ARAÇLARIN KRİTİK ARALIK KABUL KARARLARININ PEKİŞTİRMELİ ÖĞRENMEYLE SİMÜLASYONU

Yıl 2017, , 161 - 178, 19.09.2017

Bekir Oğuz Bartın

https://doi.org/10.17482/uumfd.338803

Cited By: 1

Öz

Bu çalışma pekiştirmeli öğrenme yöntemini kullanarak
araçların kritik aralık kabul kararlarının basit bir T-kavşakta modellemesini
sunmaktadır. Önerilen yaklaşım araçların ulaşım ağlarındaki nihai amaçlarının
bekleme sürelerini ve risklerini optimize edeceklerini varsayarak basit bir kritik
aralık kabul kararını pekiştirmeli öğrenme problemine çevirmektedir. Trafik
simülasyon yazılımında sürücüler kararlarının yol açacağı sonuçları bilmeyerek
hareket eder, fakat bir çok simülasyon episodu sonrasında eylemlerinin
sonuçlarını bekleme süresi ve risk şeklinde öğrenmeye başlarlar. Gerçek bir
dönel kavşağın Paramics trafik simülasyon modeli deneysel analizler için
kullanılmıştır. Elde edilen sonuçlara göre kullanılan bu simülasyon modeli “Q-learning”
öğrenme yöntemi kullanılınca sürücülerin kritik aralık kabul kararlarının
doğrulaması kolaylıkla yapılabilmektedir.  

Anahtar Kelimeler

Pekiştirmeli öğrenme, mikroskobik trafik simülasyonu, kritik aralık kabulu, Paramics, dönel kavşak

Simulation of Vehicles’ Gap Acceptance Decisions Using Reinforcement Learning

Öz

This paper presents the use of reinforcement learning
approach for modeling vehicles' gap acceptance decisions at a stop-controlled
intersection. The proposed formulation translates a simple gap acceptance
decision into a reinforcement learning problem, assuming that drivers' ultimate
objective in a traffic network is to optimize wait-time and safety. Using an
off-the-shelf simulation tool, drivers are simulated without any notion of the
outcome of their decisions. From multiple episodes of gap acceptance decisions,
they learn from the outcome of their actions, i.e., wait-time and safety. A
real-world traffic circle simulation network developed in Paramics simulation
software is used to conduct experimental analyses. The results show that drivers'
gap acceptance behavior in microscopic traffic simulation models can easily be
validated with a high level of accuracy using Q-learning reinforcement-learning
algorithm.

 

Anahtar Kelimeler

Reinforcement learning, traffic simulation, gap acceptance, Paramics, traffic circle

Kaynakça

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