A NEURAL NETWORK BASED TRAFFIC-FLOW PREDICTION MODEL

Prediction of traffic-flow in Istanbul has been a great concern for planners of the city. Istanbul as being one of the most crowded cities in the Europe has a rural population of more than 10 million. The related transportation agencies ill Istanbul continuously collect data through many ways thanks to improvements in sensor technology and communication systems which allow to more closely monitor the condition of the city transportation system. Since monitoring alone cannot improve the safety or efficiency of the system, those agencies actively inform the drivers continuously through various media including television broadcasts, internet, and electronic display boards on many locations on the roads. Currently, the human expertise is employed to judge traffic-flow on the roads to inform the public. There is no reliance on past data and human experts give opinions only on the present condition without much idea on what will be the likely events in the next hours. Historical events such as school-timings, holidays and other periodic events cannot be utilized for judging the future traffic-flows. This paper makes a preliminary attempt to change scenario by using artificial neural networks (ANNs) to model the past historical data. It aims at the prediction of the traffic volume based on the historical data in each major junction in the city. ANNs have given very encouraging results with the suggested approach explained in the paper

A neural network based traffic-flow prediction model

Prediction of traffic-flow in Istanbul has been a great concern for planners of the city. Istanbul as being one of the most crowded cities in the Europe has a rural population of more than 10 million. The related transportation agencies in Istanbul continuously collect data through many ways thanks to improvements in sensor technology and communication systems which allow to more closely monitor the condition of the city transportation system. Since monitoring alone cannot improve the safety or efficiency of the system, those agencies actively inform the drivers continuously through various media including television broadcasts, internet, and electronic display boards on many locations on the roads. Currently, the human expertise is employed to judge traffic-flow on the roads to inform the public. There is no reliance on past data and human experts give opinions only on the present condition without much idea on what will be the likely events in the next hours. Historical events such as school-timings, holidays and other periodic events cannot be utilized for judging the future traffic-flows. This paper makes a preliminary attempt to change scenario by using artificial neural networks (ANNs) to model the past historical data. It aims at the prediction of the traffic volume based on the historical data in each major junction in the city. ANNs have given very encouraging results with the suggested approach explained in the paper. © Association for Scientific Research

The Effect of ICT Qualification on Selection of Long Term Suppliers

Enterprises working in the field of Industry 4.0 or high technology should review the criteria they use when choosing the companies in the supply chain. Even in crisis situations like Covid-19, decision-making criteria should be revised to maintain the functionality of the supply chain. In this study, it has been shown that the importance of ICT competence in companies in the supply chain should be taken into consideration by the parent company and that the selection of firms will change by including it in multi-criteria decision-making processes. In addition to the classic criteria used in the current supply chain evaluation, ICT criteria and sub-criteria have taken into consideration. ICT competencies of companies selected as candidates for supply chain evaluation are at different levels. The level of ICT preferred by the decision-making parent firm is decisive and it is possible to express it analytically with multi-criteria decision-making methods. The parent company needs an agile information and communication network between itself and the supply chain to easily adapt to technological developments and not to be affected by a crisis. Considering the ICT knowledge, skills and competence of the companies to be included in the supply chain, changes the supply chain ranking prepared according to classical criteria

A NEURAL NETWORK BASED TRAFFIC-FLOW PREDICTION MODEL

Prediction of traffic-flow in Istanbul has been a great concern for planners of the city. Istanbul as being one of the most crowded cities in the Europe has a rural population of more than 10 million. The related transportation agencies ill Istanbul continuously collect data through many ways thanks to improvements in sensor technology and communication systems which allow to more closely monitor the condition of the city transportation system. Since monitoring alone cannot improve the safety or efficiency of the system, those agencies actively inform the drivers continuously through various media including television broadcasts, internet, and electronic display boards on many locations on the roads. Currently, the human expertise is employed to judge traffic-flow on the roads to inform the public. There is no reliance on past data and human experts give opinions only on the present condition without much idea on what will be the likely events in the next hours. Historical events such as school-timings, holidays and other periodic events cannot be utilized for judging the future traffic-flows. This paper makes a preliminary attempt to change scenario by using artificial neural networks (ANNs) to model the past historical data. It aims at the prediction of the traffic volume based on the historical data in each major junction in the city. ANNs have given very encouraging results with the suggested approach explained in the paper

AUGER FD: Detector response to simulated showers and real event topologies

The performance of the Auger Fluorescence telescope is discussed on the basis of a mass production chain. In order to get a realistic estimate of the detector resolution, a large number of fully simulated CORSIKA showers have been used for this study. The propagation through the atmosphere and the detector response are taken into account and simulated in detail. Results for the the case of monocular reconstruction are presented here. No quality cuts for the event reconstruction have been applied so far. Finally, a schematic overview of the expected event topologies is given together with the display of a real event recently collected