Sustainable road network design considering hydrogen fuel cell vehicles

Abstract

Environmental pollution and energy shortages have brought about an increased focus on new energy vehicles. Hydrogen fuel cell vehicles (HFCVs) have experienced rapid development due to the potential to alleviate energy pressures and reduce pollution emissions. Near zero-carbon emissions offer a promising avenue for promoting sustainable transportation development. To evaluate the impact of HFCVs on the transportation environment, this paper investigates the problem of sustainable transportation network design including HFCVs. Specifically, the problem is formulated as a bi-level multi-objective programming problem, with the upper level aimed at determining the optimal network design scheme considering multiple objectives, while the lower level addresses the mixed traffic flow that comprises both HFCVs and fuel vehicles. To solve the multi-objective sustainable network design problem, an integrated solution framework that combines the particle swarm optimization (PSO) algorithm with the Frank-Wolfe algorithm (FW) is developed. Specifically, the PSO algorithm is utilized to solve the upper-level model and identify the optimal network design schemes, while FW algorithm is adopted to handle mixed traffic flow assignments. Finally, the proposed model and algorithm are implemented in two numerical experiment to demonstrate their effectiveness and efficiency.

Forecasting Hydrogen Vehicle Refuelling for Sustainable Transportation: A Light Gradient-Boosting Machine Model

“…These models prove advantageous for handling data limitations such as missing values, outliers, and categorical features. Additionally, some of the selected models provide robustness to noise and outliers, i.e., they can filter out irrelevant information and focus on underlying patterns-an advantage when dealing with data limitations such as small sample sizes, missing values, or noisy data [4,27,[45][46][47]. The models are evaluated on performance (performance metrics presented in Section 2.4), model scalability, feature engineering, hyperparameter flexibility, and robustness to assess their usability further.…”

“…Hydrogen fuel vehicles (HFVs) are noted to be the future of this transition as they produce zero emissions once on the road [2]. Hydrogen fuel as an alternative to traditional gasoline and diesel has gained momentum recently, and HFVs have become increasingly available to consumers [3,4]. However, as with any new technology, many unknowns and potential challenges must be addressed.…”

“…However, as with any new technology, many unknowns and potential challenges must be addressed. One such challenge is understanding the refuelling behaviour of drivers and its impact on refuelling infrastructure [4]. Adequate infrastructure must be in place to support higher adoption rates of these vehicles [4], but establishing a hydrogen refuelling infrastructure is expensive because of insufficient vehicle demand [2,[4][5][6].…”

“…Import the trained LightGBM model developed. 4. Use the loaded model to make predictions for the year 2020 using the features in the data.…”