Abstract
Evaluating traffic networks is crucial for administration of roadway systems, to better address congestion, safety, and air quality issues around the globe. However, challenges in implementation abound, including major investment costs, large and dynamic data streams and a need for real-time response. Recently developed Global Positioning System (GPS) data loggers are a promising tool for traffic monitoring, thanks to their low cost, ready availability on smartphones, and ability to simultaneously track many travelers and vehicles, relative to expensive, built-in traffic GPS. GPS data from many travelers provides real-time details of traffic conditions and can improve active traffic management using various big-data analytics. We demonstrate how to couple such GPS data to estimate relative roadway speeds in order to improve system management. By analyzing real-time traffic surveillance software with high data coupling and concurrent processing, a new coupling method for real-time traffic evaluation is proposed. Experimental results show efficient coupling of all available GPS data with road condition can improve traffic state estimation accuracy. This new method may increase matching accuracy by more than 1 m in vehicle position. Over 98% of GPS data can be successfully matched to service routes when the low-cost GPS devices are used to detect real-time traffic conditions. The results of traffic network evaluation could well serve as a driving assistant for connected and autonomous vehicles (C/AVs) and other traffic operations.
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Acknowledgments
This research was supported by the China Scholarship Council (CSC) and the China Postdoctoral Science Foundation (2016M592645). The authors would like to thank the Chongqing Municipal Transportation Information Centre for providing real traffic data used in this paper, Dr. Chris Claudel for his constructive comments, and Scott Schauer-West for editing and administrative support.
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Ma, Q., Kockelman, K. A Low-Cost GPS-Data-Enhanced Approach for Traffic Network Evaluations. Int. J. ITS Res. 17, 9–17 (2019). https://doi.org/10.1007/s13177-018-0152-z
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DOI: https://doi.org/10.1007/s13177-018-0152-z