A Probabilistic Traffic-Theoretic Network Loading Model Suitable for Large-Scale Network Analysis

This paper formulates an analytical stochastic network loading model. It is a stochastic formulation of the link transmission model (LTM), which itself is an operational formulation of Newell's simplified theory of kinematic waves. The proposed model builds on an existing initial model. It proposes a formulation with enhanced scalability. In particular, compared with the initial model, it has a complexity that is linear rather than cubic in the link's space capacity. This makes it suitable for large-scale network analysis. The model is validated versus a simulation-based implementation of the stochastic LTM. The proposed model yields significant gains in computational efficiency while preserving accuracy. The validation experiments illustrate how computational run times of the proposed model increase linearly with the link's space capacity, while the initial model has an exponential increase in run times. The proposed model yields accurate distributional approximations of the link's boundary conditions. It is used to address a probabilistic formulation of a citywide signal control problem. The model is shown to be robust to the quality of the initial signal plans. It yields signal plans that systematically outperform both initial plans, as well as a plan derived by widely used commercial signal control software. The model is suitable for large-scale network optimization. The online appendix is available at https://doi.org/10.1287/trsc.2017.0804.

• Record URL:
  • https://doi.org/10.1287/trsc.2017.0804
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/1767714
• Authors:
  • Lu, Jing
  • Osorio, Carolina
• Publication Date: 2018-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1509-1530
• Serial:
  • Transportation Science
  • Volume: 52
  • Issue Number: 6
  • Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
  • ISSN: 0041-1655
  • Serial URL: http://transci.journal.informs.org/

Subject/Index Terms

• TRT Terms: Macroscopic traffic flow; Network analysis (Planning); Stochastic processes; Traffic models
• Subject Areas: Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01691441
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 25 2019 10:34AM