Collecting ambient vehicle trajectories from an instrumented probe vehicle: High quality data for microscopic traffic flow studies

This paper presents the methodology and results from a study to extract empirical microscopic vehicular interactions from a probe vehicle instrumented with sensors to monitor the ambient vehicles as it traverses a 28 mi long freeway corridor. The contributions of this paper are two fold: first, the general method and approach to seek a cost-effective balance between automation and manual data reduction that transcends the specific application. Second, the resulting empirical data set is intended to help advance traffic flow theory in general and car following models in particular. Generally the collection of empirical microscopic vehicle interaction data is either too computationally intensive or labor intensive. Historically automatic data extraction does not provide the precision necessary to advance traffic flow theory, while the labor demands of manual data extraction have limited past efforts to small scales. Key to the present study is striking the right balance between automatic and manual processing. Recognizing that any empirical microscopic data for traffic flow theory has to be manually validated anyway, the present study uses a “pretty good” automated processing algorithm followed by detailed manual cleanup using an efficient user interface to rapidly process the data. The study spans roughly two hours of data collected on a freeway during the afternoon peak of a typical weekday that includes recurring congestion. The corresponding data are being made available to the research community to help advance traffic flow theory in general and car following models in particular.

• Record URL:
  https://doi.org/10.1016/j.trc.2016.09.001
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X16301577
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Authors:
  • Coifman, Benjamin
  • Wu, Mo
  • Redmill, Keith
  • 0000-0003-1332-1332
  • Thornton, Douglas A
• Publication Date: 2016-11

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 254-271
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 72
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Car following; Data collection; Empirical methods; Lane changing; Laser radar; Microscopic traffic flow; Motor vehicles; Probe vehicles; Traffic data; Traffic flow theory; Trajectory
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01616683
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 21 2016 1:24PM