Urban connected vehicle lane planning based on improved Frank Wolfe algorithm

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 algorithm, moth flame optimization algorithm combined with chaos strategy, and adaptive firefly optimization algorithm. The proposed improved Frank Wolfe algorithm can converge at around 30 iterations, with a convergence limit of around 10-4, which is superior to the traditional Frank Wolfe algorithm. The minimum total travel cost of the road system gradually decreases with the increase of the fairness index threshold. The experimental results demonstrate the effectiveness of the proposed urban connected vehicle lane planning model and solving algorithm. The research results contribute to improving the operational safety and efficiency of the road network TS, thereby improving the current traffic situation of the urban TS.