Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2023Scopus SJRQ2/r/munzur/2091
  New Deep Learning-Based Passenger Flow Prediction Model

  Utku, Anıl; Kaya, Sema Kayapinar

  The ability to predict passenger flow in transport networks is an important aspect of public transport management. It helps improve transport services, aids those responsible for management to obtain early warning signals of emergencies and unusual circumstances and, in general, makes cities smarter and safer. This paper develops a long short-term memory-based (LTSM-based) deep learning model to predict short-term transit passenger volume on transport routes in Istanbul. This prediction model has been created using a dataset that included the number of people who used different transit routes in Istanbul at one-hour intervals between January and December 2020. The proposed multilayer LSTM-based deep learning model has been compared with popular models such as random forest (RF), support vector machines, autoregressive integrated moving average, multilayer perceptron, and convolutional neural network. The experimental findings showed that the proposed multilayer LSTM-based deep learning model outperformed the other models with regard to prediction for each transfer route. Furthermore, RF, one of the machine learning models used, produced remarkably successful results.

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  AtıfScopus19·WoS12·OpenAlex16

  İncele
• MakaleYalnız künye2021Scopus SJRQ2/r/munzur/2740
  Smart airport: Evaluation of performance standards and technologies for a smart logistics zone

  Göcxmen, Elifcan

  Owing to the current context of Industry 4.0, the importance of smart technologies in airport systems have increased substantially. State-of-art applications for transportation planning incorporating baggage services, routing, security, and safety are an evolving domain for both practitioners and researchers dealing with aviation applications. In this context, this paper seeks to answer these questions: Which standards are aimed at a smart airport to make the transportation planning sound? Which propositions are made based on the obtained prioritized standards? This study deals with standards including Environmental Effects, Docking & Navigation, Object Detection & Protection, Communications & Integration, and Terminology using a practical decision support system based on an analytical hierarchy process (AHP) and fuzzy inference system (FIS). Computational results reveal that the Object Detection & Protection standard has an effect on a safe and smart system. To give an overview of this standard for a smart logistics zone (SLZ), an architecture of autonomous robot units and a baggage handling system is proposed in this study. The suggested approach analyzes the obstacle photos obtained by cameras and allows the end-user to control the calculations visually. This research could provide advice to airport planners about smart policies and improving operations.

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  AtıfScopus25·WoS10·OpenAlex26

  İncele