Latest revision as of 14:10, 12 February 2021

Abstract

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.

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The different versions of the original document can be found in:

• http://acikerisim.pau.edu.tr:8080/xmlui/handle/11499/12940 under the license https://creativecommons.org/licenses/by

• http://acikerisim.pau.edu.tr:8080/xmlui/handle/11499/20029

• http://acikerisim.pau.edu.tr:8080/xmlui/handle/11499/6246

• http://dx.doi.org/10.3390/mca15020269

• http://www.mdpi.com/2297-8747/15/2/269/pdf under the license http://creativecommons.org/licenses/by/3.0/

• http://www.mdpi.com/2297-8747/15/2/269/pdf,

http://dx.doi.org/10.3390/mca15020269 under the license cc-by

• https://www.mdpi.com/2297-8747/15/2/269,

https://avesis.yildiz.edu.tr/yayin/e917943d-bc89-498a-9ad5-e2055c2a0e79/a-neural-network-based-traffic-flow-prediction-model,

https://www.mdpi.com/2297-8747/15/2/269/pdf,

https://academic.microsoft.com/#/detail/2187559424 under the license https://creativecommons.org/licenses/by/4.0/