On the Analytical Probabilistic Modeling of Flow Transmission Across Nodes in Transportation Networks

This paper focuses on the analytical probabilistic modeling of vehicular traffic. It formulates a stochastic node model. It then formulates a network model by coupling the node model with the link model of Lu and Osorio (2018), which is a stochastic formulation of the traffic-theoretic link transmission model. The proposed network model is scalable and computationally efficient, making it suitable for urban network optimization. For a network with r links, each with a space capacity of one, the model has a complexity of O(rl). The network model yields the marginal distribution of link states. The model is validated versus a simulation-based network implementation of the stochastic link transmission model. The validation experiments consider a set of small networks with intricate traffic dynamics. For all scenarios, the proposed model accurately captures the traffic dynamics. The network model is used to address a signal control problem. Compared with the probabilistic link model of Lu and Osorio (2018) with an exogenous node model and a benchmark deterministic network loading model, the proposed network model derives signal plans with better performance. The case study highlights the added value of using between-link (i.e., across-node) interaction information for traffic management and accounting for stochasticity in the network.

• Record URL:
  • https://doi.org/10.1177/03611981221094829
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • Jing Lu https://orcid.org/0000-0002-9937-8067 © National Academy of Sciences: Transportation Research Board 2022.
• Authors:
  • Lu, Jing
  • 0000-0002-9937-8067
  • Osorio, Carolina
• Publication Date: 2022-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 209-225
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2676
  • Issue Number: 12
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Mathematical models; Network nodes; Simulation; Stochastic processes; Traffic flow; Urban areas
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01849081
• Record Type: Publication
• Files: TRIS, TRB, ATRI
• Created Date: Jun 22 2022 9:36AM