Modern drivers equipped with GPS-enabled devices digest traffic information and work as traffic information providers. Driving experiences and fuel consumption are also shared in novel systems to help users’ route choices. Navigators in special terrain cities like Genova, Italy achieve sub-optimal results when they reroute the traffic greedily towards light-congested roads inducing new traffic jams. Furthermore, the absence of user preferences and advanced profiling curtail the competency of navigators to provide need-based responses to their users. This article aims to present a deliverable for Green MaaS for an adaptive urban planning project Liguria region in Italy to fill the gaps present in current navigators suggesting more optimal routing solutions considering the user requirements. The GeTUP application provides a specific pertinent and germane service equipped with an interface that is competent to acclimate to the characteristics of its users. The logical navigation algorithm is based on automated self-learning internet-of-things-based architecture that works on user choices to generate high-level route information. On receiving the request, the server executes the search algorithm based on the provided origin, destination and percentage of desired high-level information required based on the user profiling. The results are based on both the real-time traffic rhythm and near-future traffic distribution estimates based on the rerouting decisions. GeTUP application is implemented as a pilot MaaS solution for the city of Genova. The real-time data obtained upon evaluation revealed that the application contributes actively to coordinating the traffic based on user decisions and preferences between different alternatives and alleviates congestion situations.


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    Title :

    GeTUP: An Internet of Things-Based Navigation System Architecture for High-Level Route Planning


    Additional title:

    Lect.Notes Mobility



    Conference:

    Transport Research Arena Conference ; 2024 ; Dublin, Ireland April 15, 2024 - April 18, 2024



    Publication date :

    2025-03-07


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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