## 1 Introduction
Instead, semiconductor manufacturers advocate for SoC platforms such as ARM core, iMX [6], and Sitara [7], which are tailored for deployment in public transportation and automotive applications. Public transportation systems may involve various subsystems including IoT components. Notably, fare collection systems represent one of the critical subsystems wherein passengers are charged for transit services. Fare collection methods vary, including Radio Frequency (RF) card, credit card, Quick Response (QR) code, and smart mobile phone payments. For payment security, IoT systems integrate security features such as the Secure Access Module (SAM) card. The design of these payment systems requires specialized structures distinct from those of general IoT systems. The Controller Area Network (CAN) line structure is employed in vehicle management systems, which constitute a subset of public transportation IoT subsystems. While in a general IoT system, data can be sourced from sensors in analog or digital form, in the context of public transportation systems, data are received from sensors via the CAN line. Various sensor data, such as engine speed, fuel level, and vehicle velocity, are transmitted over the CAN line. Administrators use this received data for analysis and display purposes [8]. Light-emitting diode (LED) panels are extensively utilized in IoT systems deployed in public transportation, notably in applications such as passenger information and advertising management. These panels serve to display information regarding upcoming stops and display advertisements to passengers. Specific protocols tailored for public transportation govern the operation of LED panels [9]. The implementation of these protocols differs from that of general IoT systems. IoT-based public transportation systems, exemplified here, exhibit numerous distinctions from general IoT systems. Consequently, the utilization of model-driven IoT development tools (see Votto [10], IoTDraw [11], SoML4 IoT [12] as examples and [13] for an extensive review of their features) designed for general purposes directly in the context of public transportation can be inefficient and incomplete due to the nature of the IoT-based public transportation systems as discussed above. Addressing this gap necessitates research efforts aimed at developing vehicle models specialized for public transportation [3]. Several investigations, albeit in the nascent stage and often confined to case studies, have commenced to address public-transportation-specific challenges [14; 15; 16]. As successfully applied in many other domains, model-driven engineering (MDE), largely supported by the use of domain-specific languages (DSLs) [17; 18] and domain-specific modeling languages (DSMLs) [19; 20], may also help overcome the above-mentioned difficulties of IoT-based public transportation applications and facilitate the system development by leveraging the abstraction level during design and generating many components automatically for the complete implementation, i.e., software models of the public transformation systems can be automatically manipulated and transformed to the executable and deployable artifacts [3]. For this purpose, a modeling language, called DSML4 PT, was recently introduced in [5]. DSML4 PT is the pioneering and currently the sole modeling language that facilitates model-driven engineering (MDE) of IoT-based public transportation systems by considering different modeling perspectives [5]. It also enables the generation of necessary system software for deployment and execution on various IoT-based public transportation devices. However, the usability evaluation of this language, considering the real applications of IoT-based public transportation systems, is lacking. Within this context, the main contributions of our study can be summarized as follows:  
* A modeling language with a complete set of syntax and translational semantics definitions is presented for the MDE of IoT-based public transportation applications.