Leader-follower green traffic assignment problem with online supervised machine learning solution approach

In this paper, we propose a bi-level green traffic assignment with network design problem. At the upper level, the objective function evaluates a total traffic Carbon monoxide (CO) gas emissions problem to provide a macroscopic viewpoint of the system manager. At the lower-level, a traffic assignment network design problem is considered to individually optimize users’ travel times, with certain links being potential candidates for network addition. Although the lower-level objective function is convex with linear constraints, the proposed bi-level problem is np-hard, and even finding a near-optimal solution is an np-hard task. To address the solution approach, we applied an online supervised machine learning (SML) algorithm which solves the proposed bi-level problem within a reasonable running time. Additionally, a mat-heuristic algorithm is proposed to compare the results with the online SML algorithm. To validate the online SML algorithm, we conducted experiments using real urban transportation examples in medium and large-sized networks.