Intelligent algorithms for incident detection and management in smart transportation systems

Abstract

Prior research on traffic event detection has encountered two problems: limited sample numbers and unbalanced datasets. Moreover, the real-time properties of event detection models must be enhanced to meet traffic management demands. To solve these issues, suitable measures must be implemented, like developing an intelligent algorithm for incident detection employing Artificial Intelligence (AI) and Machine Learning (ML). Automated Incident Detection (AID) methods are the focus of current Intelligent Transportation System (ITS) technology. Modern vehicles can connect with one other and with Roadside Infrastructure Units (RSUs) to improve road safety thanks to advancements in wireless connectivity and sensor technology. Deep Learning (DL)-based methods have recently demonstrated strong performance in computer vision issues involving complicated feature associations. This work proposes a Hybrid Deep Learning-based Automated Incident Detection and Management (HDL-AIDM) system to identify traffic incidents and improve traffic management. In the suggested model, a Temporal and Spatial Stacked Autoencoder (TSSAE) is used to collect temporal and spatial associations of traffic conditions and identify events. At the same time, a generative adversarial network (GAN) is employed to improve the number of samples and equalize datasets. The proposed model is assessed from many perspectives using the dataset from real-world situations. Based on the HDL output for AID, an efficient and intelligent traffic management algorithm has been accomplished using Road Side Units (RSUs) to gather traffic information. The suggested incident management algorithm considers lane shifts and the fluctuation in vehicle speed over time, which are heavily influenced by traffic incidents. These developments in ITS enable traffic management systems to use information gathered from HDL-based AID methods using TSSAE and GAN. The proposed strategies have been devised to notify drivers about traffic issues and help them avoid congestion. The suggested method provides higher incident detection rates with an accuracy of 94.1%, a 3.9% false alarm rate, and an incident classification rate of 93.3%.