Browsing by Keywords "ARIMA"

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    Prediction of parking space availability using ARIMA and Neural Networks
    (TMMOB Makina Mühendisleri Odası, 2023) Sebatlı Sağlam, Aslı; Cavdur, Fatih
    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.
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    Testing the Forecasting Power of Statistical Models for Intercity Rail Passenger Flows in Turkey
    (Sage, 2024-11) Ekici, Üsame; Tüydeş Yaman, Hediye; Şendil, Nuri
    While going through a major rail transformation, it is important to develop reliable estimation models for rail passenger flows (RPFs) in Turkey. There are two main approaches in RPF estimation, regressions and autoregressive integrated moving-average (ARIMA) models, both of which were in this study developed using the daily RPF data for the period 2011–2015. The ARIMA models (with some variations) were used to forecast first the daily flows in 2016, during which travel restrictions for summer resulted in reduced volumes, successfully captured in the updated ARIMA model. The regression models predicted the expected demand during the restrictions, enabling evaluation of the impact of restrictions, which also showed the models’ power over the longer term. The forecasts were extended to 2017, 2018, and 2019 data. The regression results produced more reliable forecasts over the long term, whereas more accurate predictions were obtained by ARIMA-Sliding (FA-Sld) for short-term planning purposes.