With rapid urbanization worldwide, cities are under increased pressure on infrastructure, particularly in public transportation. An efficient public transportation system is critical for sustainable urban growth, reducing congestion, emissions, and commute times [1]. The smart city model leverages Information and Communication Technologies (ICT) to enhance urban services' quality, responsiveness, and efficiency. Cloud computing and Internet of Things (IoT) would become significant technologies in this context since both allow flexible and scalable approaches toward a real-time analytical assessment, operational efficiency, and a user-centered approach to design.

Scalable cloud-based resources for storage, processing, and analytics for real-time monitoring and management of transportation networks. To sum up, centralized data and computation of central resources help cloud platforms accomplish key functions, including predictive maintenance, route optimization, and demand forecasting, among others. Meanwhile, the IoT connects wide-scale networks of physical devices, that is, forms such as sensors, Global Positioning Systems (GPS) modules, and Radiofrequency Identification Systems (RFID) systems that are implanted in vehicles and infrastructures.

These IoT devices continuously gather data about locations, loads of passengers, environmental conditions, and patterns in traffic, processing and analyzing it in the cloud to make informed decisions in a data-centric fashion. Integrating cloud and IoT technologies into a cohesive architecture will benefit public transportation. Such integration will ensure that cities can create responsive and adaptive transportation systems capable of adjusting dynamically to demands within cities. Passengers will have real-time information about when vehicles are due to arrive, congested routes, and service outages. Transportation authorities can make operations streamlined and cost-efficient and improve resource allocation. This paper discusses a cloud-IoT architecture designed for smart city public transportation systems. The architecture takes a layered approach to collecting, transmitting, and processing data, which enhances system reliability, scalability, and efficiency. Below is a table summarizing the core components of a cloud-IoT-based transportation system and their respective roles.

Using this architecture would allow the city to construct an intelligent and scale-able transport system that can adequately respond to existing and urgent city mobility and lay a base for futuristic progress. With cloud and IoT convergence for public transit, a viable adaptive mode of urban transportation toward future sustainable cities takes place within this city and creates even more robust, resilient infrastructure as part of cities.

The concept of smart cities has gained much momentum as cities around the globe are dealing with increasing complexities, such as congestion and pollution, and the need to manage resources efficiently. The two key technologies that emerged and are central to smart city applications are cloud computing and the IoT. This review discusses seminal studies on cloud-IoT integration in smart city frameworks, research gaps, and the challenges in smart public transportation systems [2].

Infrastructure to 2030 telecom has proven that the changing geography of infrastructure and the growing internationalization of the economy and its impact on infrastructures where the challenges arise first of all from the fact that demand patterns for infrastructure services will change between and across countries as well as within countries. At the same time, the nature of infrastructure is likely to change as technology and user requirements evolve. Finally, financing the maintenance of existing infrastructure, deploying new ones, and managing change holistically across separate albeit interrelated facilities will raise challenges of their own [19].

In 2002, Kirikova et al. [20] reduced the challenge by implying a need for knowledge development in these areas. Still, most of all, there is a need for people who can integrate knowledge from these areas. There are specialists in GIS Geographic information systems, transport modeling, and information systems development, but few people can integrate knowledge from different areas. This is a major problem because knowledge and systems integration will be a key factor in developing ITS, and he ignored some effective criteria, for example, IBM Institute for business value is based on several factors, including the city's stage of development, physical characteristics, existing levels of transport infrastructure, and citizen preferences. For example, Amsterdam and Chicago are both mature cities but have very different characteristics that will shape their transport ambitions: in Amsterdam, over 50 percent of daily trips are on foot or by bicycle, whereas in Chicago, just under 90 percent are by private car [19].

On the other hand, Dr. Tom V. Mathew summarizes the problem and solution ITS in a table that compares the problem, possible solution, and approach of ITS.

In the same way, Dr. Tom V. Mathew is interested in user services and their requirements, where several functions are needed to accomplish the user services. These functional statements are called user services requirements. The requirements for all the user services have been specified. If any new function is added, new requirements are to be defined. Table 2 illustrates user service requirements for Traffic Control (TC) user service. TC provides the capability to manage the movement of traffic on streets and highways efficiently. These functions are provided as follows:

for Smart Public Transportation Systems

Creating a cloud-IoT architecture for smart public transportation holds great promise but comes with various technical and operational hurdles. The following key solutions tackle the main challenges in implementing this architecture to achieve security, scalability, efficiency, and reliability.

Integrating cloud and IoT technologies in public transportation systems presents an innovative approach to tackling urban mobility challenges. This paper introduces a cloud-IoT architecture designed to enhance public transit's quality, efficiency, and sustainability in smart cities. By utilizing cloud computing, public transit networks can centralize data processing and storage, enabling scalable analytics, predictive maintenance, and real-time decision-making. Concurrently, IoT devices installed in vehicles and infrastructure collect and transmit vast amounts of data, which aids in optimizing traffic flow, facilitating real-time monitoring, and improving passenger experiences. Implementing these systems comes with challenges, including data security, interoperability, real-time processing, and maintenance. Solutions such as end-to-end encryption, open standards for interoperability, scalable cloud platforms, predictive maintenance, and cost-effective models have been proposed to tackle these concerns. Collectively, these strategies ensure that the architecture remains resilient, adaptable, and effective in addressing the needs of growing urban areas. As urbanization continues, cloud-IoT-enabled public transportation systems will play a crucial role in developing sustainable, efficient, and user-centric smart cities. This cloud-IoT architecture provides a solid foundation for immediate improvements in transit efficiency and future advancements in urban mobility. By creating adaptive and intelligent transit systems, cities can enhance the quality of life for residents, reduce environmental impact, and establish a standard for sustainable development in smart city initiatives.

I want to extend my heartfelt thanks to Professor Hitesh Mohapatra at KIIT, who is deemed to be a university student, for his exceptional guidance and support during my research. His deep knowledge of IoT has significantly improved my grasp of the topic, and his encouragement has played a crucial role in completing this work.

I am the sole author of this research and responsible for all aspects of the study.

This research did not receive any funding.

No new data were generated in this research.

The author indicates no conflicts of interest, and no financial supporters were involved in this research.