Society today is enjoying an unprecedented level of human mobility but is also confronting environmental degradation resulting from fossil fuel consumption and greenhouse gas emissions. The electrification of bus transit systems is recognized as one of the practical solutions to mitigate air pollution and other externalities of increased mobility. However, the implementation of an e-bus system requires the purchase of e-buses and the development of charging infrastructure. To reduce costs and maximize benefits, it is crucial to develop an integrated strategy during the planning stage. This study applies a GIS-based multi-criteria decision analysis approach to determine the candidate bus routes to convert from diesel-powered to electric-driven. This framework appraises not only the characteristics of bus routes but also the possibility of deploying charging infrastructures in bus terminals. Fourteen common criteria are used to evaluate the main considerations of bus electrification, including economic, environmental, and social benefits and costs. The analytic hierarchy process and the technique of order preference similarity to the ideal solution are employed to determine the criteria weights and the route ranking, respectively. The bus network of Twin Cities, MN, U.S., is used as a study case to present the proposed approach. Sensitivity analysis is included to identify the overall top 10 bus routes. The result shows that this method can use widely available open data to select top candidate routes that meet multiple criteria.
In the early years of 21st century, the ever-increasing volume of greenhouse gases from fossil fuel consumption have made humans seek alternative, non-polluting fuels as an effective strategy to reduce pollution and prevent related environmental issues. Electric vehicles (EVs) are today known as one of the most effective solutions for this purpose. To inform transportation planning and policies pertinent to EVs, models are needed to capture travelers’ behavior for vehicle type and route choice and the impacts on traffic congestion. The present study proposes a mixed complementarity traffic assignment model for the networks involving both EVs and gasoline vehicles so that the demand for each vehicle class depends on the characteristics of the paths and availability of electric charging stations on the paths. To determine the demand of each class, this study applies a logit choice model, which incorporates the effect of ownership and operating costs on demand of each class under different subsidy policies. This paper further investigates the charging behavior of EVs by considering private and public charging facilities in urban areas. To this end, the complementary traffic assignment algorithm has been used to solve the mentioned assignment problem. Besides, we used a label-setting algorithm for solving the constraint shortest path problem. The results of applying the mixed-user equilibrium to Sioux Falls and Chicago sketch networks demonstrate that our proposed algorithm outperforms existing algorithms for both solution time and accuracy across multiple networks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.