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Urban parking management is a growing challenge with increasing vehicle numbers and limited parking space. Traditional methods often fail during peak hours, leading to inefficiencies, unauthorized usage, and revenue losses. For instance, a parking lot designed for 300 vehicles often exceeds 90% occupancy during peak times, creating congestion and billing inaccuracies. This research proposes an automated system integrating sensors, image processing, and database management to address these issues. A single camera monitors multiple parking slots, with predefined coordinates linked to IR sensors for dual verification. Image processing algorithms, including Optical Character Recognition (OCR), enable accurate license plate recognition. Testing under real-world conditions showed 95% accuracy in daylight, 90% in low light, and 93% for plates at 45-degree angles. Detection accuracy reached 88% at distances of 1.5–3 m, ensuring reliable operation even at the camera’s range limits. Occupancy tracking achieved less than a 5% error margin compared to manual methods, while the fare calculation module reduced billing errors by 90%, enhancing efficiency and revenue. The system’s scalable design supports applications in parking management, toll collection, and traffic monitoring. By improving vehicle detection, occupancy tracking, and billing accuracy, this solution addresses critical challenges in urban parking and contributes to smarter city infrastructure.

Kota Kinabalu (KK) is one of the pilot cities to implement smart city infrastructure in Malaysia. Given this privilege, lots of apps are needed to enable the city folks taking advantage of the highspeed bandwidth infrastructure. One of it is moving towards smart environment and smart government through supporting the green environment and cashless payment system for parking purposes. This paper describes the effort on the development of an apps, JumPark Bah! as an alternative to the current coupon-based parking system handled by the local authorities, Kota Kinabalu City Hall (KKCH). The current approach is quite cumbersome for KK city folks as they need to have a valid parking coupon displayed on the dashboard to park their car around KK city area. They must be able to correctly estimate the parking time or else summon will be issued if the parking time is exceeded. As for the KKCH, they need to assign many parking attendants for checking and validating the parking session. Thus, JumPark Bah! has been proposed as an alternative solution for KKCH by utilizing the concept of cashless payment parking system and is able to minimize the above-mentioned issues. The apps consist of a mobile application (for citizen and parking attendant) and the web-based system (for admin, i.e., KKCH). The web-based system allows the authority to monitor the fees, generate a report and even updates the user through announcement and news functionality. Based on the user evaluation using System Usability Scale (SUS), it has been found that JumPark Bah! has achieved 71.06% score which exceed the 68% score for good usability target.

In developing cities, newly emerging cities have started facing the problem of insufficient public parking facilities and ineffective regulations. To support the planning, design and management of the public parking system towards a smart and sustainable city vision, it is necessary to study deeply parking behaviors. This paper presents an empirical study on parking-choice behaviors of motorcycle users and car users in the emerging cities of developing countries through a case study of three cities in Binh Duong, Vietnam. To explore the behavioral mechanisms and influential factors, the multinomial logit parking choice models are developed using revealed preference and stated preference data. The users’ overall satisfaction and perceived importance of parking lot design and service aspects are analyzed using order logistic regression. The revealed choices show no trade-off between parking fee and walking distance, as the users are not fully aware of parking locations and service features. However, the stated choice experiments prove a potential existence of the trade-off mechanism and differentiate significant factors in the decision of choices for the two user groups. The results bring insightful implications for the development of a smart public parking system.

Commercial fleet management and operations pose distinct challenges compared to regular passenger vehicles. These challenges stem from the varying sizes, shapes, and parking demands of commercial vehicles, requiring specific curbside accommodations. Despite extensive research on smart-parking management for personal vehicles, there has been limited focus on improving parking outcomes for urban freight systems. To address this gap, we have developed a framework that utilizes sensors installed in parking areas to collect occupancy information. This framework predicts parking space availability for commercial vehicles in 10-minute intervals. The current states and the predictions are relayed to the drivers in near real-time through a web-based interface, empowering them to find suitable parking spaces and reducing search time. Our framework incorporates a suite of machine-learning models for predicting curbside parking availability based on real-time sensor data from commercial vehicle loading zones. We evaluated these models in a busy commercial district in the Seattle area, focusing on prediction accuracy and real-world performance. Our study concludes that, for practical use, the convolutional neural network (CNN) model outperforms other architectures, including Spatial Temporal Graph Convolutional Networks (ST-GCN) and Transformer.

Smart cities can be complemented by fusing various components and incorporating recent emerging technologies. IoT communications are crucial to smart city operations, which are designed to support the concept of a “Smart City” by utilising the most cutting-edge communication technologies to enhance city administration and resident services. Smart cities have been outfitted with numerous IoT-based gadgets; the Internet of Things is a modular method to integrate various sensors with all ICT technologies. This paper provides an overview of smart cities’ concepts, characteristics, and applications. We thoroughly investigate smart city applications, challenges, and possibilities with solutions in recent technological trends and perspectives, such as machine learning and blockchain. We discuss cloud and fog IoT ecosystems in the in capacity of IoT devices, architectures, and machine learning approaches. In addition we integrate security and privacy aspects, including blockchain applications, towards more trustworthy and resilient smart cities. We also highlight the concepts, characteristics, and applications of smart cities and provide a conceptual model of the smart city mega-events framework. Finally, we outline the impact of recent emerging technologies’ implications on challenges, applications, and solutions for futuristic smart cities.

The vehicle drivers pose a huge problem in determining an optimal parking space as the density of vehicles in big cities have rapidly increased over the recent years. This objective of drivers towards the identification of parking availability causes traffic congestion, time wastage and air toxicity. At this juncture, the smart parking systems enable the drivers to reserve parking spaces and achieve real time parking information. But, most of state-of-the art smart parking solutions call for requiring drivers to disclose potentially sensitive information, such as their intended destination. In addition, the chances of single point failure are maximized as the available due to their total centralization smart parking solutions are quite susceptible to privacy invasions by the service providers. In this paper, Efficiency of a Smart Parking System in Privacy-Preserving using Multi Transaction Mode Consortium blockchain. This private information retrieval scheme is proposed with the benefits of enhanced multi-transaction mode consortium blockchain which is built by various parking lot proprietors for maximizing parking offers through the inclusive factors of accessibility, openness, and security. It is proposed to covertly retrieve parking offers from the improved multi-transaction mode consortium blockchain in order to protect drivers’ location privacy. It also included the merits of light-weighted quantum blind signature for guaranteeing the drivers with a significant anonymous authentication process that aids in determining the feasibility and available parking slot reservation. The results confirmed the predominance of the proposed private information retrieval scheme with respect to the maximized privacy preservation of drivers’ sensitive information with minimized communication and computation overheads.

The Internet of Things (IoT) is rapidly becoming one of the most talked-about and essential components of any digitization process. The IoT is comprised of several key necessary components, the most important of which are sensors, communication (the internet), and user interfaces for data processing. IoTs are currently finding applications in virtually every industry, including healthcare, where they are known as the internet of medical things (IoMT), industry, where they are known as the industrial internet of things (IIoT), and interconnection between people, where they are known as the internet of everything (IoE). The challenge is to leverage the Internet of Things (IoT), technology, and data to create smarter and more sustainable cities that enhance the quality of life for residents. Therefore, in this article; we have demonstrated the use of the IoT in a variety of applications for smart communities. These applications include smart transportation, smart water management, smart garbage management, smart house illumination, smart parking, smart infrastructure, etc. This research also includes an explanation of the flow process of implementing the IoT in different applications of smart communities, as well as their characteristics and particular applications. Along with their flow illustration, the stages involved in the implementation of smart city applications and the components they consist of are also displayed here. We have also taken into consideration the instances of particular cases and their implementation utilizing IoT. Some of these cases include the automated water collection methods of smart water management systems as well as the condition of the water. Based on the findings of the research, we came to the conclusion that IoT devices play an essential role in each and every one of the smart city project implementations.

Smart cities are designed to improve the quality of life by efficiently using resources and smart parking is an important part of this puzzle to help alleviate traffic congestion and efficiently address energy consumption and search time for parking spaces. However, existing parking management systems have issues with resource management, system scalability, and real-time dynamic changes. In response to these challenges, this paper proposes a Multi-Objective Optimization Framework for Smart Parking incorporating Digital Twin Technology, Pareto Front Optimization, Markov Decision Process (MDP), and Particle Swarm Optimization (PSO). Hence, the proposed framework utilizes Digital Twin whereby there is a generation of a virtual model of the existing parking infrastructure that can give a real-time prospective estimation of the entire system. The Pareto Front is then used for multi-objective optimization of the search domain, where the goal is to minimize the search time, use of energy, and traffic disruption, and maximize the availability of parking spaces. The MDP splits the resource allocation problem into a value function which can then model the real-time parking requests. Further, PSO refines the solutions found from the Pareto front for a globally superior distribution. The framework is evaluated using extensive simulations across multiple metrics: search time, energy, congestion level, scalability, and utilization. Evaluation outcomes also show that the proposed algorithm is better than Round Robin, Random Allocation, and Threshold Based algorithms in terms of 25% improvement in the search time, 18% better energy usage, and 30% less traffic congestion. This work has shown the prospects of combining hybrid optimization and real-time decision-making in the enhancement of parking management in smart cities for better efficiency in urban mobility.

The better management of resources and the potential improvement in traffic congestion via reducing the orbiting time for parking spaces is crucial in a smart city, particularly those with an uneven correlation between the increase in vehicles and infrastructure. This paper proposes and analyses a novel green IoT-based Pay-As-You-Go (PAYG) smart parking system by utilizing unused garage parking spaces. The article also presents an intelligent system that offers the most favorable prices to its users by matching private garages’ pricing portfolio with a garage’s current demand. Malta, the world’s fourth-most densely populated country, is considered as a case of a smart city for the implementation of the proposed approach. The results obtained confirm that apart from having a high potential system in such countries, the pricing generated correctly forecasts the demand for a particular garage at a specific time of the day and year. The proposed PAYG smart parking system can effectively contribute to the macro solution to traffic congestion by encouraging potential users to use the system’s services and reducing the orbiting time for parking.