Sürdürülebilir Kentsel Hareketlilik için Açıklanabilir Makine Öğrenmesi: Hava-Mevsim Koşullarında Capital Bikeshare Talebi

Öz

Bisiklet paylaşım sistemleri, hava durumuna ve mevsimselliğe duyarlı talebi görünür kılan ayrıntılı kullanım kayıtları üretmektedir fakat talep tahmini üzerine yapılan çalışmaların çoğu, işletmecilerin planlama için ihtiyaç duyduğu şeffaflıktan çok tahmin doğruluğuna odaklanmaktadır. Bu çalışma, kamuya açık Capital Bikeshare veri setini (Washington, D.C., 2011-2012) kullanarak günlük ve saatlik düzeyde bisiklet paylaşım talebi için açıklanabilir makine öğrenmesi modelleri geliştirmekte ve doğrulamaktadır. Sentetik doldurma yapılmadan gerçekten eksik olan 165 saatlik kaydın korunduğu, tek bir nem sensörü anomalisinin düzeltildiği ve hedef değişkene sızıntıyı önlemek için casual ile registered kullanıcı sayılarının açıklayıcı değişken olarak dışlandığı bir veri kalitesi denetiminin ardından günlük ve saatlik modeller, sıkı biçimde kronolojik bir doğrulama şeması -beş genişleyen takvim tabanlı çapraz doğrulama katmanı ve 2012’nin son çeyreğini kapsayan bir test kümesi- altında karşılaştırılmıştır. Günlük düzeyde doğrusal gün trendi içeren bir Ridge regresyonu (çapraz doğrulama RMSE = 979.4; test kümesi R² = 0.458), saatlik düzeyde ise aynı trend terimini içeren bir XGBoost modeli (çapraz doğrulama RMSE = 73.4; test kümesi R² = 0.757) seçilmiştir. SHAP tabanlı açıklama, günlük düzeyde en belirleyici değişkenler olarak gün trendini, yılı ve sıcaklığı; saatlik düzeyde ise saati, gün trendini ve çalışma günü durumunu göstermiştir. Duyarlılık analizi, aşırı düşük talepli tek bir günün (Capital Bikeshare’in Hurricane Sandy öncesi hizmeti askıya aldığı 29 Ekim 2012) günlük düzeydeki toplam mutlak yüzdesel hatanın %82’sini oluşturduğunu, buna karşın ölçeğe duyarsız hata ölçütlerinin (sMAPE, WAPE) görece durağan kaldığını göstermiştir. Varyans şişirme faktörü tanılamaları, sıcaklıkla güçlü çoklu bağlantı nedeniyle hissedilen sıcaklığın doğrusal model tasarımlarından çıkarılmasını gerektirmiştir. Bulgular, hava durumu ve mevsimselliğe duyarlı, kronolojik olarak doğrulanmış talep tahmininin sürdürülebilir kentsel hareketlilik planlaması için şeffaf bir karar destek aracı olabileceğini desteklerken hizmet kesintisi olaylarının modellenmeyen bir hata kaynağı olduğunu ortaya koymaktadır.

Anahtar Kelimeler

• Açıklanabilir Makine Öğrenmesi
• Bisiklet Paylaşım Talebi
• SHAP
• Kronolojik Doğrulama
• Sürdürülebilir Kentsel Hareketlilik

Explainable Machine Learning for Sustainable Urban Mobility: Capital Bikeshare Demand under Weather and Seasonal Conditions

Öz

Bike-sharing systems generate fine-grained ridership records that make weather- and season-sensitive demand visible, yet many prediction studies emphasize predictive accuracy over the transparency that operators need for planning. This study develops and validates explainable machine learning models for day- and hour-level bike-sharing demand using the publicly available Capital Bikeshare dataset (Washington, D.C., 2011-2012). Following a data-quality screening stage —which retained 165 genuinely absent hourly records without synthetic imputation, corrected a single humidity-sensor anomaly, and excluded casual and registered ridership counts as predictors to prevent target leakage— day- and hour-level models were compared under a strictly chronological validation scheme, using five expanding calendar-based cross-validation folds and a final-quarter 2012 hold-out. A Ridge regression with a linear day-trend term was selected at day level (cross-validation RMSE = 979.4; hold-out R² = 0.458), while an XGBoost model with the same trend term was selected at hour level (cross-validation RMSE = 73.4; hold-out R² = 0.757). SHAP-based explanation identified the day trend, year, and temperature as the leading day-level demand drivers, and hour-of-day, the day trend, and working-day status as the leading hour-level drivers. A sensitivity analysis showed that a single extreme low-demand day (29 October 2012, coinciding with Capital Bikeshare’s service suspension ahead of Hurricane Sandy) accounted for 82% of total day-level absolute percentage error, while scale-robust error measures (sMAPE, WAPE) remained comparatively stable. Variance-inflation diagnostics required excluding apparent temperature from linear model specifications due to strong collinearity with temperature. These findings support weather- and season-aware, chronologically validated demand forecasting as a transparent decision-support tool for sustainable urban mobility planning, while identifying service-disruption events as an unmodeled source of error.

Anahtar Kelimeler

• Explainable Machine Learning
• Bike-Sharing Demand
• SHAP
• Chronological Validation
• Sustainable Urban Mobility

Kaynakça

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