2023

Journal

Memetic Comp. 15, 71–87 (2023).

This paper models a transit control system for the management of traffic perturbations of public transport. The transit system data is voluminous and highly dynamic. Moreover, the transit domain has a remarkable lack of intelligent systems to monitor and maintain better performance. Consequently, realizing an intelligent transit control system has become a consistent need. The modeling of the system addresses a problem of optimizing performance measures based on key performance indicators. Its objective is to find the optimal control action in disturbance cases. The solution consists in combining all performance measures in a single measure by using fuzzification without neglecting the space and time requirements of the traffic. To model and implement our system we used a multi-agent approach. The experiments performed were based on real network traffic data. The obtained results demonstrate the relevance of the proposed fuzzy approach in our optimization problem and show the advantage of the multi-agent system in the modeling of our control system. We prove that the proposed control system achieves better results than certain existing fuzzy approaches and is able to manage disturbances with a better performance than the existing solutions.

@article{morri2023fuzzy,
  title={Fuzzy logic based multi-objective optimization of a multi-agent transit control system},
  author={Morri, Nabil and Hadouaj, Sameh and Said, Lamjed Ben},
  journal={Memetic Computing},
  volume={15},
  number={1},
  pages={71--87},
  year={2023},
  publisher={Springer}
}