## 2 Overview of Artificial Intelligence and Machine Learning in Road Transport Systems
### Road Management Using Artificial Intelligence/Machine Learning  
The dynamic nature of vehicle networks and their underlying heterogeneous structure introduce additional requirements for networking algorithms that deal with network control and resource allocation. While network control works with the handover, routing, and offloading of network traffic, network resource allocation deals with the spectrum, transmission power, and computer resources. Traditional approaches cannot detect the underlying patterns in in-vehicle networks, but the use of ML methods in this field of vehicular networking is coupled with several enabling technologies [38]. Developing a message queue telemetry-based traffic congestion monitoring system is suggested for looking at traffic congestion trends (MQTT). The benefits of an infrastructure-based approach and the MQTT techniques are combined in the proposed system, and the adaptability to support the integration of various types of sensors for traffic flow observation in low-bandwidth and high-latency vehicular networks environments is offered [37]. A country's ability to maintain economic growth and development is greatly influenced by its transportation infrastructure. People wish to travel to more and more destinations in a shorter time due to their hectic lifestyles and limited free time [39]. Relevance analysis would be programmed into the software, showing the sample picture of road traffic management artificial intelligence and machine learning, which will be executed if the database does not contain any similar circumstances [40]. Artificial Intelligence and Machine Learning and Deep Leaning Approaches in Automated Incident Detection  
Artificial intelligence is an intelligent machine that emulates or imitates how a human behaves; it encompasses learning concepts and obtains an understanding of those concepts and uses them to engage humans in dialogue. AI contains machine learning and deep learning. Machine learning is a subset of artificial intelligence; it allows data to be fed into the machine for it to learn, and in [41], it is postulated that machine learning offers a flexible network architecture and large data and path loss forecasts. Radial basis function (RBF) and support vector regression (SVR) models provide path loss estimates in the examined settings. Multiple input parameters might be processed by the SVR model without complicating the network design. Deep learning is a bigger part that represents data and is focused on the neural network; it is also a computing system that allows the system to identify objects. There are several approaches to detecting road traffic accidents according to [42]. Multiple sub-sections (links) of the target corridor are separated by the same unit of distance. Consider comparing the journey times of a probe vehicle at two successive links. The travel time of the link is longer than that of the downstream link if there is congestion in the upstream link. The bottleneck is located using both absolute and relative disparities in the probe vehicle's connection trip timings. To detect foreground regions in the videos with a semi-stationary background, the technique of adaptive non-parametric kernel density estimation (AKDE) is identified as the baseline system. AKDE addresses both scene independence and the multi-modality of the backdrop. The advantages of AKDE are caused by the fact that individual pixels can have distinct and adaptive thresholds, and thus, a global threshold is not necessary for the pixels in a video scene. The system can consistently operate with varied video scenarios without changing settings [43]. Another detecting device is the installed automatic device, such as CCTVs that run continuously and alert action when necessary. In [44], the Haar-like algorithm and the AdaBoost algorithm are discussed. The primary function of the Haar-like algorithm is to extract details about certain object features, such as edges, contours, and intensity gradients.