On the value of relative flow data

Traffic flow can be described using three dimensions, i.e., space x, time t and cumulative flow N. This study considers estimating the cumulative flow over space and time, i.e., N(x,t), using relative flow data collected by stationary and moving observers. Stationary observers, e.g., loop-detectors, can observe flow at fixed position over time. Furthermore, automated or other equipped and connected vehicles can serve as moving observers that observe flow relative to their position over time. To present the value of relative flow data, in this paper, the authors take the perspective of a model-based estimation approach. In this approach, the data is used in two processes: (1) information assimilation of real-time data and models and (2) learning of the models used in information assimilation based on historical data. This paper focuses on traffic state estimation on links. However, the authors explain that, in absence of stationary observer that are positioned at the link boundaries, it is valuable to consider the information propagation over nodes. Throughout this study a Lighthill-Whitham-Richards (LWR)-model with a triangular fundamental diagram (FD) is used to develop the principles that can be used for the two processes. These principles are tested in a simulation (VISSIM) study. This study shows that the authors can find the traffic flow model parameters and can partially estimate the link boundary conditions based on relative flow data collected by moving observers alone. It also shows that the traffic flow behavior differs partially from the LWR-model with triangular FD, and therefore, the authors recommend the option to learn and use other traffic flow models in future research. Overall, relative flow data is considered valuable to obtain model learning datasets and to estimate the traffic state.

• Record URL:
  • https://doi.org/10.1016/j.trc.2019.05.001
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X18317303
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2019 Paul B.C. van Erp et al. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • van Erp, Paul B C
  • Knoop, Victor L
  • Hoogendoorn, Serge P
• Publication Date: 2020-4

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 74-90
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 113
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Estimation theory; Macroscopic traffic flow; Mathematical models; Network links; Simulation; Traffic density; Traffic speed
• Uncontrolled Terms: Fundamental diagrams
• Subject Areas: Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01707605
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 6 2019 9:30AM