Short-term prediction of parking availability in an open parking lot
نویسندگان
چکیده
Abstract The parking of cars is a globally recognized problem, especially at locations where there high demand for empty spaces. Drivers tend to cruise additional distances while searching spaces during peak hours leading problems, such as pollution, congestion, and driver frustration. Providing short-term predictions availability would facilitate the in making informed decisions planning their arrival be able choose with higher availability. Therefore, aim this study provide available low volume data. open lot provides free charge one lot, located beside shopping center, was selected study. Parking data 21 days were collected 19 used training, multiple periods remaining 2 test evaluate prediction methods. dataset consists from weekday weekend. Based on reviewed literature, three methods suitable selected, namely, long memory (LSTM), seasonal autoregressive integrated moving average exogenous variables (SARIMAX), Ensemble-based method. LSTM method deep learning-based method, SARIMAX regression-based Ensemble based decision trees random forest predictions. performance use visitor trends an variable evaluated. results, provided better times weekday, over However, trend did not improving it improved
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ژورنال
عنوان ژورنال: Journal of intelligent systems
سال: 2022
ISSN: ['2191-026X', '0334-1860']
DOI: https://doi.org/10.1515/jisys-2022-0039