Public transport optimisation is becoming everyday a more difficult and challenging task, because of the increasing number of transportation options as well as the increase of users. Many research contributions about this issue have been recently published under the umbrella of the smart cities research. In this work, we sketch a possible framework to optimize the tourist bus in the city of Barcelona. Our framework will extract information from Twitter and other web services, such as Foursquare to infer not only the most visited places in Barcelona, but also the trajectories and routes that tourist follow. After that, instead of using complex geospatial or trajectory clustering methods, we propose to use simpler clustering techniques as K-means or DBScan but using a real sequence of symbols as a distance measure to incorporate in theclustering process the trajectory information.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    On the Use of Social Trajectory-Based Clustering Methods for Public Transport Optimization


    Contributors:


    Publication date :

    2013


    Size :

    12 Seiten





    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    A Flight Trajectory Prediction Method based on Trajectory Clustering

    Wang, Guangchao / Chen, Hui / Liu, Kun et al. | IEEE | 2019


    Public transport trajectory planning with probabilistic guarantees

    Varga, Balázs / Tettamanti, Tamás / Kulcsár, Balázs et al. | Elsevier | 2020


    K-means ship trajectory clustering algorithm based on trajectory image similarity

    Shi, Qi / Fan, Yaqiong / Zhang, Danpu et al. | SPIE | 2023



    Trajectory Clustering for Metroplex Operations

    Leiden, K. / Atkins, S. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2011