A Deep Learning Approach for Traffic Flow Prediction in City of Sarajevo

被引：0

作者：

Kamenjasevic, Nedim ^{[1
]}

Eljazovic, Maida ^{[2
]}

Sarajlic, Mirzet ^{[3
]}

机构：

[1] Cantonal Adm Inspect Issues Canton Sarajevo, Sarajevo 71000, Bosnia & Herceg

[2] Nelt Ltd, Sarajevo 71000, Bosnia & Herceg

[3] Univ Sarajevo, Fac Traff & Commun, Sarajevo 7100, Bosnia & Herceg

来源：

NEW TECHNOLOGIES, DEVELOPMENT AND APPLICATION VII, VOL 2, NT-2024 | 2024年 / 1070卷

关键词：

traffic jams; traffic congestions; traffic flow; deep learning;

D O I：

10.1007/978-3-031-66271-3_21

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Constant traffic jams and congestion in the City of Sarajevo reduce efficiency of road infrastructure and increase travel time and air pollution. Achieving a satisfactory level of road network service, which will lead to greater satisfaction of traffic participants, as well as all citizens, requires short-term planning of traffic flows in the City of Sarajevo. Deep learning techniques can be used with technological progress to collect information from real time and to predict future traffic flow in the City of Sarajevo.

引用

页码：191 / 197

页数：7

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