Demonstration of a nonlinear dynamic model of traffic flow planning in intelligent transport systems
The significant relevance of the problems of traffic flow planning in intelligent transport systems is pointed out, with a critical analysis of previous scientific studies on this issue. The proposed nonlinear dynamic model of traffic flow planning in intelligent transport systems was characterised. Mathematical modelling methods, methods of correlation and regression analysis, and expert assessments were used. A demonstration of the nonlinear dynamic model of traffic flow planning was carried out, with an example of data developed, and a corresponding optimisation problem formulated and solved. A separate matrix was prepared for each group of constraints. The problem prepared for optimisation was written in the format of the Portfolio Safeguard package. To solve the problem, Solver VANGRB was chosen, which uses the Gurobi optimisation package. The problem contained 1,280 variables C k sr and y k sr each, as well as approximately 50,000 variables x jsrkt . The matrices contained about 1,280, 30,000, 18,000, 1,176 rows of constraints. The optimal values of the variables C k sr and y k sr were found for pairs AB, AC, AD, AE, BA, CA for all types of agreements.
Authors
- Hanna Shelekhan (ORCID: https://orcid.org/0000-0002-6640-6084)
- Yevhen Liubyi (ORCID: https://orcid.org/0000-0003-0681-0920)
- Sergiy Lytvynenko (ORCID: https://orcid.org/0000-0001-7185-1193)
- Viktor Voitsehovskiy (ORCID: https://orcid.org/0000-0002-6147-5160)
- V. М. Kotov
- Alona Desiatko
Institutions
- State University of Trade and Economics (UA)
- Kharkiv National Automobile and Highway University (UA)
- Ukrainian State University of Railway Transport (UA)
- National University "Kyiv Aviation Institute" (UA)
Publication Details
- Journal
- E3S Web of Conferences
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1051/e3sconf/202562103015
- Citations
- 1
- Primary Topic
- Traffic control and management
- Type
- article
- Field-Weighted Citation Impact
- 1.21