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Nowadays, the park and its enterprises face many challenges in the global environment that is service-oriented, customer experience centered and customer demand oriented. ICT is becoming a major promoter and partner for smart parks and modern businesses. The smart park takes advantage of the results of data integration and sharing, and provides dec...
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Citations
... The SIPs should integrate internal resources and create a community of interest with information as a link [18,19]. The SIP management platform is considered a solution for the intelligent construction of the SIP [20,21], which can realize information sharing among subsystems and quickly respond to the needs of enterprises in the parks [22][23][24]. The intelligent construction of SIPs is very necessary [12,25,26]. ...
The intelligent development of smart industrial parks (SIPs) can not only promote the development of smart cities, but also promote the development of intelligent large-scale buildings. China is strengthening the construction of SIPs; however, the development of SIPs is limited. Due to different understandings of SIPs, the intelligence level of each SIP varies greatly. It is necessary to develop a SIP intelligence level assessment model to check these limitations. Most of the existing evaluations focus on the qualitative evaluation of the overall intelligence level of SIPs, ignoring the influence of each individual dimension. Therefore, this study used quantitative methods to measure the intelligence level of SIPs from the overall and dimensional levels. The evaluation method included five processes: (1) Classifying the intelligence level of SIPs through expert interviews; (2) Using the literature analysis method to identify various dimensions of the intelligence level; (3) Using literature analysis and expert interviews to determine the evaluation indicators (4) Weighting indicators based on correlation and induced ordered weighted average (IOWA) operator; (5) Using grey clustering analysis to calculate the overall intelligence performance of SIPs and each dimension. Finally, the developed model was verified by Z SIP. The analysis results show that the developed model can measure both overall and dimensional performance of SIPs, and demonstrated that enterprise information services, public information services, SIP security, and energy consumption monitoring platform construction make the greatest contributions to the improvement of the intelligence level. Our research results will help to improve the intelligence level of SIPs, and lay the foundation for the determination of the operating costs of SIPs and the formulation of national standards related to SIPs in the future.
With the developing world, cities have begun to become smarter. Smart parking systems, with the ever-increasing number of vehicles, are among the important matters in smart cities. The reason for this is that the search for parking spaces that are already insufficient, brings along a serious cost, air pollution and stress issues. In this study, a new approach that attempts to forecast the parking lot occupancy rate in the short- and medium-term with its deep learning-based Gated Recurrent Units (GRU) model was proposed. Initially, data belonging to 607 carparks located in the city of Istanbul in Turkey, and weather data have been collected, and a multivariate time series data set has been created. In the second stage, to forecast the parking places that would be available in the short- and medium-term, the GRU model was used in the system proposed. To show the effectiveness of the model, the results obtained through the 27 different models were compared by means of the Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), which were some other sequence models. According to the experimental results made on the weather data obtained from İSPARK dataset and AKOM, the our proposed GRU model achieves 99.11% accuracy gave the best results with 0.90 MAE, 2.35 MSE and 1.53 RMSE metric values. Experimental results obtained with various hyperparameters clearly demonstrate the success of the GRU deep learning model in prediction parking occupancy rates.
