Traffic State Estimation Using Speed Profiles and Convolutional Neural Networks (CROSBI ID 694726)
Prilog sa skupa u zborniku | ostalo | međunarodna recenzija
Podaci o odgovornosti
Tišljarić, Leo ; Carić, Tonči ; Erdelić, Tomislav ; Erdelić, Martina
engleski
Traffic State Estimation Using Speed Profiles and Convolutional Neural Networks
Determining the traffic state is one of the most attractive problems for experts in the field of Intelligent Transport Systems (ITS). In this paper, a deep learning model for determining the traffic state is presented. The model is based on Convolutional Neural Networks (CNN) and uses weekly speed profiles as input data. The proposed model consists of an input and output layer with addition to four convolutional layers, two pooling layers, and two fully connected layers that are extracting important features and classifying intersections as congested or not congested. We analyze data and predict traffic state for the most relevant road segments in the City of Zagreb which is the capital and largest city in Croatia. Speed profiles from included road segments are represented as one traffic image and used to train CNN. In that way traffic state for all sequentially connected road segments is estimated. The proposed method achieves a classification accuracy of more than 90% on three analyzed types of road topologies. The results show that CNN trained with traffic images can be used as a tool for traffic state estimation.
Traffic state estimation ; Convolutional neural networks ; Speed profiles ; Intelligent transport systems
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Podaci o prilogu
2147-2152.
2020.
objavljeno
10.23919/MIPRO48935.2020.9245177
Podaci o matičnoj publikaciji
MIPRO 2020 43rd International Convention Proceedings
Skala, Karolj
Rijeka: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO
1847-3938
1847-3946
Podaci o skupu
MIPRO 2020
predavanje
28.09.2020-02.10.2020
Opatija, Hrvatska
Povezanost rada
Informacijske i komunikacijske znanosti, Interdisciplinarne tehničke znanosti, Tehnologija prometa i transport