Appropriate method to benchmark latency is currently lacking, and is the focus of this paper.Consumer electronics are finding an ever-increasing role in our everyday lives. A majority of these devices are also equipped with a point-to-point networking protocol commonly referred to as Bluetooth. Bluetooth enabled devices can communicate with other Bluetooth enabled devices anywhere from one meter to about 100 meters. This variability in the communications cap...

Cross-Vendor and Cross-State Analysis of GPS Probe Data Latency

“…This paper uses the definition and implements the methodology given by Wang et al ( 7 ) to measure and analyze the latency of probe data. It includes data preparation, data filtering, interpolation, smoothing, and finally data shifting to calculate latency.…”

“…In their previous work (7), authors had observed that latency is not symmetric for each SRE, meaning that the probe data feed might reflect the reduction in the speed with less latency compared to the speed recovery. To revisit that observation, all SRE cases are examined and the percentage of cases that show higher latency in SP compared to RP are reported in table 4.…”

“…In order to measure the latency of probe data with respect to the reference data, start and end time of each Slowdown and Recovery Episode (SRE) as well as the transition time between Slowdown Period (SP) to Recovery Period (RP) in the collected data must be identified. In the previous effort to measure latency (7), authors visually inspected the daily speed graphs to record slowdowns. However, manual approach is tedious and analyzing a large number of segments for an extended time period calls for automation.…”

“…In order to verify the ASPRF output and calibrate the thresholds, it was applied to the same data set used in (7) and results were found to be compatible with the manual process. Table 2 shows the number of slowdown and recovery episodes for each state after running the code.…”

A Data-Driven Method for Vehicle Speed Estimation

“…However, these approaches are still characterized by high signal latency time during the broadcasting of corrections. Typically, this latency is in the range of seconds, which might be far from the requirements of the active solutions implemented on board of a vehicle [11][12].…”