A data-driven matching algorithm for ride pooling problem

Name: A data-driven matching algorithm for ride pooling problem
Author: Şahin, Ahmet, Sevim, İ., Albey, Erinç, Güler, M. G.

İsim	A data-driven matching algorithm for ride pooling problem
Yazar	Şahin, Ahmet, Sevim, İ., Albey, Erinç, Güler, M. G.
Basım Tarihi:	2022-04
Basım Yeri	- Elsevier
Konu	Binary programming, Machine learning, Nonlinear bilevel programming, Rank aggregation, Ride matching
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0305-0548
Kayıt Numarası	b26547e3-5912-4632-9136-0f7e4e2c0500
Lokasyon	Industrial Engineering
Tarih	2022-04
Örnek Metin	This paper proposes a data-driven matching algorithm for the problem of ride pooling, which is a transportation mode enabling people to share a vehicle for a trip. The problem is considered as a variant of matching problem, since it aims to find a matching between drivers and riders. Proposed algorithm is a machine learning algorithm based on rank aggregation idea, where every feature in a multi-feature dataset provides a ranking of candidate drivers and weight for each feature is learned from past data through an optimization model. Once weight learning and candidate ranking problems are considered simultaneously, resulting optimization model becomes a nonlinear bilevel optimization model, which is reformulated as a single level mixed-integer nonlinear optimization model. To demonstrate the performance of the proposed algorithm, a real-life dataset from a mobile application of a ride pooling start-up company is used and company's current approach is considered as benchmark. Results reveal that proposed algorithm correctly predicts the first choice of riders 17% to 28% better compared to the benchmark in different scenarios. Similarly, proposed algorithm offers recommendation lists in which the preferred driver is ranked 0.38 to 1.12 person closer (to the rider's actual choice) compared to the benchmark.
DOI	10.1016/j.cor.2021.105666
Cilt	140

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A data-driven matching algorithm for ride pooling problem

Yazar Şahin, Ahmet, Sevim, İ., Albey, Erinç, Güler, M. G.

Basım Tarihi 2022-04

Basım Yeri - Elsevier

Konu Binary programming, Machine learning, Nonlinear bilevel programming, Rank aggregation, Ride matching

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0305-0548

Kayıt Numarası b26547e3-5912-4632-9136-0f7e4e2c0500

Lokasyon Industrial Engineering

Tarih 2022-04

Örnek Metin This paper proposes a data-driven matching algorithm for the problem of ride pooling, which is a transportation mode enabling people to share a vehicle for a trip. The problem is considered as a variant of matching problem, since it aims to find a matching between drivers and riders. Proposed algorithm is a machine learning algorithm based on rank aggregation idea, where every feature in a multi-feature dataset provides a ranking of candidate drivers and weight for each feature is learned from past data through an optimization model. Once weight learning and candidate ranking problems are considered simultaneously, resulting optimization model becomes a nonlinear bilevel optimization model, which is reformulated as a single level mixed-integer nonlinear optimization model. To demonstrate the performance of the proposed algorithm, a real-life dataset from a mobile application of a ride pooling start-up company is used and company's current approach is considered as benchmark. Results reveal that proposed algorithm correctly predicts the first choice of riders 17% to 28% better compared to the benchmark in different scenarios. Similarly, proposed algorithm offers recommendation lists in which the preferred driver is ranked 0.38 to 1.12 person closer (to the rider's actual choice) compared to the benchmark.

DOI 10.1016/j.cor.2021.105666

Cilt 140