Urban connected vehicle lane planning based on improved Frank Wolfe algorithm
Anqi Jiang, Faziawati binti Abdul Aziz, Norsidah binti Ujang, Mohd Afzan bin Mohamed
TL;DR
This paper proposes an improved lane planning model for urban connected vehicles using enhanced optimization algorithms to improve traffic safety and efficiency.
Contribution
The novelty lies in combining improved whale optimization and Frank-Wolfe algorithms for urban connected vehicle lane planning.
Findings
The improved whale optimization algorithm achieved 95.27% accuracy and 92.65% recall on the Iris dataset.
The improved Frank Wolfe algorithm converges in about 30 iterations with a convergence limit of 10-4.
The model reduces road system travel cost as the fairness index threshold increases.
Abstract
As the new generation of information technology matures and improves, the functions of intelligent connected vehicles become more and more perfect, and the number of urban connected vehicles is also increasing. To provide an effective optimization scheme to the mixed traffic flow road network in the networked environment, the study investigates the lane planning for urban connected vehicles method. First, a lane planning for urban connected vehicles bi-level programming model is constructed. Then, the upper-level model is solved using improved whale optimization, and the lower-level model is solved using improved Frank-Wolfe algorithm. The results showed that the accuracy and recall of the proposed improved whale optimization algorithm on the Iris dataset were 95.27% and 92.65%, respectively, which were superior to traditional whale optimization algorithm, moth flame optimization…
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Taxonomy
TopicsTraffic control and management · Transportation Planning and Optimization · Traffic Prediction and Management Techniques