A sustainable road pricing oriented bilevel optimization approach under multiple environmental uncertainties

The rapidly-increasing urban transportation contributes to a number of externalities such as congestion and pollution; moreover, environmental uncertainties widely exist and bring challenges to creating traffic emission control strategies. To address these problems, this paper proposes an inexact bilevel programming approach under stochastic and fuzzy uncertainties (BLP-SF) for a toll scheme design with the considerations of the externalities of vehicular emission and road pricing policy. The BLP-SF approach can deal with multiple environmental uncertainties by (1) specifying the uncertainties as probability distributions and/or fuzzy sets, and (2) integrating chance-constrained programming and fuzzy possibility programming into the bilevel programming framework. A road pricing problem is exemplified under various policy scenarios to demonstrate the applicability of the BLP-SF approach. It is shown that the proposed BLP-SF approach can achieve optimal charging schemes with improvements in total emission reduction and congestion alleviation compared with traditional models. This study contributes to urban transportation management associated with congestion and emission.