Traffic signal timing optimisation based on genetic algorithm approach, including drivers’ routing

By Halim Ceylan, Michael G. H. Bell. 2004

328 citations recorded by the source corpus.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/563215a4c6458ff1f33d0e1e363a2236db72e90e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Kalyanmoy Deb
2. Lara Codecà
3. Dongbin Zhao
4. Zhen Zhang
5. Jie Wang
6. Tianshu Chu
7. Pekka Malo
8. Ankur Sinha

In Control and Systems Engineering

1. Kalyanmoy Deb
2. Lara Codecà
3. Dongbin Zhao
4. Zhen Zhang
5. Jie Wang
6. Tianshu Chu
7. Pekka Malo
8. Ankur Sinha

In Transportation

1. Kalyanmoy Deb
2. Lara Codecà
3. Dongbin Zhao
4. Zhen Zhang
5. Jie Wang
6. Tianshu Chu
7. Pekka Malo
8. Ankur Sinha

Citations in the corpus

• Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control (2019, methodology): “Since the 90s, various interdisciplinary techniques have been applied to ATSC, such as fuzzy logic [5], genetic algorithm [6], and immune network algorithm [7].”
• Computational Intelligence in Urban Traffic Signal Control: A Survey (2012, methodology): “[30] used the GA to optimize time parameters for junctions.”
• A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications (2017, background): “Instead of going through specific problems, we refer the readers to a variety of applications of bilevel optimization to the area of network design [21], [38], [42], [44], [68], [73], [99], [114], [119], [133], [178], [180], [181], [184].”