Modeling Urban Traffic by Means of Traffic Density Data: Istanbul Case
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The main goal of the proposed research is to perform a predictive modeling study on Istanbul’s traffic congestion estimation by using traffic density data. Istanbul Metropolitan Municipality (IMM) shared the data, which includes traffic density status according to 336 different routes in Istanbul, at the end of January 2020. Previous studies on the traffic problem in Istanbul have been limited due to a lack of data. Therefore, it is aimed to perform an initial study on predictive modeling for Istanbul’s traffic congestion forecast. As a preliminary result of the analysis, it is seen that the traffic density is low at 93% accuracy for all locations between 00:00–07:00 am. When the locations are examined for other hours, it is seen that there was no traffic density at some locations. In the planned study, intelligent modeling techniques will be performed with identifying out-of-routine situations in traffic flow. Advantages and disadvantages of predicted models will be discussed according to performance indicators such as RMSE and MAPE. The superior model will be selected according to these criteria and it is expected that preferred approach would be a starting point on future research for predictive forecast studies of Istanbul’s traffic congestion. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
Açıklama
International Conference on Intelligent and Fuzzy Systems, INFUS 2020 -- 21 July 2020 through 23 July 2020 -- -- 242349
Anahtar Kelimeler
Traffic density, Traffic forecasting, Urban traffic
Kaynak
Advances in Intelligent Systems and Computing
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
1197 AISC
