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Population growth in urban cities has created transport problems such as traffic congestion, pollution, accidents, and infrastructure failures. These problems often lead to disruption of mobility, especially in emerging economies. The goal of any city is to have a resilient transport system that is free from frequent and severe disruptions. Resilience can be incorporated into smart mobility systems to create a robust, integrated and intelligent transport system. Therefore, this study sought to determine resilience factors required for a resilient smart mobility system in emerging economies. The study used interpretive structural modelling (ISM) to establish the causal interrelationships among the factors drawing from data from experts in smart cities, city transport and academia. It was found that the most critical factor to drive resilience is foresight – the ability to anticipate disruptions. Three other resilience factor categories identified were dynamism, certainty and optimisation, which all point to the importance of building dynamic capabilities. The key implication for practice highlights the role that human and artificial intelligence can play in anticipating natural, social and infrastructure disruptions. ISM offered a mechanism to determine the critical causal resilience factors to be prioritised amongst the competing demands on the limited resources of emerging economies. Apart from foresight, which was found to be a driving factor, the other resilience factors are equally strong linkage factors (influence each other), thus further revealing the complexities that decision-makers in urban cities of emerging economies constantly face. A research agenda for resilience in smart mobility in emerging economies was developed. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITS) are globally installed in smart cities, which enable the next generation of ITS depending on the potential integration of autonomous and connected vehicles. Both technologies are being tested widely in various cities across the world. However, these two developing technologies are vital in allowing a fully automatic transportation system; it is necessary to automate other transportation and road components. Unmanned aerial vehicles (UAVs) or drones are utilized for many surveillance applications in the ITS. Detecting on-ground vehicles in drone images is significant for disaster rescue operations, traffic and parking management, and navigating uneven territories. This study presents a flying foxes optimization with deep learning-based vehicle detection and classification model on aerial images (FFODL-VDCAI) technique for ITS application. The main objective of the FFODL-VDCAI technique is to automate and accurately classify vehicles that exist in aerial images. Three primary processes are involved in the presented FFODL-VDCAI technique. Initially, the FFODL-VDCAI approach utilizes YOLO-GD (Ghost-Net and Depthwise convolution) for vehicle detection, where the YOLO-GD uses lightweight Ghost Net in place on the backbone network of YOLO-v4 and interchanges the conventional convolutional with depthwise separable convolutional and pointwise convolutional. Next, the FFO technique is used for hyperparameter tuning the Ghost Net technique. Finally, a deep Q-network (DQN) based reinforcement learning technique is used to classify detected vehicles effectively. A comprehensive simulation analysis of the FFODL-VDCAI methodology is conducted on the UAV image dataset. The performance validation of the FFODL-VDCAI methodology exhibited superior values of 96.15% and 92.03% under PSU and Stanford datasets concerning various aspects. [ABSTRACT FROM AUTHOR]

Due to the rapidly developing technologies, fast and practical solutions are offered to the problems encountered in daily life. Metropolitan cities are greatly affected by the ever-increasing population and migrations to big cities, the increase in production with the economy and job opportunities. At this point, with the introduction of smart transportation systems, fast and effortless solutions can be produced by saving time and space. City life can be facilitated by applying more efficient and rational solutions with smart transportation systems. In this study, it is aimed to investigate information about the Intelligent Transportation Systems and one of its applications, park and ride, which has created a significant agenda within the scope of transportation engineering in the recent past, and to provide information about the investments made by examining the application for Istanbul along with its various applications in the world. Some suggestions will be made by emphasizing the importance of the park and Ride smart city application for Istanbul. In conclusion, predictions of P + R application and energy consumption in periods of 1-24 months were made through machine learning. By obtaining energy consumption data thanks to machine learning, carbon gas emissions and its effects on greenhouse gases were also examined. It can be thought that by obtaining energy consumption data for the long term thanks to machine learning, it can make significant contributions to future investments, green environment-green world, and climate change studies. [ABSTRACT FROM AUTHOR]

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This article examines the operational functionality of intelligent transport systems to enhance smart cities by reducing traffic congestion. Given the increasing populations of smart cities, there is a growing demand for public transit systems to address the issue of traffic congestion. Therefore, the suggested system is developed using a few parametric design models, which combine point-to-point protocol and mode control optimization. The multi-objective parametric design for a smart transportation system is conducted using min–max functions to minimize the waiting time period for end users. Furthermore, customers are given the option to utilize a line following mechanism that offers suitable connectivity, along with independent identification and revitalize functions. The predicted model effectively eliminates the delay produced by transportation devices when positioning units are involved, ensuring that individual messages are delivered without any interruptions. In order to evaluate the results of the proposed system model, four different scenarios were examined. A comparison analysis revealed that the suggested method achieves a suitable directional flow for 96% of smart transport units. Additionally, it reduces delays and waiting periods by 2% and 6% respectively, while increasing energy consumption by 29%. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) promises to revolutionize transportation in smart cities, but its interconnectedness raises critical security and privacy concerns. Limited computational power, diverse network technologies, and many sensors and vehicles challenge data integrity and trust in data exchange. Existing solutions, often dependent on specific environments and protocols, struggle to address these issues across the entire IoV ecosystem. This paper explores the potential of blockchain technology to address these challenges. We argue that blockchain's immutability and decentralization offer a unique solution for trust management in various IoV environments. We review existing blockchain-based algorithms and models proposed for IoV integration and propose a novel taxonomy to categorize these approaches. This taxonomy will help us analyze effective parameters, implementation methods, and evaluation metrics in the reviewed literature. According to our research, the most critical evaluation parameter for blockchain-based methods is time, including system-level service-related time parameters and solution implementation time, and 38% of existing papers simulated the approach using Hyperledger. Additionally, we will identify key challenges from integrating blockchain into the IoV landscape. By providing a comprehensive review and analysis of blockchain-based trust management solutions for IoV, this paper aims to contribute to the ongoing development of secure and reliable intelligent transportation systems. [ABSTRACT FROM AUTHOR]

The emergence of the intelligent city, in the late 2000s, was a worldwide success that has invited itself to the national political agendas of several countries in world association of the major metropolises as well as in the multinational strategy. The success of this global phenomenon is indisputable; however, the smart city remains enigmatic, without precise definition and vague terms. The smart cities projects are characterized by the ability to interconnect urban systems between themselves, including transport system, electricity system, waste system, water system, gas system, etc. and to make each system develop itself; For instance, the field of urban and interurban transport has experienced a spectacular boom over the last few decades mainly with the appearance of vehicular ad hoc networks (VANETs) having promoted the concept of smarts cities mentioned by the birth of intelligent transport systems (ITS). The notion of routing is important and necessary for ITS in order to establish an efficient transmission process in VANETs. A large number of routing protocols exist in VANETs that are classified by different methods and according to determined criteria. This paper proposes a new geographic routing solution based on an intersection entitled A Geographic Routing Based on Local Traffic Density and Multi-hop Intersections in VANETs (GRBLTD-MI) that is more adapted in the context of smart cities. This new approach will discuss the selection of the next intersection when taking into consideration the connectivity of the multi-hop intersection with the use of vehicle's local traffic density, speed, distance and direction. Simulation comparison of GRBLTD-MI against some strong routing protocols has shown the superiority of our approach in terms of packet delivery ratio, end-to-end delay and packet overhead. [ABSTRACT FROM AUTHOR]

VANET technology is an essential component of Intelligent Transportation Systems, which makes c communication between moving cars and stationary Road Side Units more accessible. It allows vehicle nodes to share crucial data among communication devices. VANET has significant potential to enhance traffic efficiency and road safety. This is accomplished by decreasing the chances of collisions between vehicles and reducing the number of accidents. Man-in-the-middle (MITM) attacks are a crucial issue in VANET which needs significant consideration from researchers. To solve the problem of man-in-the-middle attacks, this article presents a dynamic and optimized routing approach for VANET conversation in smart cities by utilizing a chaotic secure multi-verse optimization algorithm. The strategy that has been proposed seeks to achieve the goal of ensuring safe and effective interaction between vehicles participating in VANETs by dynamically determining the optimal path for the exchange of data. A chaotic protect multi-verse optimization approach is used to generate several random sequences from which the most secure route may be selected. This is done to enhance the security of the VANET transmission network during transmission. The results of the trials indicate that the suggested technique is more successful in avoiding MITM and improving the functioning of VANET connections in settings that are characterized by intelligent cities. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) and 5G communication technologies in smart cities deliver promising services for heterogeneous applications. The application reliability banks on uninterrupted and seamless services experienced by the users. However, the increasing smart city application demands influence the experience reliability through augmented wait times. This article therefore introduces a Coherent Reliability Service Broadcasting Technique (CRSBT) for sustaining constructive application services. This technique incorporates linear regressive and digressive learning for application service improvements and restrictions. Based on the demand, the regressive process verifies the wait time and with the reducing demands, the service broadcast ratio is verified. These two factors are verified post the demand and response through 5G resource allocations and IoT computations. Both the service-oriented features are validated for regressive service broadcast and either of the one is used for digressive response. The coherence between the computations (IoT) and resources (5G) is verified on-demand and linearly. Therefore, the proposed technique is reliable in sustaining service broadcast, less wait time, and maximum flexibility. [ABSTRACT FROM AUTHOR]

In this study, we present a novel approach leveraging the segment anything model (SAM) for the efficient detection and tracking of vehicles in urban traffic surveillance systems by utilizing uncalibrated low-resolution highway cameras. This research addresses the critical need for accurate vehicle monitoring in intelligent transportation systems (ITS) and smart city infrastructure. Traditional methods often struggle with the variability and complexity of urban environments, leading to suboptimal performance. Our approach harnesses the power of SAM, an advanced deep learning-based image segmentation algorithm, to significantly enhance the detection accuracy and tracking robustness. Through extensive testing and evaluation on two datasets of 511 highway cameras from Quebec, Canada and NVIDIA AI City Challenge Track 1, our algorithm achieved exceptional performance metrics including a precision of 89.68%, a recall of 97.87%, and an F1-score of 93.60%. These results represent a substantial improvement over existing state-of-the-art methods such as the YOLO version 8 algorithm, single shot detector (SSD), region-based convolutional neural network (RCNN). This advancement not only highlights the potential of SAM in real-time vehicle detection and tracking applications, but also underscores its capability to handle the diverse and dynamic conditions of urban traffic scenes. The implementation of this technology can lead to improved traffic management, reduced congestion, and enhanced urban mobility, making it a valuable tool for modern smart cities. The outcomes of this research pave the way for future advancements in remote sensing and photogrammetry, particularly in the realm of urban traffic surveillance and management. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things (IoT) in the 5G age, the construction of smart cities around the world consequents on the exploration of carbon reduction path based on IoT technology is an important direction for global low carbon city research. Carbon dioxide emissions in small cities are usually higher than that in large and medium cities. However, due to the huge difference in data environment between small cities and Medium-large sized cities, the weak hardware foundation of the IoT, and the high input cost, the construction of a small city smart carbon monitoring platform has not yet been carried out. This paper proposes a real-time estimate model of carbon emissions at the block and street scale and designs a smart carbon monitoring platform that combines traditional carbon control methods with IoT technology. It can exist long-term data by using real-time data acquired with the sensing device. Therefore, the dynamic monitoring and management of low-carbon development in small cities can be achieved. The contributions are summarized as follows: (1) Intelligent thermoelectric systems, industrial energy monitoring systems, and intelligent transportation systems are three core systems of the monitoring platform. Carbon emission measurement methods based on sample monitoring, long-term data, and real-time data have been established, they can solve the problem of the high cost of IoT equipment in small cities. (2) Combined with long-term data, the real-time correction technology, they can dispose of the matter of differences in carbon emission measurement under diverse scales. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITSs) make it possible for applications such as autonomous driving, active safety systems and smart cities. As the infrastructure of ITSs, vehicular ad-hoc network (VANET) plays a key role in ensuring traffic safety while improving driving experience and comfort. However, VANET faces many challenges as the network is exposed to the public and involves sensitive information such as vehicle control commands and driving records. Although blockchain technology can provide such features as decentralization, immutability, non-reliance on trust and traceability, most of the existing blockchain systems impose high storage and computing capacity requirements for participating nodes while terminals nodes in VANET, such as road-side units (RSUs), on-board units (OBUs) and sensors, usually have very limited storage and computing capacity. To solve this problem, this paper proposes a blockchain system that provides scalable storage capacity for VANETs. The proposed scheme uses network sharding and multi-consensus strategy to improve the topology and consensus process of the blockchain. Aimed at lowering the storage requirement on the RSU nodes that participate in the blockchain, a collaborative storage mechanism and a dynamic copy number strategy for the blockchain ledger is designed. Experimental results show that compared to existing schemes, the proposed scalable blockchain storage scheme can significantly lower the storage requirement for the blockchain nodes, thus making it possible for the nodes with limited storage capacity in VANET to participate in the maintenance of the blockchain to prevent the blockchain from drifting towards centralization, and support maximal sharing of road traffic information while ensuring the security, privacy and trustworthiness of information. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is essential in several Internet application areas and remains a key technology for communication technologies. Shorter delays in transmission between Roadside Units (RSUs) and vehicles, road safety, and smooth traffic flow are the major difficulties of Intelligent Transportation System (ITS). Machine Learning (ML) was an advanced technique to find hidden insights into ITSs. This article introduces an Improved Arithmetic Optimization with Deep Learning Driven Traffic Congestion Control (IAOADL-TCC) for ITS in Smart Cities. The presented IAOADL-TCC model enables traffic data collection and route traffic on existing routes for avoiding traffic congestion in smart cities. The IAOADL-TCC algorithm exploits a hybrid convolution neural network attention long short-term memory (HCNN-ALSTM) method for traffic congestion control. In addition, an IAOA-based hyperparameter tuning strategy is derived to optimally modify the parameters of the HCNN-ALSTM model. The presented IAOADL-TCC model effectively enhances the flow of traffic and reduces congestion. The experimental validation was performed using the road traffic dataset from the Kaggle repository. The proposed model obtained an average accuracy of 98.03 % with an error rate of 1.97 %. The experimental analysis stated the superior performance of the IAOADL-TCC approach over other DL methods.

Wavelength division multiplexing (WDM) is one of the emerging technologies for data transmission in 5G access network assembly between the antenna unit (AU) and the distribution unit (DU). Here, in this work, we evaluate and analyze a dense wavelength division multiplexing (DWDM) — passive optical networks (PONs) employing erbium–ytterbium-doped fiber amplifier (EYDFA) + Raman hybrid optical amplifier (HOA) using two modulation schemes — modified duobinary return to zero (MDRZ) and differential phase shift keying (DPSK) for alternate channels, where the channel count has been varied from 8 to 40. The system has been analyzed over the wavelength span of 1550–1558.8 nm with a channel spacing, input power and data rate of 25 GHz, 0 dB and 40 Gb/s, respectively. An average gain, gain ripple and optical signal-to-noise ratio (OSNR) of 20.87, 0.95 and 30 dB, respectively have been observed in the case of HOA along with the achievement of Q-factor of up to 9 at the receiving end of the system. The DPSK-modulated channels result in higher values of gain and OSNR along with lower gain ripple as compared to MDRZ channels making the proposed system a good option for 5G access network applications. [ABSTRACT FROM AUTHOR]

In recent years, Internet of Things (IoT) systems have changed the way we live, work and do businesses in many areas, even those that until recently seemed unlikely. Some areas that can benefit from their application are, among others, healthcare, smart cities, industrial automation, smart agriculture, intelligent transportation systems, smart logistics, and emergency response. This research work proposes a novel alternative that allows the creation of IoT systems capable of making remote changes in devices' communication in a fast and agile manner. Our proposal gives way to some of the most common changes in communication made during the development and maintenance phase in IoT systems, like changing the destination of data transmission, sending the data to multiple destinations, and changing the frequency of sending data. Our solution, which is used in the programs, is loaded on the device. When the device starts, it connects to a configuration server in the background and listens for changes. The changes are sent to the configuration server using specified commands. When a change is detected, the command is processed, and the change in communication is applied without stopping the running program. We designed experiments to evaluate the complexity of the programs developed using our proposal and the actions needed to make a change. [ABSTRACT FROM AUTHOR]

Autonomous systems are becoming increasingly relevant in our everyday life. The transportation field is no exception and the smart cities concept raises new tasks and challenges for the development of autonomous systems development which has been progressively researched in literature. One of the main challenges is communication between different traffic objects. For instance, a mobile robot system can work as a standalone autonomous system reacting to a static environment and avoiding obstacles to reach a target. Nevertheless, more intensive communication and decision making is needed when additional dynamic objects and other autonomous systems are present in the same working environment. Traffic is a complicated environment consisting of vehicles, pedestrians, and various infrastructure elements. To apply autonomous systems in this kind of environment it is important to integrate object localization and to guarantee functional and trustworthy communication between each element. To achieve this, various sensors, communication standards, and equipment are integrated via the application of sensor fusion and AI machine learning methods. In this work review of vehicular communication systems is presented. The main focus is the researched sensors, communication standards, devices, machine learning methods, and vehicular-related data to find existing gaps for future vehicular communication system development. In the end, discussion and conclusions are presented. [ABSTRACT FROM AUTHOR]

Smart Autonomous Vehicles (AVSs) are networks of Cyber-Physical Systems (CPSs) in which they wirelessly communicate with other CPSs sub-systems (e.g., smart -vehicles and smart-devices) to efficiently and securely plan safe travel. Due to unreliable wireless communication among them, such vehicles are an easy target of malware attacks that may compromise vehicles' autonomy, increase inter-vehicle communication latency, and drain vehicles' power. Such compromises may result in traffic congestion, threaten the safety of passengers, and can result in financial loss. Therefore, real-time detection of such attacks is key to the safe smart transportation and Intelligent Transport Systems (ITSs). Current approaches either employ static analysis or dynamic analysis techniques to detect such attacks. However, these approaches may not detect malware in real-time because of zero-day attacks and huge computational resources. Therefore, we introduce a hybrid approach that combines the strength of both analyses to efficiently detect malware for the privacy of smart-cities. [ABSTRACT FROM AUTHOR]

As a new pattern of urbanization, smart cities offer a set of initiatives to achieve a win-win situation for both environmental protection and economic development. This paper aims to explore the influence of smart city policy (SCP) on green technology innovation (GTI) by Chinese listed enterprises. Employing the difference-in-differences (DID) approach, the results show that SCP promotes enterprises' GTI. This finding remains valid after various robustness tests, which include the parallel trend test, PSM-DID, placebo test, the substitution of estimation model, replacement of dependent variable, and the exclusion of the impacts of other policies. Additional tests reveal that SCP improves GTI through government environmental subsidy, corporate environmental awareness, and research and development investments. Furthermore, the heterogeneity analysis suggests that our findings are more prominent in subsamples during the growth stage, in high-tech industries, and in eastern regions. Our research is vital to the urban ecological environment and high-quality economic development. [ABSTRACT FROM AUTHOR]

Currently, technology-based methods are widely used in the solutions developed for smart cities and sustainable transportation. Owing to the rapid advances in technology, the traditional structure of transportation networks is undergoing a paradigm shift. Artificial Intelligence (AI), that has started to have disruptive effects in many sectors in the future, is expected to be one of the most influential factors in the paradigm shift in transportation. In this paper, the dynamics of the paradigmatic shift in transportation to promote sustainable transportation in Türkiye are evaluated through conducting a review of the existing literature. Scenarios exhibiting the use of disruptive and innovative technologies in transport systems, specifically, AI applications in intelligent transportation systems (ITS), are examined. Additionally, the economic, environmental, and social impacts of AI applications are discussed by emphasizing the need to identify priority areas for the effective use of AI in the field of intelligent transport. Thus, this paper, by summarizing the use of AI-based technologies for intelligent transport in Türkiye, contributes to the literature by providing an overview of the existing knowledge. [ABSTRACT FROM AUTHOR]

Vehicular networks are the key paradigm of the Internet of Vehicles (IoV) as the extension of the Internet of Things (IoT) notion in Intelligent Transportation Systems (ITS) which can assist in the development of autonomous driving in smart cities. This technology can provide a wide variety of onboard data services, such as road safety, and increase traffic efficiency by connecting vehicles with road infrastructure and pedestrians. However, it is a challenging task to provide a satisfactory quality of service (QoS) to this network due to a number of limiting factors such as resource collision, resource interference, and congested channels because of the network topology and rapid changes produced by the high mobility as well as hardware imperfections and the anticipated growth of vehicular network devices. As a result, it will be essential to ensure that the resources of the available cellular network are allocated and used in the most efficient possible way. To achieve these goals, 3GPP has standardized the cellular vehicle to everything (C-V2X) with two versions, the long-term evolution-V2X (LTE-V2X) in Release 14 and the new radio-V2X (NR-V2X) in Release 16, as prominent technologies to improve resource allocation for vehicular networks. In order to capture the continuous effort for improving resource allocation, we present a systematic literature review (SLR) on distributed resource allocation (DRA) schemes for the two cellular-based vehicular network technologies. First, we discuss the technical configuration of resource allocation in the light of LTE-V2X and NR-V2X technologies and classify the state-of-the-art for each technology. Afterward, we explain the impact of machine learning (ML) and congestion control (CC) on the DRA. Then, we point out the primary performance metrics and simulation tools that were used in the related work. Ultimately, we highlight the challenges, open issues, and opportunities for DRA in C-V2X and outline several promising future research directions. [ABSTRACT FROM AUTHOR]

The potential of Internet of Things and wireless sensor networks technologies can be used to build a picture of a future intelligent surveillance system. Because of the small size of the sensor nodes and their ability to transmit data remotely, they can be deployed at locations that are difficult or impossible to access. Wireless multimedia sensor network represents a distinct subdomain within the broader scope of wireless sensor networks. It has found diverse applications in the context of future smart cities, particularly in areas such as healthcare monitoring, home automation, transportation systems, and vehicular networks. A wireless multimedia sensor network is a resource-constrained network in which the nodes are small battery-powered devices. In addition, sending a large amount of collected data by wireless multimedia sensor network across the Internet of Things network imposes important challenges in terms of bandwidth, storage, processing, energy consumption, and wireless multimedia sensor network lifespan. One of the solutions to these kinds of problems is data transmission reduction. Therefore, this systematic literature review will review various techniques used in data transmission reduction, ranging from data transmission redundancy reduction to machine learning algorithms. Additionally, this review investigates the range of applications and the challenges encountered within the domain of wireless multimedia sensor networks. This work can serve as a basic strategy and a road map for scholars interested in data reduction techniques for intelligent surveillance systems using WMSN in Internet of Things networks. [ABSTRACT FROM AUTHOR]

This paper uses the panel data of 245 cities in China from 2005 to 2020 to build an ambient air pollution index to measure ambient air quality. Taking China's national smart city pilot as a quasiexperiment, the impact of smart city construction on urban ambient air quality and its impact path is tested by using the multi-period double difference method. The research results show that the construction of smart cities has significantly improved the urban ambient air quality, and with the improvement of the level of smart city construction, the degree of improvement in urban ambient air quality shows a positive fluctuation trend. The mechanism test results show that the construction of smart cities improves urban ambient air quality by enhancing the level of technological innovation and marketization in the city. Heterogeneity testing shows that the ambient air quality improvement effects of smart city construction vary depending on the size of the city's population and the geographical location of the city. Furthermore, the moderating effect test shows that financial autonomy and industrial structure upgrading have a positive moderating effect on the improvement of ambient air quality in smart cities, while local government environmental regulation intensity has a reverse moderating effect. [ABSTRACT FROM AUTHOR]

Traffic has been on the rise since the past decade that pose threats to driving safety and traffic efficiency. Intelligent Transportation System (ITS) evolved as an alternative solution to ensure traffic efficiency, safety and provides comfort to the commuters on roads. In traditional transportation systems, there exist problems related to safety, traffic management, congestion, routing, road infrastructure management, emergency response, communication, and security which can be solved by ITS. From the existing literature, it is evident that several classes of applications pertaining to safety, surveillance, traffic management, weather/pollution monitoring, disaster management in ITS will create an incredible experience to the commuters and the drivers. ITS engulfs applications pertaining to monitor road surfaces incisively and to recognize risks to alleviate unsafe environments and perilous accidents by means of wireless communications. This provides a motivation in this paper to review distinct types of various research works pertaining to applications of Intelligent Transportation Systems which address the problem of traffic congestion, safety and efficiency in modern ITS. Various applications, communication techniques and security are summarized, analyzed and compared with the existing works using various performance metrics. Moreover, an in-depth survey is carried out to provide insights to bridge the gaps and directions for future researchers. Further, in this paper, the case studies related to ITS have been discussed to identify how the paradigm shift will take us to the design of the future transportation systems. [ABSTRACT FROM AUTHOR]

Smart cities like Neom require efficient and reliable transportation systems to support their vision of sustainable and interconnected urban environments. High-speed trains (HSTs) play a crucial role in connecting different areas of the city and facilitating seamless mobility. However, to ensure uninterrupted communication along the rail lines, advanced communication systems are essential to expand the coverage range of each base station (BS) while reducing the handover frequency. This paper presents the dual transceiver free space optical (FSO) communication system as a solution to achieve these objectives in the operational environment of HSTs in Neom city. Our channel model incorporates log-normal (LN) and gamma–gamma (GG) distributions to represent channel impairments and atmospheric turbulence in the city. Furthermore, we integrated the siding loop model, providing valuable insights into the system in real-world scenarios. To assess the system's performance, we formulated the received signal-to-noise ratio (SNR) of the network under assumed fading conditions. Additionally, we analyzed the system's bit error rate (BER) analytically and through Monte Carlo simulation. A comparative analysis with reconfigurable intelligent surfaces (RIS) and relay-assisted FSO communications shows the superior coverage area and efficiency of the dual transceiver model. A significant reduction of up to 76% and 99% in the number of required BSs compared to RIS and relay, respectively, is observed. This reduction leads to fewer handovers and lower capital expenditure (CAPEX) costs. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of transportation infrastructure, the quality and condition of road networks play a pivotal role in societal progress and economic growth. In the realm of road distress detection, traditional methods have long grappled with manual intervention and high costs, requiring trained observers for time-consuming and expensive data collection processes. The limitations of these approaches are compounded by challenges in adapting to diverse road surfaces and handling low-resolution data, particularly in early automated distress survey technologies. This article addresses the critical need for efficient road distress detection, a key component of ensuring safe and reliable transportation systems. Effectively addressing these challenges is crucial for enhancing the efficiency, accuracy, and safety of road distress detection systems. Leveraging advancements in object detection, we introduce the Innovative Road Distress Detection (IR-DD), a novel framework that integrates the YOLOv8 algorithm to enhance the accuracy and real-time capabilities of road distress detection, catering to applications such as smart cities and autonomous vehicles. Our approach incorporates bidirectional feature pyramid network (BiFPN) recursive feature fusion and bidirectional connections to optimize the utilization of multi-scale features, addressing challenges related to information loss and gradients encountered in traditional methods. Comprehensive experimental analysis demonstrates the superior performance, efficiency, and robustness of our integrated approach, positioning it as a cost-effective and compelling alternative to conventional road distress detection methods. Our findings demonstrate the superior performance of our approach compared to other state-of-the-art methods across various evaluation metrics, including precision, recall, F1 score, and mean average precision (mAP) at different intersection over union (IoU) thresholds. Specifically, our method achieves notable results with a precision of 0.666, F1 score of 0.630, mAP@0.5 of 0.650, all while operating at a speed of 86 frames per second (FPS). These outcomes underscore the effectiveness of our approach in real-time road distress detection. This article contributes to the ongoing innovation in object detection techniques, emphasizing the practicality and effectiveness of our proposed solution in advancing the field of road distress detection. [ABSTRACT FROM AUTHOR]

Using and connecting different technologies to improve the quality of urban life is a significant challenge for smart cities. The interconnection of transport, energy, water, public lighting, and other urban infrastructure systems is critical to providing quality services to citizens and reducing environmental impact. However, data interoperability from these systems is a significant challenge, as data is mainly stored in different formats and locations from various sources. This paper examines the challenges of integrating existing datasets in intelligent cities and proposes solutions to create secure and interoperable data-sharing environments. To this end, an approach called data federation is discussed by implementing a federated platform for integrating such data. However, it is essential to note that there may be challenges in terms of cybersecurity and interoperability. This is where context brokers play a crucial role, regulating access to data by enforcing rules and providing security standards. In this way, these challenges can be managed appropriately, ensuring data protection and the system's proper functioning. Finally, it is essential to mention that this work is linked to a smart cities project, which aims to promote innovation in smart cities by implementing several innovative solutions. The study presented in this research contributes to understanding the challenges in smart cities. It proposes solutions for creating secure and interoperable data-sharing environments, which are essential both for the project's success and for the development of smart cities worldwide. [ABSTRACT FROM AUTHOR]

In this paper, we explore the use of visible light positioning (VLP) technology in vehicles in intelligent transportation systems (ITS), highlighting its potential for maintaining effective line of sight (LOS) and providing high-accuracy positioning between vehicles. The proposed system (V2V-VLP) is based on a position-sensitive detector (PSD) and exploiting car taillights to determine the position and inter-vehicular distance by angle of arrival (AoA) measurements. The integration of the PSD sensor in vehicles promises exceptional positioning accuracy, opening new prospects for navigation and driving safety. The results revealed that the proposed system enables precise measurement of position and distance between vehicles, including lateral distance. We evaluated the impact of different focal lengths on the system performance, achieving cm-level accuracy for distances up to 35 m, with an optimum focal length of 25 mm, and under low signal-to-noise conditions, which meets the standards required for safe and reliable V2V applications. Several experimental tests were carried out to validate the results of the simulations. [ABSTRACT FROM AUTHOR]

The Metaverse has the potential to revolutionize various aspects of human life, including transportation systems. The integration of the Metaverse into intelligent transportation systems has the potential to significantly improve traffic safety in smart cities. By creating a virtual replica of the physical world, the Metaverse can provide a platform for testing new traffic management systems, road designs, and vehicle technologies in a controlled and safe environment before implementing them in the real world. One way to integrate the Metaverse into intelligent transportation systems (ITS) is by enhancing traffic safety. This can be achieved by developing an evaluation model that considers both safety and traffic efficiency. The proposed evaluation methodology encompasses three phases. Firstly, the obligations/criteria, and subsidiary obligations are modeled into nodes within levels based on Tree Soft Sets (TrSSs). Secondly, the Opinion Weight Criteria Method (OWCM) is utilized for generating the weights for obligations and subsidiary obligations. Finally, the Root Assessment Method (RAM) harnesses the generated weights for assessing and ranking alternative approaches to improving traffic safety in smart cities. The utilized techniques are working under the authority of neutrosophic theory to support these techniques in uncertain and ambiguous circumstances. Subsequently, the proposed methodology is tested in a case study that considers three alternative approaches to improving traffic safety in a smart city. The criteria for evaluation include safety and traffic aspects. The results of the case study indicate that the proposed evaluation model effectively ranks the alternative approaches based on their safety and traffic efficiency. This suggests that the Metaverse can be effectively integrated to enhance traffic safety and improve overall transportation efficiency. Overall, the results of the case study suggest that the proposed evaluation model effectively ranks the alternative approaches based on their safety and traffic efficiency. This indicates that the integration of the Metaverse can indeed enhance traffic safety and improve overall transportation efficiency in smart cities. [ABSTRACT FROM AUTHOR]

Our daily activities revolve around various technologies, the smart city technologies and the services they offer have all influenced modern living conditions. The purpose of a smart city is to enhance people's quality of life and provide possibilities to address social and environmental issues. As a developing technology, blockchain is beneficial for enhancing smart city services including food tracking, supply-demand matching, the security of connected cars, and regulatory compliance. We propose a new framework for describing how blockchain technology is used in smart contracts to improve security, dependability, and many other positive outcomes in a smart city environment. We propose smarter and resilient smart contracts using blockchain technology to manage real estate information. We propose a framework with tamperproof functionality to store the data and retrieve the data. Smart contract mathematical computations like overhead rate, execution time, mean computational cost, standard deviation, throughput, and resource utilization are evaluated and the results are compared. This paper focuses on real-world rental file management scenarios to demonstrate the benefits of blockchain technology and how it is used to address the issues that currently exist in developing smart contracts and services while exchanging real land and other properties in a smart city environment. [ABSTRACT FROM AUTHOR]

Due to the tremendous growth of road traffic accidents, Intelligent Transportation Systems (ITSs) are becoming even more important. To prevent road traffic accidents in the long term, it is necessary to find new vehicle flow management techniques in order to optimize traffic flow. With the high growth of deep learning and machine learning, these methods are increasingly being used in ITSs. This research provides a novel conceptual ITS model that aims to predict vehicle movement through the collective learning usage to anticipate intersections. The proposed approach consists of three main stages: data collection through cameras and sensors, implementation of machine learning and deep learning algorithms, and result evaluation, utilizing the coefficient of determination (R-squared), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). To accomplish this, various machine learning and deep learning algorithms, such as Random Forest, LSTM, Linear Regression, and ensemble methods (bagging), were incorporated into the model. The findings revealed the enhancement due to the proposed method, which was observed through a significant performance improvement of 93.52%. [ABSTRACT FROM AUTHOR]

The continuous global urbanization with rapid and dynamic transitioning in traffic situations among highly populated cities results in difficulty for data collection and communication. Data collection for millions of vehicles hinders by various problems, i.e., higher cost of energy, time, space, and storage resources. Moreover, higher data traffic results in higher delays, larger throughput, excessive bottlenecks, and frequent repetition of data. To better facilitate the aforementioned challenges and to provide a solution, we have proposed a lightweight Machine Learning based data collection protocol named ML-TDG to effectively deal with higher data volumes in a real-time traffic environment capable of bringing the least burden on the network while utilizing less space, time, and energy. ML-TDG is functional based on Apache Spark, an effective data processing engine that indexes the data based on two logs, i.e., old commuters or frequent/daily commuters and second new/occasional commuters. The proposed protocol's main idea is to utilize real-time traffic, distinguish the indexes in parallel based on two assigned logs criteria to train the network, and collect data with the least sources. For energy and time optimization, dynamic segmentation switching is introduced which is an intelligent road segments division and switching for reducing bottlenecks and replication. ML-TDG is tested and verified on Dublin, Ireland's busiest motorway M50. ML-TDG performs the data collection, data sorting, and network training to decide the next execution altogether for better optimization every time. The experimental results verify that our proposed protocol is attaining higher performance with lower resource requirements along with rich and time-efficient sustainable data collection clusters in comparison with baseline protocols. [ABSTRACT FROM AUTHOR]

The preface to the Proceedings of the 8th Smart Data Smart Cities (SDSC) conference provides an overview of the concept of a Smart City, which involves the use of digital layers for city governance, citizen services, and sustainable development. Smart Cities utilize intelligent technology to enhance urban life by integrating data from various sources such as sensors, demographics, mapping, and building information models. The conference focuses on the use of spatial information and communication technologies in Smart Cities, covering topics such as 3D modeling, citizen engagement, transportation, urban decision-making, and smart urban mobility. The volume consists of 31 research papers selected through blind review, addressing different aspects of Smart Cities. [Extracted from the article]

In the realm of modern urban mobility, automatic incident detection is a critical element of intelligent transportation systems (ITS), since the ability to promptly identify unexpected events allows for quick implementation of preventive measures and efficient response to the situations as they arise. With the growing availability of traffic data, Machine Learning (ML) has become a vital tool for enhancing traditional incident detection methods. Automated machine-learning (AutoML) techniques present a promising solution by streamlining the machine-learning process; however the application of AutoML for incident detection has not been widely explored in scientific research In this paper, we propose and apply an AutoML-based methodology for traffic incident detection and compare it with state-ofthe-art ML approaches. Our approach integrates data preprocessing with AutoML, and uses Tree-based Pipeline Optimization Tool (TPOT) to refine the process from raw data to prediction. We have tested the efficiency of our approach in two major European cities, Athens and Antwerp. Finally, we present the limitations of our work and outline recommendations for application of AutoML in the incident detection task and potentially in other domains. [ABSTRACT FROM AUTHOR]

Using unmanned aerial vehicles (drones) in security has become common in the Czech Republic. This paper focuses on using drones as an example of the application of smart technologies in conjunction with the Internet of Things (IoT) and state security forces, especially in cooperation with the police, fire and medical emergency services in the context of the new Smart Cities concept. Drones are one of the most suitable examples of use along with IoT within the security sector, which play a key role in the daily protection of citizens' safety, public order security and crisis management. It is related to developing and implementing 5G networks, which allow real-time transmission of data obtained by drones directly to information systems, and is also gaining importance. This technology enables fast and efficient communication between the different actors involved in an intervention and creates a platform for fast and effective emergency response. Among other things, paying attention to cybersecurity, such as securing data transmissions, protecting against cyber threats, complying with security standards and legislation, and introducing special procedures and methodologies applicable in crisis, is essential. [ABSTRACT FROM AUTHOR]

Effective means for transportation form a critical city infrastructure, particularly for resource-constrained smart cities. Rapid advancements in information and communication technologies have paved the path for intelligent transportation system (ITS), specifically designed for optimal effectiveness and safety with existing transportation infrastructure. A key function of ITS is its ability to aggregate large volumes of data across various sources for event detection. However, prediction accuracy remains a challenge since ITS event detection is characterized by very stringent latency requirements necessitating the use of lightweight detection schemes, thus seriously compromising the efficiency of ITS. This paper attempts to tackle this problem by introducing an IoT-integrated distributed context-aware fog-cloud ensemble that intelligently manages context instances at fog nodes ensuring availability of context instances for ITS. This system enhances prediction accuracy by utilizing a hybrid convolutional neural network (CNN) where each vehicle within the system retains only local information, while adjacent fog nodes gain access to global events via continual federated learning, updating regularly between fog and cloud models. Experiments presented herein illustrate the superiority of the CNN model, yielding an accuracy of more than 95%, which is an improvement of around 3% compared to the LeNet with same RGB input images. [ABSTRACT FROM AUTHOR]

Fog computing is a distributed computing architecture, as opposed to depending entirely on centralized cloud servers, which brings the processing of data, functionality of an application, and its storage closer to the network's edge, where it can be closer to the data source or an end‐user device. Some of the potential applications of the fog computing‐based Internet of Things (IoT)‐enabled system are smart healthcare, smart agriculture, smart manufacturing, intelligent transportation system, and smart cities (i.e., in parking management, lighting control, traffic control, and security of civilians). The fog computing‐based IoT‐enabled system is vulnerable to various attacks. Therefore, one needs to deploy security mechanisms, like authentication, access control, key management, and malware detection, in order to secure its communication. In this article, we design a signature‐based access control and key management scheme for fog computing‐based IoT‐enabled big data applications (in short, SBAC‐FC). A detailed security analysis and performance comparison of the SBAC‐FC with other similar existing schemes reveal that the SBAC‐FC surpasses the existing schemes in terms of security and functionality characteristics, as well as complexity overheads. [ABSTRACT FROM AUTHOR]

A Vehicular Ad-hoc Network (VANET) is an essential component of intelligent transportation systems in the building of smart cities. A VANET is a self-configure high mobile and dynamic potential wireless ad-hoc network that joins all vehicle nodes in a smart city to provide in-vehicle infotainment services to city administrators and residents. In the smart city, the On-board Unit (OBU) of each vehicle has multiple onboard sensors that are used for data collection from the surrounding environment. One of the main issues in VANET is energy efficiency and balance because the small onboard sensors can't be quickly recharged once installed on On-board Units (OBUs). Moreover, conserving energy stands out as a crucial challenge in VANET which is primarily contingent on the selection of Cluster Heads (CH) and the adopted packet routing strategy. To address this issue, this paper proposes distance and energy-aware clustering algorithms named SOMNNDP, which use a Self-Organizing Map Neural Network (SOMNN) machine learning technique to perform faster multi-hop data dissemination. Individual Euclidean distances and residual node energy are considered as mobility parameters throughout the cluster routing process to improve and balance the energy consumption among the participating vehicle nodes. This maximizes the lifetime of VANET by ensuring that all intermediate vehicle nodes use energy at approximately the same rate. Simulation findings demonstrate that SOMNNDP improves Quality of Service (QoS) better and consumes 17% and 14% less energy during cluster routing than distance and energy-aware variation of K-Means (KM) and Fuzzy C-Means (FCM) called KMDP and FCMDP respectively. [ABSTRACT FROM AUTHOR]

"Smart city" refers to a modern solution to organizing a city's services, using cloud technologies to collect and evaluate large amounts of data, including data from camera systems. Smart city management covers several areas that can be implemented separately, but only their combination can realize the overall desired smart city function. One of the core areas of smart city automation is smart city transport. Transportation is a crucial system in any city, and this is why it needs to be monitored. The primary objective of this publication is to generate top-notch 4K UHD video sequences that are solely dedicated to showcasing smart cities and their transportation systems. The resulting comprehensive database will be made accessible to all professionals in the field, who can utilize it for extensive research purposes. Additionally, all the reference video sequences will be transcoded into various quality settings by altering critical parameters like the resolution, compression standard, and bit rate. The ultimate aim is to determine the best combination of video parameters and their respective settings based on the measured values. This in-depth evaluation will ensure that each video sequence is of the highest quality and provides an unparalleled experience for the service providers offering the service. The video sequences captured will be analyzed for quality assessments in smart cities or smart transport technologies. The database will also include objective and subjective ratings, along with information about the dynamics determined by spatial and temporal information. This will enable a comparison of the subjective evaluation of a selected sample of our respondents with the work of other researchers, who may evaluate it with a different sample of evaluators. The assumption of our future research is to predict the subjective quality based on the type of sequence determined by its dynamicity. [ABSTRACT FROM AUTHOR]

Sustainable smart cities (SSCs) target decarbonisation by optimising energy consumption through the emerging capabilities of technology. Nevertheless, the energy consumption behaviour of end users has the potential to compromise the effectiveness of technological interventions, reflecting the importance of active social engagement in realising decarbonisation goals. Although extensive research exists on energy consumption behaviour, little is known about how technology engagement affects it, the nature of these technologies, and their role in SSC. The paper aims to identify, categorise, and investigate the smart technologies that impact household energy consumption behaviours and their integration into the larger SSC system. Following a systematic review of 60 articles from the Scopus database (2013–2023), the study found 45 smart technologies cited, with 49% affecting efficiency behaviour and 51% affecting curtailment behaviour. While these technologies inform the city administration level in the SSC framework, the role of end users remains unclear, suggesting a technocratic approach. The study proposes the Sustainable Smart City Network to facilitate a grassroots approach, identifying five key domains: government policies, smart technology adoption, smart technology engagement, smart city infrastructure, and urban sustainability. The study provides an original contribution to knowledge by unveiling the key technologies affecting energy consumption behaviour and outlining the pragmatic requirements for achieving decarbonisation through a grassroots approach. [ABSTRACT FROM AUTHOR]

Traffic congestion is one of the major challenges faced by daily commuters in smart cities. An autonomous transportation system with a 5 G-based Cellular Vehicle-to-Everything (C-V2X) communication system is the solution to meet the traffic challenges faced in smart cities. Vehicular networks provide wireless connectivity to enable a large number of connected vehicle applications. Vehicular networks allow vehicles to share their emergency and infotainment traffic by following vehicle-to-vehicle (V2V) or by using vehicle-to-infrastructure (V2I) communication. The infrastructure of vehicular networks mainly comprises multiple Road Side Units (RSUs). Fog computing nodes are placed adjacent to these RSUs to provide quick access to vehicles. For infotainment traffic, vehicles intend to download their required content from the content provider. Caching the same contents from the nearby fog computing node significantly reduces delay with improved quality of service. As there are millions of contents with varying sizes, caching all demanded contents on these fog nodes is not possible due to their limited caching capacity. In this work, we propose an improved content caching scheme for fog nodes to satisfy vehicles and content providers for fair content placement. The proposed algorithm is based on a modified Gale–Shapley technique that considers factors such as content popularity, vehicle connectivity, and quality of the communication channel to optimize the content caching process. Simulation results show that the proposed technique caches a higher number of popular contents with lower downloading time. [ABSTRACT FROM AUTHOR]

Smart cities today can utilize vehicular delay tolerant networks (VDTN) to collect data from connected-objects in the environment for various delay-tolerant applications. They can take advantage of the available intelligent transportation systems (ITS) infrastructures to deliver data to the central server. The system can also exploit multiple and diverse mobility patterns found in cities, such as privately owned cars, taxis, public buses, and trams, along with their vehicle-toeverything (V2X) communications capabilities. In the envisioned convergence between the ITS and V2X, we believe that a simple and efficient routing protocol can be deployed for the delay-tolerant data delivery, contrary to the implementation of optimized solutions that might be resource-demanding and difficult to standardize. In this paper, we analyzed the performances of four baseline VDTN routing protocols, namely: direct delivery, first contact, epidemic, and spray and wait, to understand their strengths and weaknesses. Our simulation results highlighted the trade-off between distinct approaches used by those protocols and pointed out some gaps that can be refined. This study provides new interesting ideas and arguments towards developing a simple, efficient, and high-performing routing protocol for data collection in smart cities. [ABSTRACT FROM AUTHOR]

Nowadays, smart parking systems (SPS) represent one of the active research areas of intelligent transport systems (ITS). It is an interesting topic and attractive to researchers due to the difficult challenges that are needed to solve it. It enhances city life and plays an important role in reducing gas emissions, air pollution, and traffic congestion while improving the time override for drivers to easily allocate vacant parking lots. In this paper, we discuss and dedicate a review of how computer visionbased approaches can assist in providing wellness solutions for the occupancy of smart parking lots. In addition, we provide an overview of the most common standard datasets and some well-known applications of vacant smart parking lots. [ABSTRACT FROM AUTHOR]

Introduction: Vehicle re-identification is a crucial task in intelligent transportation systems, presenting enduring challenges. The primary challenge involves the inefficiency of vehicle re-identification, necessitating substantial time for recognition within extensive datasets. A secondary challenge arises from notable image variations of the same vehicle due to differing shooting angles, lighting conditions, and diverse camera equipment, leading to reduced accuracy. This paper aims to enhance vehicle re-identification performance by proficiently extracting color and category information using a multi-attribute dense connection network, complemented by a distance control module. Methods: We propose an integrated vehicle re-identification approach that combines a multi-attribute dense connection network with a distance control module. By merging a multi-attribute dense connection network that encompasses vehicle HSV color attributes and type attributes, we improve classification rates. The integration of the distance control module widens inter-class distances, diminishes intra-class distances, and boosts vehicle re-identification accuracy. Results: To validate the feasibility of our approach, we conducted experiments using multiple vehicle re-identification datasets. We measured various quantitative metrics, including accuracy, mean average precision, and rank-n. Experimental results indicate a significant enhancement in the performance of our method in vehicle re-identification tasks. Discussion: The findings of this study provide valuable insights into the application of multi-attribute neural networks and deep learning in the field of vehicle re-identification. By effectively extracting color information from the HSV color space and vehicle category information using a multi-attribute dense connection network, coupled with the utilization of a distance control module to process vehicle features, our approach demonstrates improved performance in vehicle re-identification tasks, contributing to the advancement of smart city systems. [ABSTRACT FROM AUTHOR]

The problem of traffic assignment consists of determining the routes taken by the users of transportation infrastructure. This problem has been the subject of numerous studies, particularly in analyzing scenarios for developing road infrastructure and pricing strategies. This paper reviews the major progress in the field. Accordingly, it shows that the evolution of intelligent transportation systems and the emergence of connected and autonomous vehicles present new challenges to classical approaches for solving the traffic assignment problem. It addresses two major perspectives: digital twins coupled with artificial intelligence to help decision-makers, and rule-based policy to offer users fair and efficient itineraries while respecting infrastructure capacity. [ABSTRACT FROM AUTHOR]

With the advancement of information and communication technologies (ICTs), the way we live and communicate with each other is changing rapidly. As urban environments continue to evolve, the smart sustainable city (SSC) has sparked considerable attention. We are hoping for a new era in which numerous devices and machines including vehicles, sensors, and robots are all connected to communicate, respond, and operate in real time. The next-generation communication system, the sixth generation (6G), is expected to play a crucial role in improving the efficiency of urban operations and services. In this paper, we first provide the recent trends and key features of standardization in the SSC. To make the future SSC, we highlight key candidate technologies of 6G such as non-terrestrial networks, advanced mobile edge computing, vision-aided wireless communication, artificial intelligence (AI)-based wireless communication, and integrated sensing and communication. We put forth the main technical challenges given to each prime technology along with the potential benefits to pave the way for 6G-enabled SSC. We further address how the potential benefits of prime technologies enable various urban practice cases for 6G-enabled SSC. [ABSTRACT FROM AUTHOR]

The effective tracking and analysis of moving objects within urban environments presents a complex challenge that necessitates robust geospatial data integration. Open Geospatial Consortium (OGC) APIs offer standardized approaches to managing dynamic geospatial information. This paper presents a case study of real-time moving object tracking including buses and trains in the city of Munakata, Japan, utilizing two prominent OGC APIs: OGC API Moving Features and OGC SensorThings API. The study explores the implementation of both APIs, examining their strengths and limitations in handling real-time location updates and associated sensor data generated by moving buses. The research provides insights into the practical suitability of each API model for dynamic object tracking, offering valuable guidance for practitioners seeking to optimize geospatial data integration within smart cities and intelligent transportation systems. [ABSTRACT FROM AUTHOR]

Smart cities and the Internet of Things have enabled the integration of communicating devices for efficient decision-making. Notably, traffic congestion is one major problem faced by daily commuters in urban cities. In developed countries, specialized sensors are deployed to gather traffic information to predict traffic patterns. Any traffic updates are shared with the commuters via the Internet. Such solutions become impracticable when physical infrastructure and Internet connectivity are either non-existent or very limited. In case of developing countries, no roadside units are available and Internet connectivity is still an issue in remote areas. Internet traffic analysis is a thriving field of study due to the myriad ways in which it may be put to practical use. In the intelligent Internet-of-Vehicles (IOVs), traffic congestion can be predicted and identified using cutting-edge technologies. Using tree-based decision-tree, random-forest, extra-tree, and XGBoost machine learning (ML) strategies, this research proposes an intelligent-transport-system for the IOVs-based vehicular network traffic in a smart city set-up. The suggested system uses ensemble learning and averages the selection of crucial features to give high detection accuracy at minimal computational costs, as demonstrated by the simulation results. For IOV-based vehicular network traffic, the tree-based ML approaches with feature-selection (FS) outperformed those without FS. When contrasted to the lowest KNN accuracy of 96.6% and the highest SVM accuracy of 98.01%, the Stacking approach demonstrates superior accuracy as 99.05%. [ABSTRACT FROM AUTHOR]