A Cellular Automaton Model with Random Update Rules for Urban Traffic Flow

Traffic jamming can easily lead to wasting time and fuel consumption and induce traffic accidents, thus seriously affecting daily life. In this study, an urban traffic flow cellular automaton (CA) model with random update rules is proposed to analyze the influence of network size and the probabilities of the change of the motion directions of cars, from up to right (p [subscript ur]) and from right to up (p [subscript ru]) on traffic flow. Simulation results show that, as the size of the system increases, the critical density tends to decrease causing larger phase transition, and for a larger size network system, the critical density is stable. The greater the p [subscript ur] and p [subscript ur], the greater the average velocity of vehicles, which means increase in the opportunity that vehicle change directions effectively avoids the formation of traffic jamming. By studying the operational status of urban traffic flow from the microlevel, it can provide some new ideas for alleviating urban traffic jamming.

• Record URL:
  • https://doi.org/10.1155/2022/4607340
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/5121625
• Supplemental Notes:
  • © 2022 Cheng Da et al.
• Authors:
  • Da, Cheng
  • Qian, Yongsheng
  • Zeng, Junwei
  • Zhang, Yongzhi
  • Xu, Dejie
• Publication Date: 2022-4

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: Article ID 4607340
• Serial:
  • Journal of Advanced Transportation
  • Volume: 2022
  • Publisher: John Wiley & Sons, Incorporated
  • ISSN: 0197-6729
  • EISSN: 2042-3195
  • Serial URL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2042-3195
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Traffic congestion; Traffic density; Traffic flow; Traffic models; Urban highways
• Subject Areas: Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01846947
• Record Type: Publication
• Files: TRIS
• Created Date: May 25 2022 9:35AM