Kişiselleştirilmiş yönlendirme için deneyime dayalı bir yöntem

Kullanıcının tercihlerine göre uyarlanmış navigasyon cihazları, kişiselleştirilmiş rotalar sunar. Ancak, birden çok kullanıcı söz konusu olduğunda, herkesin tercihlerine uygun bir rota bulmak ve çıkar çatışmalarından kaçınmak zor olabilir. Bu bağlamda karar destek sistemleri kullanıcıların kararlar almalarını kolaylaştırabilir. Geleneksel sistemler tipik olarak yalnızca bir kullanıcının veya benzer tercihlere sahip bir grubun önceden tanımlanmış tercihlerini dikkate alır. Bu çalışma, farklı tercihlere sahip bir kullanıcı grubu için, zaman ve mekanla ilgili deneyimlerini dikkate alan, karar destek destek sistemine dayalı bir yöntem sunar. Bu yöntem, grup üyelerinin tercihlerini dikkate alan kişiselleştirilmiş bir navigasyon sistemi oluşturmak için Nesnelerin İnterneti, etmen tabanlı modelleme, çok amaçlı optimizasyon ve kitle kaynaklı verileri kullanır. Çalışma, bu yöntemin nasıl uygulanabileceğini göstermek için Grasshopper ve Rhino kullanılarak bir simülasyon geliştirir. Bu araştırmanın orijinal katkısı, heterojen bir grup için kişiselleştirilmiş navigasyon sistemlerine sosyal yönlerin nasıl dahil edilebileceğini göstermektir. Bu çalışmanın karşılaştığı en büyük sıkıntı veri paylaşım politikalarıdır.

Anahtar Kelimeler:

Etmen Tabanlı Modelleme, Kitle Kaynaklı Veriler, Nesnelerin İnterneti, Kişiselleştirilmiş Navigasyon., Agent-based modelling, Crowdsourced Data, IoT, Multi-user, Personalized Routing.

An experience-based method for personalized routing

Navigation devices that are tailored to the user's preferences offer personalized routes. When multiple users are involved, it can be hard to find a route that suits everyone's preferences and avoid conflicting interests. A decision support system can improve the quality of user decisions. Traditional systems typically consider only the predefined preferences of one user or a group with similar preferences. This study aims to develop a decision support system for a group of people with diverse preferences, using a method that considers their experiences regarding time and space. The method utilizes IoT, agent-based modeling, multi-objective optimization, and crowdsourced data to create a personalized navigation system for a group, such as a family car, that considers each group member's preferences. The study uses simulation to demonstrate how this method can be applied, and it is created using Grasshopper for Rhino and add-ons. The main original contribution of this research is to show how social aspects can be incorporated into personalized navigation systems for a heterogeneous group. The major challenge was the data-sharing policies.

Keywords:

Etmen Tabanlı Modelleme, Kitle Kaynaklı Veriler, Nesnelerin İnterneti, Kişiselleştirilmiş Navigasyon., Agent-based modelling Crowdsourced Data, IoT, Multi-user, Personalized Routing., Agent-based modelling, Crowdsourced Data, IoT, Multi-user, Personalized Routing.,

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Gunantara, N. (2018). A review of multi-objective optimization: Methods and its applications. Cogent Engineering, 5(1). https://doi.org/10.1080/23311916.2018.1502242
Huang, H., Klettner, S., Schmidt, M., Gartner, G., Leitinger, S., Wagner, A., & Steinmann, R. (2014). AffectRoute – considering people’s affective responses to environments for enhancing route-planning services. International Journal of Geographical Information Science, 28(12), 2456-2473. https://doi.org/10.1080/13658816.2014.931585
Kengpol, A. (2008). Design of a decision support system to evaluate logistics distribution network in Greater Mekong Subregion Countries. International Journal of Production Economics, 115(2), 388-399.
Liu, T. K., Moskowitz, P. A., Greenwood, M. C., Lieberman, L. I., & Wood, D. A. (2002). System for personalized mobile navigation information. U.S. Patent No. 6,349,257. Washington, DC: U.S. Patent and Trademark Office.
Majid, A., Chen, L., Chen, G., Mirza, H. T., Hussain, I., & Woodward, J. (2013). A context-aware personalized travel recommendation system based on geotagged social media data mining. International Journal of Geographical Information Science, 27(4), 662-684. https://doi.org/10.1080/13658816.2012.696649
Mermelstein, Y. Z. (2017). Method and system for providing personalized navigation services and crowd-sourced location-based data. U.S. Patent Application No. 15/187,400. Ng, A. (n.d.). Machine learning. Coursera. https://www.coursera.org/learn/machine-learning
Palanca, J., Terrasa, A., Rodriguez, S., Carrascosa, C., & Julian, V. (2021). An agent-based simulation framework for the study of urban delivery. Neurocomputing, 423, 679-688.
Payne, A. (2016, October 25). Firefly. Food4Rhino. https://www.food4rhino.com/app/firefly
Quan, J. C., & Cho, S. B. (2014, June). A hybrid recommender system based on AHP that awares contexts with Bayesian networks for smart TV. In International Conference on Hybrid Artificial Intelligence Systems (pp. 527-536). Springer, Cham.
Sha, W., Kwak, D., Nath, B., & Iftode, L. (2013, February). Social vehicle navigation: integrating shared driving experience into vehicle navigation. In Proceedings of the 14th workshop on mobile computing systems and applications (pp. 1-6).
Smuts, C. (n.d.). Mosquito – Synthetic spaces. Synthetic Spaces-Conceptual Explorations of the Evolving Dimenions- Architecture, Industrial Design, Furniture. https://www.synthetic.space/synthetic/2443/
Sopher, H., Schaumann, D., & Kalay, Y. E. (2016). Simulating Human Behavior in (Un) Built Environments: Using an Actor Profiling Method. International Journal of Computer, Electrical, Automation and Information Engineering, 10(12), 2030-2040.
Wan, L., Hong, Y., Huang, Z., Peng, X., & Li, R. (2018). A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks. International Journal of Geographical Information Science, 32(11), 2225-2246. https://doi.org/10.1080/13658816.2018.1458988
Zheng, W., & Liao, Z. (2019). Using a heuristic approach to design personalized tour routes for heterogeneous tourist groups. Tourism Management, 72, 313-325. https://doi.org/10.1016/j.tourman.2018.12.013
Zhu, X., Hao, R., Chi, H., & Du, X. (2017). FineRoute: Personalized and time-aware route recommendation based on check-ins. IEEE Transactions on Vehicular Technology, 66(11), 10461-10469. https://doi.org/10.1109/tvt.2017.2764999