Cities that have the means for ferry and rail transportation options for their urban mobility have high levels of potential benefits in terms of economy, environment, and sustainability. However, to take full advantage of these systems’ benefits, the integration between these different modes of public transportation systems must be improved. In this study, the goal is to optimize the integration between rail and ferry transportation modes using a developed innovative advanced traveler information system. By using historical data (such as ridership, and weather) from the corresponding authorities and conducted revealed preference survey results, insight into the current state of the rail and ferry transportation systems can be obtained using machine learning and pattern recognition methods. With the constraints and the objective function obtained from the current state diagnosis, the integration between the rail and ferry transportation systems can be optimized by using different optimization methods. Hence, public buses and alternative transportation modes such as bike- sharing can also be utilized to further improve the integration between rail and ferry transportation systems and make them more sustainable.


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    Title :

    Moving Towards Sustainable Transportation: Integrating Rail and Ferry Transportation for a Connected and Smart Istanbul Mobility


    Additional title:

    Lecture Notes in Operations res.


    Contributors:
    Gong, Daqing (editor) / Ma, Yixuan (editor) / Fu, Xiaowen (editor) / Zhang, Juliang (editor) / Shang, Xiaopu (editor) / Gokasar, Ilgin (author) / Ozcan, Ece (author) / Deveci, Muhammet (author) / Tang, Mincong (author)

    Conference:

    International Conference on Logistics, Informatics and Service Sciences ; 2023 ; Beijing, China July 25, 2023 - July 28, 2023


    Published in:

    LISS 2023 ; Chapter : 8 ; 99-104


    Publication date :

    2024-07-21


    Size :

    6 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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