Heterogeneity aware urban traffic control in a connected vehicle environment: A joint framework for congestion pricing and perimeter control

Real-time control of large-scale urban networks has been attracting significant research attention. This paper, using the information provided by connected vehicles, proposes a novel control approach based on the concept of perimeter control to maximize the social welfare of all vehicles. The contributions of this paper are threefold. First, the authors consider vehicle heterogeneity (i.e. corresponding to different transportation modes, or with different occupancies, values of time, priority levels, etc.) and integrate a priority scheme into perimeter control to improve both the traffic performance and the social welfare. This is achieved by installing priority lanes at some of the perimeter intersections. Unlike the existing research works that provide priority to certain traffic modes, the authors dynamically identify the groups of vehicles that they should prioritize, in order to maximize the social welfare. Second, the authors develop a model predictive control approach to simultaneously optimize the toll for using the priority lanes and the traffic signal timings at the perimeter intersections. This approach can explicitly handle the constraint of the storage capacity of each intersection link. Third, the authors propose a recursive estimation algorithm to update their knowledge on the distribution of the value of times (VOTs), using the lane choice information of connected vehicles. The proposed approach is tested in a simulated network which resembles the main features of the city center of Zurich, Switzerland. By using the proposed strategy, the traffic accumulation inside the network is still stabilized, and the monetary costs due to delay are significantly reduced at the entire network (up to 25.8%) compared to the strategy without priority. The distribution of the combined cost (including cost due to delay and tolls) is more uniform across VOT groups than that resulting from the strategy without priority. It is also shown that the proposed recursive estimation algorithm quickly converges and further improves the social welfare.

• Record URL:
  https://doi.org/10.1016/j.trc.2019.06.007
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X18316127
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Yang, Kaidi
  • Menendez, Monica
  • 0000-0001-5701-0523
  • Zheng, Nan
• Publication Date: 2019-8

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 439-455
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 105
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Congestion pricing; Connected vehicles; High occupancy vehicle lanes; Highway traffic control; Optimization; Urban areas; Value of time
• Geographic Terms: Zurich (Switzerland)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01710836
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 16 2019 10:18AM