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RETRACTED ARTICLE: Multimodal transport path optimization model and algorithm considering carbon emission multitask

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This article was retracted on 04 October 2022

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Abstract

The globalization of the economy and trade has made the transportation industry flourish, and the traffic demand is growing. Under this trend, energy consumption is increasing and environmental pollution is becoming more and more serious, so the development of “low-carbon transportation” is inevitable. Intermodality is a green transportation method that reduces transportation costs, shortens transportation time, improves transportation quality, reduces road congestion and is environmentally friendly. It can reduce carbon emissions and noise pollution while improving energy efficiency. Therefore, strengthening the use of intermodality can significantly reduce carbon dioxide emissions, thereby reducing the greenhouse effect. In the present study, carbon emissions are added to the intermodality route study, and an intermodality path selection model in a low-carbon environment is established. Through the use of genetic algorithms and step-by-step method to solve this problem, we find the best low-carbon transportation methods and routes. It has practical application value, enabling decision makers to balance the economic interests of the company while making decisions and to meet the government’s carbon dioxide emission limitations.

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Authors and Affiliations

  1. School of Automobile, Chang’an University, P.O. Box 710064, Xi’an, Shaanxi, China

    HuiFang Li

  2. Zhejiang Scientific Research Institute of Transport, P.O. Box 310006, Hangzhou, Zhejiang, China

    Luan Su

Authors
  1. HuiFang Li
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  2. Luan Su
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Correspondence to HuiFang Li.

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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s11227-022-04853-1

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Li, H., Su, L. RETRACTED ARTICLE: Multimodal transport path optimization model and algorithm considering carbon emission multitask. J Supercomput 76, 9355–9373 (2020). https://doi.org/10.1007/s11227-019-03103-1

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  • DOI: https://doi.org/10.1007/s11227-019-03103-1

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