• Integrated optimization of spa...

Treffer: Integrated optimization of spatiotemporal resources at the intersection for delay minimization using genetic algorithm.

Title:
Integrated optimization of spatiotemporal resources at the intersection for delay minimization using genetic algorithm.
Authors:
Yang Z; College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing, China., Wu L; College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing, China., Li G; College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing, China., Xu Y; College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing, China., Liu W; College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing, China.
Source:
PloS one [PLoS One] 2026 Jan 23; Vol. 21 (1), pp. e0339519. Date of Electronic Publication: 2026 Jan 23 (Print Publication: 2026).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Imprint Name(s):
Original Publication: San Francisco, CA : Public Library of Science
MeSH Terms:
Algorithms* , Models, Theoretical*, Spatio-Temporal Analysis ; Genetic Algorithms
References:
PLoS One. 2019 Jun 10;14(6):e0216958. (PMID: 31181080)
Entry Date(s):
Date Created: 20260123 Date Completed: 20260123 Latest Revision: 20260126
Update Code:
20260126
PubMed Central ID:
PMC12829821
DOI:
10.1371/journal.pone.0339519
PMID:
41575968
Database:
MEDLINE

Weitere Informationen

Integrated optimization of spatiotemporal resources at the intersection (IOSTRI) is crucial for traffic signal control, where both the lane allocation and signal control plans are optimized in a unified framework. This paper addresses the IOSTRI problem with delay minimization, formulating it as a binary mixed-integer nonlinear program (BMINLP) model that fully incorporates all possible uses of shared lanes and lane utilization adjustments. A genetic algorithm tailored to the model's characteristics is designed, where four modules named lane converter, signal plan converter, flow calculation function and delay calculation function are used to calculate the fitness of each solution. Numerical results show the proposed model and algorithm's ability to adapt to diverse traffic flow distribution patterns. High-quality solutions are obtained within 40-55 seconds, representing a significant improvement over previous studies and satisfying the requirements for real-time adaptive control of a single intersection.
(Copyright: © 2026 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The authors have declared that no competing interests exist.