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Bisiklet Paylaşımı Büyük Veri Kümelerinde Kullanıcıların İstasyon Tercihlerinin Analizi

Yıl 2020, Ejosat Özel Sayı 2020 (ARACONF), 591 - 597, 01.04.2020
https://doi.org/10.31590/ejosat.araconf71

Öz

Bisiklet Paylaşım Sistemleri (BPS), geleneksel taşımacılık sistemlerini tercih etmek istemeyen şehir sakinleri için alternative bir taşımacılık sistemi sunan ve son yıllarda yaygınlık kazanan sistemlerdir. BPS kullanan şehir sakinleri, açık havada sportif bir aktivite yaparak varmak istedikleri hedeflerine ulaşabilmektedirler. BPS çevre dostu yaklaşımları, hareketliliğe zorlayıcı yanı ve temiz taşımacılık fırsatı gibi çeşitli avantajları sayesinde diğer taşımacılık sistemlerinden daha yaygın ve tercih edilir hale gelmişlerdir. BPS’ler çok fazla sayıda kullanıcı tarafından tercih edildikçe BPS operatörleri daha iyi bilgiler edinebilmek için kullanıcılarının verilerini toplamaya başlamışlardır. Literatürde, BPS veri kümelerini kullanan ve şehir örüntü analizini de içeren çeşitli çalışmalar bulunmaktadır. Bu çalışmada, BPS büyük veri kümesi kullanılarak farklı kullanıcı türlerinin istasyon tercihlerinin analizi yapılmıştır. Her bir kullanıcı türü için bisiklet istasyonları ve bu istasyonlara yapılan ziyaretler saydırılmış ve her bir kullanıcı türü için en çok tercih edilen ilk 10 istasyon, tercih edilen istasyonlar olarak çıkarılmıştır. Deneysel sonuçlar, Müşteri ve Üye kullanıcı türlerinin farklı istasyon tercihleri olduğunu doğrulamışlardır.

Kaynakça

  • Bikes, D. (2020) Divvy Bike Sharing Dataset. Available: https://www.divvybikes.com/ Access Date: 01.02.2020.
  • Cheng, P., Hu, J., Yang, Z., Shu, Y., & Chen, J. (2018). Utilization-aware trip advisor in bike-sharing systems based on user behavior analysis. IEEE Transactions on Knowledge and Data Engineering, 31(9), 1822-1835.
  • Dell’Amico, M., Iori, M., Novellani, S., & Subramanian, A. (2018). The bike sharing rebalancing problem with stochastic demands. Transportation research part B: methodological, 118, 362-380.
  • Eren, E., & Uz, V. E. (2019). A Review on Bike-Sharing: The Factors Affecting Bike-Sharing Demand. Sustainable Cities and Society, 101882.
  • Faghih-Imani, A., Eluru, N., El-Geneidy, A. M., Rabbat, M., & Haq, U. (2014). How land-use and urban form impact bicycle flows: evidence from the bicycle-sharing system (BIXI) in Montreal. Journal of Transport Geography, 41, 306-314.
  • Faghih-Imani, A., & Eluru, N. (2015). Analysing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system. Journal of transport geography, 44, 53-64.
  • Hyland, M., Hong, Z., de Farias Pinto, H. K. R., & Chen, Y. (2018). Hybrid cluster-regression approach to model bikeshare station usage. Transportation Research Part A: Policy and Practice, 115, 71-89.
  • Jiménez, P., Nogal, M., Caulfield, B., & Pilla, F. (2016). Perceptually important points of mobility patterns to characterise bike sharing systems: The Dublin case. Journal of Transport Geography, 54, 228-239.
  • Li, Y., & Zheng, Y. (2019). Citywide bike usage prediction in a bike-sharing system. IEEE Transactions on Knowledge and Data Engineering,
  • Vogel, P., Greiser, T., & Mattfeld, D. C. (2011). Understanding bike-sharing systems using data mining: Exploring activity patterns. Procedia-Social and Behavioral Sciences, 20, 514-523.
  • Wei, X., Luo, S., & Nie, Y. M. (2019). Diffusion behavior in a docked bike-sharing system. Transportation Research Part C: Emerging Technologies, 107, 510-524.
  • Wergin, J., & Buehler, R. (2017). Where Do Bikeshare Bikes Actually Go?: Analysis of Capital Bikeshare Trips with GPS Data. Transportation research record, 2662(1), 12-21.
  • Yang, Z., Chen, J., Hu, J., Shu, Y., & Cheng, P. (2019). Mobility modeling and data-driven closed-loop prediction in bike-sharing systems. IEEE Transactions on Intelligent Transportation Systems.

Station Preference Analysis of Users in Bike Sharing Systems Big Datasets

Yıl 2020, Ejosat Özel Sayı 2020 (ARACONF), 591 - 597, 01.04.2020
https://doi.org/10.31590/ejosat.araconf71

Öz

Bike Sharing Systems (BSS) have emerged as an alternative transportation tool for city residents who do not want to prefer conventional transportation systems. By using BSS, city residents could reach their desired destinations while making sports activity in fresh air. BSS became more preferred and prevalent among other transportation systems because of their several benefits, such as environmental friendly, activity enforcing and fresh transportation opportunity. After BSS are being utilized by more users, BSS operators started to collect the BSS datasets to gain insights from these datasets. In the literature, several applications are performed using BSS datasets, including urban pattern analysis. In this study, BSS big dataset is used for analyzing station preferences of different user types. The bike stations and their visits are counted and sorted for each user type, and top-10 preferred bike stations are extracted for each user type as preferred stations. Experimental results show that Customer and Subscriber user types have different station preferences, as hypothesized in this study.

Kaynakça

  • Bikes, D. (2020) Divvy Bike Sharing Dataset. Available: https://www.divvybikes.com/ Access Date: 01.02.2020.
  • Cheng, P., Hu, J., Yang, Z., Shu, Y., & Chen, J. (2018). Utilization-aware trip advisor in bike-sharing systems based on user behavior analysis. IEEE Transactions on Knowledge and Data Engineering, 31(9), 1822-1835.
  • Dell’Amico, M., Iori, M., Novellani, S., & Subramanian, A. (2018). The bike sharing rebalancing problem with stochastic demands. Transportation research part B: methodological, 118, 362-380.
  • Eren, E., & Uz, V. E. (2019). A Review on Bike-Sharing: The Factors Affecting Bike-Sharing Demand. Sustainable Cities and Society, 101882.
  • Faghih-Imani, A., Eluru, N., El-Geneidy, A. M., Rabbat, M., & Haq, U. (2014). How land-use and urban form impact bicycle flows: evidence from the bicycle-sharing system (BIXI) in Montreal. Journal of Transport Geography, 41, 306-314.
  • Faghih-Imani, A., & Eluru, N. (2015). Analysing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system. Journal of transport geography, 44, 53-64.
  • Hyland, M., Hong, Z., de Farias Pinto, H. K. R., & Chen, Y. (2018). Hybrid cluster-regression approach to model bikeshare station usage. Transportation Research Part A: Policy and Practice, 115, 71-89.
  • Jiménez, P., Nogal, M., Caulfield, B., & Pilla, F. (2016). Perceptually important points of mobility patterns to characterise bike sharing systems: The Dublin case. Journal of Transport Geography, 54, 228-239.
  • Li, Y., & Zheng, Y. (2019). Citywide bike usage prediction in a bike-sharing system. IEEE Transactions on Knowledge and Data Engineering,
  • Vogel, P., Greiser, T., & Mattfeld, D. C. (2011). Understanding bike-sharing systems using data mining: Exploring activity patterns. Procedia-Social and Behavioral Sciences, 20, 514-523.
  • Wei, X., Luo, S., & Nie, Y. M. (2019). Diffusion behavior in a docked bike-sharing system. Transportation Research Part C: Emerging Technologies, 107, 510-524.
  • Wergin, J., & Buehler, R. (2017). Where Do Bikeshare Bikes Actually Go?: Analysis of Capital Bikeshare Trips with GPS Data. Transportation research record, 2662(1), 12-21.
  • Yang, Z., Chen, J., Hu, J., Shu, Y., & Cheng, P. (2019). Mobility modeling and data-driven closed-loop prediction in bike-sharing systems. IEEE Transactions on Intelligent Transportation Systems.
Toplam 13 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Ahmet Şakir Dokuz 0000-0002-1775-0954

Yayımlanma Tarihi 1 Nisan 2020
Yayımlandığı Sayı Yıl 2020 Ejosat Özel Sayı 2020 (ARACONF)

Kaynak Göster

APA Dokuz, A. Ş. (2020). Station Preference Analysis of Users in Bike Sharing Systems Big Datasets. Avrupa Bilim Ve Teknoloji Dergisi591-597. https://doi.org/10.31590/ejosat.araconf71