PREDICTION OF PARKING SPACE AVAILABILITY USING ARIMA AND NEURAL NETWORKS

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Endüstri mühendisli?i dergisi

سال: 2023

ISSN: ['1300-3410', '2667-7539']

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