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

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.

Trustworthiness in Mobile Cyber-Physical Systems

“…By contrast, the study conducted by Oh et al [10] concerns real-time data transmission to mobile equipment used by groups of workers, termed a mobile sink group (MSG), for which rapid and reliable data are vital to ensure the efficient operation of groups working on collaborative projects, which often involve multiple pieces of equipment where miscommunication could result in an industrial accident. The authors proposed a real-time data delivery mechanism based on a virtual grid structure to support MSGs.…”