Determining the Macroscopic Fundamental Diagram from Mixed and Partial Traffic Data

  • Yangbeibei Ji Shanghai University
  • Mingwei Xu Shanghai University
  • Jie Li Hunan University
  • Henk J van Zuylen Delft University of Technology
Keywords: macroscopic fundamental diagram, urban traffic, probe vehicles, GPS, loop detector, incomplete data

Abstract

The macroscopic fundamental diagram (MFD) is a graphical method used to characterize the traffic state in a road network and to monitor and evaluate the effect of traffic management. For the determination of an MFD, both traffic volumes and traffic densities are needed. This study introduces a methodology to determine an MFD using combined data from probe vehicles and loop detector counts. The probe vehicles in this study were taxis with GPS. The ratio of taxis in the total traffic was determined and used to convert taxi density to the density of all vehicles. This ratio changes over the day and between different links. We found evidence that the MFD was rather similar for days in the same year based on real data collected in Changsha, China. The difference between MFDs made of data from 2013 and 2015 reveals that the modification of traffic control can influence the MFD significantly. A macroscopic fundamental diagram could also be drawn for an area with incomplete data gained from a sample of loop detectors. An MFD based on incomplete data can also be used to monitor the emergence and disappearance of congestion, just as an MFD based on complete traffic data.

Author Biographies

Yangbeibei Ji, Shanghai University
Associated professor; School of Management
Mingwei Xu, Shanghai University
Master student; School of Management
Jie Li, Hunan University
lecturer; Civil Engineering College
Henk J van Zuylen, Delft University of Technology
Professor; Department of Transport & Planning, Faculty of Civil Engineering and Geosciences

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Published
2018-06-27
How to Cite
1.
Ji Y, Xu M, Li J, van Zuylen HJ. Determining the Macroscopic Fundamental Diagram from Mixed and Partial Traffic Data. Promet - Traffic & Transportation [Internet]. 27Jun.2018 [cited 19Jul.2018];30(3):267-79. Available from: http://www.fpz.unizg.hr/traffic/index.php/PROMTT/article/view/2406
Section
Articles