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

Author:

Wang Zhongxiang1,Hamedi Masoud2,Sharifi Elham2,Young Stanley3

Affiliation:

1. Department of Civil & Environmental Engineering, University of Maryland, MD

2. Center for Advanced Transportation Technology, University of Maryland, College Park, MD

3. Advanced Transportation and Urban Scientist, National Renewable Energy Laboratory, CO

Abstract

Crowd sourced GPS probe data have become a major source of real-time traffic information applications. In addition to traditional traveler advisory systems such as dynamic message signs (DMS) and 511 systems, probe data are being used for automatic incident detection, integrated corridor management (ICM), end of queue warning systems, and mobility-related smartphone applications. Several private sector vendors offer minute by minute network-wide travel time and speed probe data. The quality of such data in terms of deviation of the reported travel time and speeds from ground-truth has been extensively studied in recent years, and as a result concerns over the accuracy of probe data have mostly faded away. However, the latency of probe data—defined as the lag between the time at which disturbance in traffic speed is reported in the outsourced data feed, and the time at which the traffic is perturbed—has become a subject of interest. The extent of latency of probe data for real-time applications is critical, so it is important to have a good understanding of the amount of latency and its influencing factors. This paper uses high-quality independent Bluetooth/Wi-Fi re-identification data collected on multiple freeway segments in three different states, to measure the latency of the vehicle probe data provided by three major vendors. The statistical distribution of the latency and its sensitivity to speed slowdown and recovery periods are discussed.

Publisher

SAGE Publications

Subject

Mechanical Engineering,Civil and Structural Engineering

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Evaluation and augmentation of traffic data including Bluetooth detection system on arterials;Journal of Intelligent Transportation Systems;2019-07-01

2. Automatic Detection of Major Freeway Congestion Events using Wireless Traffic Sensor Data: Machine Learning Approach;Transportation Research Record: Journal of the Transportation Research Board;2019-05-25

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