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Dynamic All-Red Signal Control Based on Deep Neural Network Considering Red Light Runner Characteristics

Title
Dynamic All-Red Signal Control Based on Deep Neural Network Considering Red Light Runner Characteristics
Authors
Kwon, Seong KyungJung, HojinKim, Kyoung-Dae
DGIST Authors
Kwon, Seong Kyung; Jung, Hojin; Kim, Kyoung-Dae
Issue Date
2020-09
Citation
Applied Sciences, 10(17), 6050
Type
Article
Article Type
Article
Author Keywords
Intelligent Transportation System (ITS)deep neural networkRed Light Runner (RLR)dynamic signal controlintersection safety
Keywords
DRIVING BEHAVIORPREDICTIONVEHICLEMODELS
ISSN
2076-3417
Abstract
Despite recent advances in technologies for intelligent transportation systems, the safety of intersection traffic is still threatened by traffic signal violation, called the Red Light Runner (RLR). The conventional approach to ensure the intersection safety under the threat of an RLR is to extend the length of the all-red signal when an RLR is detected. Therefore, the selection of all-red signal length is an important factor for intersection safety as well as traffic efficiency. In this paper, for better safety and efficiency of intersection traffic, we propose a framework for dynamic all-red signal control that adjusts the length of all-red signal time according to the driving characteristics of the detected RLR. In this work, we define RLRs into four different classes based on the clustering results using the Dynamic Time Wrapping (DTW) and the Hierarchical Clustering Analysis (HCA). The proposed system uses a Multi-Channel Deep Convolutional Neural Network (MC-DCNN) for online detection of RLR and also classification of RLR class. For dynamic all-red signal control, the proposed system uses a multi-level regression model to estimate the necessary all-red signal extension time more accurately and hence improves the overall intersection traffic safety as well as efficiency. © 2020 by the authors.
URI
http://hdl.handle.net/20.500.11750/12651
DOI
10.3390/app10176050
Publisher
MDPI AG
Related Researcher
Files:
Collection:
Department of Information and Communication EngineeringARC Lab1. Journal Articles


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