Abstract

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.

摘要

快速增长的城市交通导致拥堵和污染等一系列外部因素；此外，环境不确定性广泛存在，为制定交通排放控制策略带来挑战。为了解决这些问题，本文提出了一种随机和模糊不确定性下的不精确双层规划方法（BLP-SF），用于考虑车辆排放和道路定价政策的外部性的收费方案设计。BLP-SF 方法可以通过 (1) 将不确定性指定为概率分布和/或模糊集，以及 (2) 将机会约束规划和模糊可能性规划集成到双层规划框架中来处理多种环境不确定性。在各种政策情景下举例说明了道路定价问题，以证明 BLP-SF 方法的适用性。结果表明，与传统模型相比，所提出的 BLP-SF 方法可以实现最优充电方案，在总排放量减少和拥堵缓解方面有所改善。本研究有助于与拥堵和排放相关的城市交通管理。