User-based relocation strategy for free floating car-sharing system: An Istanbul case

Abstract

In a world with limited resources, it is crucial for individuals to utilise shared systems and develop strategies to optimise their usage. To cope with this, 'servicizing' has emerged as a rapidly growing promising solution, especially in car-sharing systems. These systems can be split into two: station-based and free-floating. The latter introduces more flexibility to the customers as free-floating systems allow users to pick up and drop off vehicles anywhere within predetermined operational zones. This flexibility may come with an additional cost by bringing a potential imbalance between demand and supply. This imbalance can harm the company's profitability and customer satisfaction. In this study, the imbalance problem of the system of free-floating car sharing is considered. A mixed integer linear programming model is developed and tested with real data for free floating car sharing systems to solve this problem. The proposed system consists of four modules: clustering, forecasting, optimization model, and relocation strategy. According to the results, it is observed that the system is more balanced with satisfying 9% more demand and more profitable with earning 6% more. The study was conducted on a car-sharing company that is based in Istanbul, but the results can be applied to any free-floating car-sharing system. This ensures customer satisfaction by meeting demand and balancing the system.

Keywords

• Car-Sharing
• Free-Floating
• User-Based relocation
• Machine learning

Serbest gezen araba paylaşım sistemi için kullanıcı temelli yer değiştirme stratejisi: Bir İstanbul örneği

Abstract

Sınırlı kaynaklara sahip bir dünyada, bireylerin paylaşımlı sistemleri kullanma ve kullanımlarını optimize etmek için stratejiler geliştirmesi hayati öneme sahiptir. Bu duruma cevap olarak, ‘hizmetleştirme’ özellikle araç paylaşım sistemlerinde hızla büyüyen umut verici bir çözüm olarak ortaya çıkmıştır. Bu sistemler ikiye ayrılmaktadır; istasyon tabanlı ve serbest dolaşan. Serbest dolaşan sistemlerin belirlenmiş operasyonel bölgeler içinde araçları herhangi bir yerden alıp bırakmaya izin verdiği bilindiği için müşterilere daha fazla esneklik sunar. Bu esneklik, talep ile arz arasındaki potansiyel bir dengesizlik getirerek ek bir maliyete neden olabilmektedir. Bu çalışmada, serbest dolaşan araç paylaşım sisteminin dengesizlik problemi ele alınmıştır. Bu problemi çözmek için serbest dolaşan araç paylaşım sistemleri için bir karma tamsayılı doğrusal programlama modeli geliştirilmiş ve gerçek verilerle test edilmiştir. Önerilen sistem dört modülden oluşmaktadır: kümeleme, tahmin, optimizasyon modeli ve yeniden konumlandırma stratejisi. Sonuçlara göre, sistemin %9 daha fazla talebi karşılayarak daha dengeli ve %6 daha fazla kazanç sağlayarak daha karlı olduğu gözlemlenmiştir. Çalışma İstanbul merkezli bir araç paylaşım şirketi üzerinde gerçekleştirilmiştir, ancak sonuçlar herhangi bir serbest dolaşan araç paylaşım sistemine uygulanabilir. Bu, talebi karşılayarak ve sistemi dengeleyerek müşteri memnuniyetini sağlamaktadır.

Keywords

• Araç paylaşımı
• Serbest-Gezen
• Kullanıcı tabanlı yeniden konumlandırma
• Makine öğrenmesi

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