###### Abstract
ANNs, genetic algorithms, simulated annealing, fuzzy logic models, and ant colony optimizers are some examples of AI technologies that support transportation that is deemed innovative, disruptive, and emerging by the World Economic Forum. These technologies are used to address problems with transportation management such as congestion, guaranteeing that journey times are reasonable for passengers, and boosting the efficiency of the entire transportation network [21]. In addition, AI helps to resolve some of the problems affecting the transportation industry's sub-systems, including traffic management, public transportation, safety management, manufacturing, and logistics [22]. AI growth has resulted in enormous models being used to meet the daily needs of mankind, including safe ITS [23]. However, AI models are challenged by the need for transparency, \"simulability\", fidelity, and compactness, despite their benefits. The drawback is a result of the systems \"black-box\" design, which enables accurate decision-making but leaves out the justification for the decision being made. This then led to the development of \"explainable AI\" (XAI), a revolutionary technology. This novel idea enhances the dependability and openness of AI-based systems [24]. The concept of XAI was put up as a means of increasing AI's transparency and fostering its acceptability in some important industries [25]. XAI reduces the complexity of AI while increasing the application of AI systems in sectors such as security, healthcare, and transportation [26]. Consequently, several research works have been published on XAI. One common challenge of the plethora of publications on XAI is the difficulty to situate growth in XAI with specific sector unique demands. To address this, several review papers on XAI have been published. However, a general review of XAI still leaves stakeholders with the problem to grapple with their sector-specific needs. Consequently, in this review, we have articulated all XAI models and how they have addressed the unique demand of securing ITS. Intrusion detection is the process of monitoring and analyzing events in a system or network [27] for indications of potential events, which are breaches or vulnerabilities of security regulations, and acceptable use of standard security practices [28]. Intrusion detection techniques are usually grouped based on the identified activities and the approach used to identify intrusions. Intrusion detection systems (IDS) can be host-based or network-based. Specification-based, signature-based, and anomaly-based detection are the focal intrusion detection approaches. Aside from event monitoring and assessment, IDS normally collects activity information, alerts of critical occurrences via warnings and alarms, and provides relevant reports [23]. IDS is an essential security tool to safeguard networks against adversarial and non-adversarial attacks from malicious traffic [29]. In recent times, machine learning (ML) has aided the development of IDS research. According to the IoT Analytics [30], in August 2022, Nozomi researchers identified three security holes (CVE-2022-29831, CVE-2022-29832, and CVE-2022-29833) that could allow an attacker to obtain information from Mitsubishi GX Works3 (configuration and programming software for certain Mitsubishi PLCs) project files to compromise connected safety CPU modules. [PERSON] et al. [31] also revealed that researchers found 14 vulnerabilities in the infotainment system of several BMW series. A critical part of modern ITSs is the possibility of interconnected vehicles, known as vehicle-to-vehicle (V2V) connection, IoV in general, the connection of vehicles to other road infrastructures known as (V2I), the connection of vehicles to other \"things\" known as (V2X), or simply ICVs [1]. The prevailing challenge of connected things is vulnerability to attacks and falsification of data as they get transmitted in the ICVs [32; 33]. It is thus critical to detect and mitigate against these attacks in ICVs [2; 31].