Araştırma Makalesi
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Dilsel Özetleme ile Kentsel Hareketlilik Kalıpları: Bisiklet Verilerinden İçgörüler

Yıl 2023, Cilt: 39 Sayı: 3, 538 - 547, 31.12.2023

Öz

Bu çalışma, tanımlayıcı veri analitiği araçlarından biri olan dilsel özetleme kullanılarak kentsel hareketlilik modellerinin nasıl analiz edilebileceğini incelemektedir. Araştırma, kentsel hareketlilik modellerini anlamak için zengin bir bilgi kaynağı olan kentsel bisiklet verilerine odaklanmaktadır. Çalışmada çeşitli değişkenlere sahip bir veri seti kullanılmaktadır: gün, saat, istasyon ve kart türü. Veriler, bulanık kümenin gücü aracılığıyla kentsel harekete değerli bilgiler sunan dilsel tanımlamalara dönüştürülmektedir. Seyahat modellerinin analizi, günün çeşitli zamanlarındaki yoğun istasyonların belirlenmesini, kullanıcı segmenti tercihlerini (öğrenciler ve öğrenci olmayanlar) ve genel hareketlilikteki değişiklikleri içermektedir. Kentsel bisiklet verilerinin dilsel özetlemesinin sonuçları, kentsel seyahat kalıpları hakkında daha kapsamlı bilgi edinilmesine olanak sağlamaktadır. Şehir planlamacıları, karar vericiler ve ulaşım yetkilileri, kentsel hareketliliğin dinamiklerine ışık tutan sonuçlar sayesinde artık şehrin mevcut altyapısını optimize edebilir, erişilebilirliği artırabilir ve sakinlerinin geniş yelpazedeki ihtiyaçlarını karşılayabilir. Çalışma, tanımlayıcı veri analitiğinin, özellikle şehir bisikletlerinden elde edilen bilgileri kullanarak seyahat modellerini incelemek için kullanıldığında, bilgiyi açığa çıkarmada ne kadar pratik olabileceğini göstermektedir.

Teşekkür

Kayseri Ulaşım AŞ

Kaynakça

  • [1] Sun, L., Axhausen, K.W., 2016. Understanding urban mobility patterns with a probabilistic tensor factorization framework. Transportation Research Part B: Methodological, 91, 511–524.
  • [2] Oxley, J., 2015. Understanding travel patterns to support safe active transport for older adults. Journal of Transport & Health, 2, 79–85.
  • [3] Sun, L., Wang, S., Liu, S., Yao, L., Luo, W., Shukla, A., 2018. A completive research on the feasibility and adaptation of shared transportation in mega-cities–A case study in Beijing. Applied Energy, 230, 1014–1033.
  • [4] Galatoulas, N.F., Genikomsakis, K.N., Ioakimidis, C.S., 2020. Spatio-temporal trends of e-bike sharing system deployment: A review in Europe, North America and Asia. Sustainability, 12, 4611.
  • [5] Liu, Y., Tian, Z., Pan, B., Zhang, W., Liu, Y., Tian, L., 2022. A hybrid big-data-based and tolerance-based method to estimate environmental benefits of electric bike sharing. Applied Energy, 315, 118974.
  • [6] He, Y., Song, Z., Liu, Z., Sze, N.N., 2019. Factors influencing electric bike share ridership: Analysis of Park City, Utah. Transportation Research Record, 2673, 12–22.
  • [7] Kaltenbrunner, A., Meza, R., Grivolla, J., Codina, J., Banchs, R., 2010. Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system. Pervasive and Mobile Computing, 6, 455–466.
  • [8] Kou, Z., Cai, H., 2019. Understanding bike sharing travel patterns: An analysis of trip data from eight cities. Physica A: Statistical Mechanics and its Applications. 515, 785–797.
  • [9] Zhou, X., 2015. Understanding spatiotemporal patterns of biking behavior by analyzing massive bike sharing data in Chicago. PloS One, 10, e0137922.
  • [10] Etienne, C., Latifa, O., 2014. Model-based count series clustering for bike sharing system usage mining: a case study with the Vélib’system of Paris. ACM Transactions on Intelligent Systems and Technology, 5, 1–21.
  • [11] García-Palomares, J.C., Gutiérrez, J., Latorre, M., 2012. Optimizing the location of stations in bike-sharing programs: A GIS approach. Applied Geography, 35, 235–246.
  • [12] Li, Y., Zheng, Y., Zhang, H., Chen, L., 2015. Traffic prediction in a bike-sharing system, Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. Seattle, Washington, 1–10.
  • [13] Kim, K., 2023. Discovering spatiotemporal usage patterns of a bike-sharing system by type of pass: a case study from Seoul. Transportation, 1–35.
  • [14] Eren, E., Katanalp, B.Y., 2022. Fuzzy-based GIS approach with new MCDM method for bike-sharing station site selection according to land-use types. Sustainable Cities and Society, 76, 103434.
  • [15] Lathia, N., Ahmed, S., Capra, L., 2012. Measuring the impact of opening the London shared bicycle scheme to casual users. Transportation Research Part C: Emerging Technologies, 22, 88–102.
  • [16] Levy, N., Golani, C., Ben-Elia, E., 2019. An exploratory study of spatial patterns of cycling in Tel Aviv using passively generated bike-sharing data. Journal of Transportation Geography, 76, 325–334.
  • [17] Liu, J., Li, Q., Qu, M., Chen, W., Yang, J., Xiong, H., Zhong, H., Fu, Y., 2015. Station site optimization in bike sharing systems, 2015 IEEE International Conference on Data Mining. IEEE, Atlantic City, NJ, USA, 883–888.
  • [18] Zhang, Y., Brussel, M.J., Thomas, T., van Maarseveen, M.F., 2018. Mining bike-sharing travel behavior data: An investigation into trip chains and transition activities. Computers, Environment and Urban Systems, 69, 39–50.
  • [19] Zadeh, L.A., 1965. Fuzzy sets. Information and Control, 8, 338–353.
  • [20] Tan, P., Steinbach, M., and Kumar, V., 2006, Introduction to Data Mining, Pearson Education India.
  • [21] Zadeh, L.A., 1975. The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8, 199–249.
  • [22] Zadeh, L.A., 1978. PRUF—a meaning representation language for natural languages, International Journal of Man-Machine Studies, 10, 395–460.
  • [23] Yager, R.R., 1982. A new approach to the summarization of data. Information Sciences, 28, 69–86.
  • [24] Zadeh, L.A., 1983. A computational approach to fuzzy quantifiers in natural languages, Comuters & Mathematics with Applications, 9(1), 149–184.
  • [25] Boran, F.E., Akay, D., Yager, R.R., 2016. An overview of methods for linguistic summarization with fuzzy sets. Expert Systems with Applications, 61, 356–377.
  • [26] Delgado, M., Sánchez, D., Vila, M.A., 2000. Fuzzy cardinality based evaluation of quantified sentences. International Journal of Approximate Reasoning, 23, 23–66.
  • [27] Sánchez, D., Delgado, M., Vila, M.-A., 2009. Fuzzy quantification using restriction levels, Fuzzy Logic and Applications: 8th International Workshop, WILF 2009 Palermo, Italy, 28–35.
  • [28] Liétard, L., Rocacher, D., 2008. Evaluation of quantified statements using gradual numbers, ss. 246-269. Galinda, J., ed. 2008. Handbook of Research on Fuzzy Information Processing in Databases. IGI Global, 926s.
  • [29] Díaz-Hermida, F., Bugarín, A., Barro, S., 2003. Definition and classification of semi-fuzzy quantifiers for the evaluation of fuzzy quantified sentences. International Journal of Approximate Reasoning, 34, 49–88.
  • [30] Díaz-Hermida, F., Losada, D.E., Bugarín, A., Barro, S., 2005. A probabilistic quantifier fuzzification mechanism: The model and its evaluation for information retrieval. IEEE Transactions on Fuzzy Systems, 13, 688–700.
  • [31] Glöckner, I., 2000. Advances in DFS theory. Technical Report TR2000-01, University of Bielefeld, Technical Faculty, PO-Box 100131, 33501, Bielefeld, Germany.
  • [32] Martin, T., & Shen, Y. (2009, June). Fuzzy association rules in soft conceptual hierarchies, NAFIPS 2009-2009 Annual Meeting of the North American Fuzzy Information Processing Society, Cincinnati, OH, USA, 1-6.
  • [33] Matlab, S., 2017, 9.2.0.538062 (R2017a), MathWorks Natick MA.
  • [34] Albuquerque, V., Sales Dias, M., Bacao, F., 2021. Machine learning approaches to bike-sharing systems: A systematic literature review. ISPRS International Journal of Geo-Information, 10, 62.

Urban Mobility Patterns with Linguistic Summarization: Insights from Bicycle Data

Yıl 2023, Cilt: 39 Sayı: 3, 538 - 547, 31.12.2023

Öz

This study examines how urban mobility patterns might be analyzed using linguistic summarization, one of the descriptive data analytics tools. The investigation focuses on urban bicycle data, a rich source of knowledge for comprehending urban mobility patterns. The study uses a dataset with several variables: day, hour, station, and card type. The data is turned into linguistic descriptions that offer valuable insights into urban movement through the strength of the fuzzy set. The analysis of travel patterns includes identifying busy stations at various times of the day, user segment preferences (students vs. non-students), and changes in general mobility. The results of the linguistic summarization of the urban cycling data allow for a more thorough knowledge of urban travel patterns. Urban planners, decision-makers, and transportation authorities may now optimize the city's current infrastructure, increase accessibility, and meet its residents' wide range of needs thanks to the results that shed light on the dynamics of urban mobility. The study shows how practical descriptive data analytics can be in revealing information, mainly when used to examine travel patterns utilizing information from urban bicycles.

Kaynakça

  • [1] Sun, L., Axhausen, K.W., 2016. Understanding urban mobility patterns with a probabilistic tensor factorization framework. Transportation Research Part B: Methodological, 91, 511–524.
  • [2] Oxley, J., 2015. Understanding travel patterns to support safe active transport for older adults. Journal of Transport & Health, 2, 79–85.
  • [3] Sun, L., Wang, S., Liu, S., Yao, L., Luo, W., Shukla, A., 2018. A completive research on the feasibility and adaptation of shared transportation in mega-cities–A case study in Beijing. Applied Energy, 230, 1014–1033.
  • [4] Galatoulas, N.F., Genikomsakis, K.N., Ioakimidis, C.S., 2020. Spatio-temporal trends of e-bike sharing system deployment: A review in Europe, North America and Asia. Sustainability, 12, 4611.
  • [5] Liu, Y., Tian, Z., Pan, B., Zhang, W., Liu, Y., Tian, L., 2022. A hybrid big-data-based and tolerance-based method to estimate environmental benefits of electric bike sharing. Applied Energy, 315, 118974.
  • [6] He, Y., Song, Z., Liu, Z., Sze, N.N., 2019. Factors influencing electric bike share ridership: Analysis of Park City, Utah. Transportation Research Record, 2673, 12–22.
  • [7] Kaltenbrunner, A., Meza, R., Grivolla, J., Codina, J., Banchs, R., 2010. Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system. Pervasive and Mobile Computing, 6, 455–466.
  • [8] Kou, Z., Cai, H., 2019. Understanding bike sharing travel patterns: An analysis of trip data from eight cities. Physica A: Statistical Mechanics and its Applications. 515, 785–797.
  • [9] Zhou, X., 2015. Understanding spatiotemporal patterns of biking behavior by analyzing massive bike sharing data in Chicago. PloS One, 10, e0137922.
  • [10] Etienne, C., Latifa, O., 2014. Model-based count series clustering for bike sharing system usage mining: a case study with the Vélib’system of Paris. ACM Transactions on Intelligent Systems and Technology, 5, 1–21.
  • [11] García-Palomares, J.C., Gutiérrez, J., Latorre, M., 2012. Optimizing the location of stations in bike-sharing programs: A GIS approach. Applied Geography, 35, 235–246.
  • [12] Li, Y., Zheng, Y., Zhang, H., Chen, L., 2015. Traffic prediction in a bike-sharing system, Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. Seattle, Washington, 1–10.
  • [13] Kim, K., 2023. Discovering spatiotemporal usage patterns of a bike-sharing system by type of pass: a case study from Seoul. Transportation, 1–35.
  • [14] Eren, E., Katanalp, B.Y., 2022. Fuzzy-based GIS approach with new MCDM method for bike-sharing station site selection according to land-use types. Sustainable Cities and Society, 76, 103434.
  • [15] Lathia, N., Ahmed, S., Capra, L., 2012. Measuring the impact of opening the London shared bicycle scheme to casual users. Transportation Research Part C: Emerging Technologies, 22, 88–102.
  • [16] Levy, N., Golani, C., Ben-Elia, E., 2019. An exploratory study of spatial patterns of cycling in Tel Aviv using passively generated bike-sharing data. Journal of Transportation Geography, 76, 325–334.
  • [17] Liu, J., Li, Q., Qu, M., Chen, W., Yang, J., Xiong, H., Zhong, H., Fu, Y., 2015. Station site optimization in bike sharing systems, 2015 IEEE International Conference on Data Mining. IEEE, Atlantic City, NJ, USA, 883–888.
  • [18] Zhang, Y., Brussel, M.J., Thomas, T., van Maarseveen, M.F., 2018. Mining bike-sharing travel behavior data: An investigation into trip chains and transition activities. Computers, Environment and Urban Systems, 69, 39–50.
  • [19] Zadeh, L.A., 1965. Fuzzy sets. Information and Control, 8, 338–353.
  • [20] Tan, P., Steinbach, M., and Kumar, V., 2006, Introduction to Data Mining, Pearson Education India.
  • [21] Zadeh, L.A., 1975. The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8, 199–249.
  • [22] Zadeh, L.A., 1978. PRUF—a meaning representation language for natural languages, International Journal of Man-Machine Studies, 10, 395–460.
  • [23] Yager, R.R., 1982. A new approach to the summarization of data. Information Sciences, 28, 69–86.
  • [24] Zadeh, L.A., 1983. A computational approach to fuzzy quantifiers in natural languages, Comuters & Mathematics with Applications, 9(1), 149–184.
  • [25] Boran, F.E., Akay, D., Yager, R.R., 2016. An overview of methods for linguistic summarization with fuzzy sets. Expert Systems with Applications, 61, 356–377.
  • [26] Delgado, M., Sánchez, D., Vila, M.A., 2000. Fuzzy cardinality based evaluation of quantified sentences. International Journal of Approximate Reasoning, 23, 23–66.
  • [27] Sánchez, D., Delgado, M., Vila, M.-A., 2009. Fuzzy quantification using restriction levels, Fuzzy Logic and Applications: 8th International Workshop, WILF 2009 Palermo, Italy, 28–35.
  • [28] Liétard, L., Rocacher, D., 2008. Evaluation of quantified statements using gradual numbers, ss. 246-269. Galinda, J., ed. 2008. Handbook of Research on Fuzzy Information Processing in Databases. IGI Global, 926s.
  • [29] Díaz-Hermida, F., Bugarín, A., Barro, S., 2003. Definition and classification of semi-fuzzy quantifiers for the evaluation of fuzzy quantified sentences. International Journal of Approximate Reasoning, 34, 49–88.
  • [30] Díaz-Hermida, F., Losada, D.E., Bugarín, A., Barro, S., 2005. A probabilistic quantifier fuzzification mechanism: The model and its evaluation for information retrieval. IEEE Transactions on Fuzzy Systems, 13, 688–700.
  • [31] Glöckner, I., 2000. Advances in DFS theory. Technical Report TR2000-01, University of Bielefeld, Technical Faculty, PO-Box 100131, 33501, Bielefeld, Germany.
  • [32] Martin, T., & Shen, Y. (2009, June). Fuzzy association rules in soft conceptual hierarchies, NAFIPS 2009-2009 Annual Meeting of the North American Fuzzy Information Processing Society, Cincinnati, OH, USA, 1-6.
  • [33] Matlab, S., 2017, 9.2.0.538062 (R2017a), MathWorks Natick MA.
  • [34] Albuquerque, V., Sales Dias, M., Bacao, F., 2021. Machine learning approaches to bike-sharing systems: A systematic literature review. ISPRS International Journal of Geo-Information, 10, 62.

Ayrıntılar

Birincil Dil İngilizce
Konular Mekansal Veri ve Bilgi İşleme
Bölüm Makaleler
Yazarlar

Fatma ŞENER FİDAN 0000-0002-2397-3628

Sena AYDOĞAN

Diyar AKAY

Erken Görünüm Tarihi 31 Aralık 2023
Yayımlanma Tarihi 31 Aralık 2023
Gönderilme Tarihi 16 Kasım 2023
Kabul Tarihi 27 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 39 Sayı: 3

Kaynak Göster

APA ŞENER FİDAN, F., AYDOĞAN, S., & AKAY, D. (2023). Urban Mobility Patterns with Linguistic Summarization: Insights from Bicycle Data. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 39(3), 538-547.
AMA ŞENER FİDAN F, AYDOĞAN S, AKAY D. Urban Mobility Patterns with Linguistic Summarization: Insights from Bicycle Data. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. Aralık 2023;39(3):538-547.
Chicago ŞENER FİDAN, Fatma, Sena AYDOĞAN, ve Diyar AKAY. “Urban Mobility Patterns With Linguistic Summarization: Insights from Bicycle Data”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 39, sy. 3 (Aralık 2023): 538-47.
EndNote ŞENER FİDAN F, AYDOĞAN S, AKAY D (01 Aralık 2023) Urban Mobility Patterns with Linguistic Summarization: Insights from Bicycle Data. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 39 3 538–547.
IEEE F. ŞENER FİDAN, S. AYDOĞAN, ve D. AKAY, “Urban Mobility Patterns with Linguistic Summarization: Insights from Bicycle Data”, Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, c. 39, sy. 3, ss. 538–547, 2023.
ISNAD ŞENER FİDAN, Fatma vd. “Urban Mobility Patterns With Linguistic Summarization: Insights from Bicycle Data”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 39/3 (Aralık 2023), 538-547.
JAMA ŞENER FİDAN F, AYDOĞAN S, AKAY D. Urban Mobility Patterns with Linguistic Summarization: Insights from Bicycle Data. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. 2023;39:538–547.
MLA ŞENER FİDAN, Fatma vd. “Urban Mobility Patterns With Linguistic Summarization: Insights from Bicycle Data”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, c. 39, sy. 3, 2023, ss. 538-47.
Vancouver ŞENER FİDAN F, AYDOĞAN S, AKAY D. Urban Mobility Patterns with Linguistic Summarization: Insights from Bicycle Data. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. 2023;39(3):538-47.

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