Dilsel Özetleme ile Kentsel Hareketlilik Kalıpları: Bisiklet Verilerinden İçgörüler

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

Fatma Şener Fidan , Sena Aydoğan , Diyar Akay

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.

Anahtar Kelimeler

Kentsel hareketlilik, Bisiklet paylaşım sistemi, Dilsel özetleme, Bulanık kümeler, Seyahat örüntüsü

Teşekkür

Kayseri Ulaşım AŞ

Urban Mobility Patterns with Linguistic Summarization: Insights from Bicycle Data

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.

Anahtar Kelimeler

Urban mobility, Bike-sharing system Linguistic summarization, Fuzzy sets, Travel pattern

Kaynakça

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