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    Item type:Publication,
    Crowdsourced adaptive vehicle routing framework for Last-Mile delivery in dynamic traffic environments
    (2025-01-01)
    Dahlan, Ahmad Faisal
    ;
    Cheng, Chen Yang
    ;
    Sae-chai, Pornkanok
    ;
    Jientrakul, Ranon
    ;
    Yuangyai, Chumpol
    Last-Mile Delivery (LMD) operations are significantly impacted by real-time traffic disruptions, leading to delays and increased costs. While traditional vehicle routing problem (VRP) model struggle to adapt to dynamic traffic environments, crowdsourced data from social media platforms presents a valuable source of real-time traffic data. This research proposes Crowdsourced Adaptive Vehicle Routing Framework (CAVRF) that integrates crowdsourced social media data into the VRP model for enhanced efficiency. The framework employs a machine learning model to classify tweets based on impact severity and effectively filtering relevant traffic information. Furthermore, a mathematical model known as the Adaptive Traffic VRP (AT-VRP) has been developed to accommodate the integration of social media data with the VRP model. The framework’s effectiveness is demonstrated through a case study using a package delivery network in Jakarta with various levels of traffic disruptions. The findings suggest that integrating crowdsourced social media data into AT-VRP significantly improves efficiency by avoiding any road closure. CAVRF offers a cost-effective and efficient solution to the dynamic challenges inherent in LMD.
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    Item type:Publication,
    Power of crowdsourcing in Twitter to find similar/related users
    (2016-11-18)
    Kanungsukkasem, Nont
    ;
    Leelanupab, Teerapong
    Identifying similar users in social media, e.g., Twitter, is very useful for a variety of applications, such as promoting social connections by user recommendation and defining the type of user accounts for target advertisement. Although many existing methods are available, especially that of Similar-To Framework of Twitter, they require a massive amount of data to process. This has become a major obstacle for non-commercial organizations and in particular academic researchers to study and analyze the similarity, due to the resource requirement to handle large datasets and the limitations of Twitter API to access Tweets per day. Accordingly, this paper proposes a new method that uses only a small amount of data and can be applied by not exceeding Twitter API limitations. Our method, called List Voting, uses only Lists feature that is provided by Twitter to determine accounts of similar users who are likely to produce similar contents. All the Lists are created by Twitter users. Thus, the List can be considered as a crowdsourcing. We also study the characteristics of the definition of crowdsourcing to confirm the consideration. Our experimental result shows that our method gets the benefit of the power of this crowdsourcing and can provide a list of users that are similar or related to a specified user.
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