Many alternative approaches to achieve improvement in transportation system sustainability are continuously being proposed. The sheer number and diversity of suggested approaches available in the literature is overwhelming. Decision makers, consequently, need support in the fair and comprehensive evaluation, comparison, and combination of these proposed initiatives. A methodology for the generation of scientific-based decision support on the development of truly integrated strategies to improve transport sustainability is presented in this paper. The key contribution of the approach produced is that all the underlying complexities in the formulation of such strategies are adequately addressed and incorporated in its design. This is achieved through the development of a customized multi-objective metaheuristic simulation optimization model, called the Transport Energy Management Tool (TEMT). A case study application of the TEMT is used to demonstrate its success and usefulness and serves as a proof of concept for adoption of this approach in sustainable transport management.

Although the electrification of transportation can bring long-term sustainability, increasing penetration of Electric Vehicles (EVs) may cause more congestion. Inappropriate deployment of charging stations not only hinders the EVs adoption but also increases the total system costs. This paper attempts to identify the optimal locations for fast-charging stations in the urban network considering heterogeneous vehicles with respect to the traffic congestion at different levels of EVs' penetration. A bi-level optimization framework is proposed to solve this problem in which the upper level aims to locate charging stations by minimizing the total travel time and the infrastructure costs. On the other hand, the lower level captures re-routing behaviors of travelers with their driving ranges. Finally, numerical study is performed to demonstrate the fast convergence of the proposed framework.