ARIMA VE SİNİR AĞLARINI KULLANARAK PARK YERİ UYGUNLUĞUNUN TAHMİNİ

Year 2023, Volume: 34 Issue: 1, 86 - 108, 27.04.2023

Aslı Sebatlı Sağlam , Fatih Çavdur

https://doi.org/10.46465/endustrimuhendisligi.1241453

Abstract

Sürücülerin park yerlerinin doluluk oranları hakkında bilgi sahibi olmaları, park yeri bulmak için yapılan yolculuklardan kaynaklanan trafik yoğunluğunun azaltılması açısından kritik olabilir. Bu çalışmada, üç farklı teknik kullanarak park doluluk oranları (ve dolayısıyla yer uygunluğu) tahmin edilmektedir: (i) otoregresif entegre hareketli ortalama modeli, (ii) mevsimsel otoregresif entegre hareketli ortalama modeli ve (iii) sinir ağları. Uygulama aşamasında, San Francisco'da gerçekleştirilen “SFpark” projesinin yol üstü park yerlerinin veri seti kullanılmıştır. Park yerlerinin yalnızca geçmiş doluluk oranları değil, bu doluluk oranlarını etkileyen gün tipi ve saat dilimi olmak üzere dışsal değişkenler de dikkate alınmıştır. Veri setindeki farklı park doluluk paternlerine sahip her bir park yeri için ele alınan yöntemlerin her birinin farklı model yapıları ile tahminler yapılmış ve ardından her bir park yeri için en iyi model tasarımını bulmak için sonuçlar karşılaştırılmıştır. Buna ek olarak, yöntemlerin birbirine olan üstünlüğü açısından da sonuçlar değerlendirilmiş ve ortalama karesel hatalar cinsinden sinir ağlarının performansının diğer yaklaşımlardan daha iyi olduğu gözlenmiştir.

Keywords

Park tahmini, Zaman serisi tahmini, ARIMA, SARIMA, Sinir ağları

PREDICTION OF PARKING SPACE AVAILABILITY USING ARIMA AND NEURAL NETWORKS

Abstract

It may be critical for drivers to have information about the occupancy rates of the parking spaces around their destination in order to reduce the traffic density, a non-negligible part of which caused by the trips to find an available parking space. In this study, we predict parking occupancy rates (and thus, space availability) using three different techniques: (i) auto-regressive integrated moving average model, (ii) seasonal auto-regressive integrated moving average model and (iii) neural networks. In the implementation phase, we use the data set of the on-street parking spaces of the well-known “SFpark” project carried out in San Francisco. We take into account not only the past occupancy rates of parking spaces, but also exogenous variables that affect the corresponding occupancy rates as day type and time period of the day. We make predictions with different model structures of each of the considered methods for each parking space with different parking occupancy patterns in the data set and then compare the results to find the best model design for each parking space. We also, evaluate the results in terms of the superiority of the methods over each other and note that the performance of neural networks is better than those of the other approaches in terms of the mean squared errors.

Keywords

Parking prediction, Time series forecasting, ARIMA, SARIMA, Neural networks

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