Jul 2026· 2026 11th International Conference on Applying New Technology in Green Buildings (ATiGB)· pp. 608-613· 0 citations· 28 references
Abstract
Traditional transportation systems have evolved into more advanced systems, namely the Intelligent Transportation Systems (ITS). But despite this transformation, modern transportation systems still face problems. Challenges such as traffic congestion, accidents, and high emissions are yet to be effectively solved. Gathered data show that around 10% of the world's emissions come from the transportation sector, and approximately 1.3 million deaths happen every year from road accidents. These recurring issues demonstrate the necessity for ITS to be enhanced and evolved to gain the ability to solve those problems. Numerous existing literatures have covered the topic of data-driven ITS, with the primary focus on explaining the technological side of transportation innovations. In this context, ITS has become a foundation of smart city development, enabling data-driven and sustainable mobility systems. However, there remains a knowledge gap as no research specifically learned about the enablers behind those successful ITS implementations. Studying enablers helps to create knowledge on how previous ITS implementations were successfully launched. This can aid transportation providers to replicate those functional outcomes for the creation of smarter mobility solutions. This study uses the Systematic Literature Review (SLR) methodology, where 22 documents were collected from databases using relevant search words before being processed using a five-step framework. This study contributes by suggesting a taxonomy of ITS enablers, grouped into three categories: technological, institutional, and human. The long-term goal of this study is to improve how ITS responds to real-world transportation challenges and to fully eliminate those problems using knowledge from previous studies.
This study investigates the integration of Emerging Computing Technologies into smart road infrastructures as a potential response to these challenges, and explores key enabling technologies for their capacity to support intelligent transportation systems.
Afzal Badshah, Ali Daud, S. Arafat et al.· Computers, Materials & C...· 0 citations
Intelligent Transportation Systems (ITS) increasingly combine sensing, communication, computation, data platforms, and mobility services. This article presents a structured, literature-based mapping review of ITS applications from a standards-oriented perspective. The study analyzes 42 studies organized into five thematic groups and evaluated through 63 article–standard assessments using selected ITU-T Recommendations as an analytical lens. The rubric used in this work examined whether each study reported, or allowed reviewers to infer, evidence on architecture, data handling, interoperability, security and privacy, deployment assumptions, and digital-twin capabilities. Partial alignment was the most frequent outcome, accounting for 25 of 63 article–standard assessments (39.7%). At group level, satisfactory or optimal alignment occurred in 5 of 8 assessments (62.5%) in the digital-twin group and in 3 of 13 (23.1%) in the Big Data group; in the latter, 6 of 13 assessments (46.2%) showed limited or no alignment. Stronger alignment was usually found when studies described architectures, data flows, sensing mechanisms, service workflows, physical–virtual modeling, or system-management components relevant to the Recommendation, and weaker alignment when they focused mainly on algorithms, datasets, prediction accuracy, authentication, or secure dissemination without sufficient detail on interfaces, data governance, gateway roles, deployment conditions, or platform integration. The review proposes a five-dimension standards-facing reporting checklist addressing interoperability, data lifecycle and governance, security and privacy, operational readiness, and standards-facing evidence. It supports traceable reporting through explicit evidence-status categories and locations, and can be implemented as a Standards and Interoperability Reporting Statement (SIRS) for authors, reviewers, and editors. Overall, the findings show that standards-oriented assessment depends not only on technical performance but also on explicit and traceable integration evidence, while the proposed reporting profile provides a practical mechanism for making such evidence more systematically visible in future ITS studies.
Francisco Cachumba, P. B. Bautista, Nathaly Verónica Orozco Garzón et al.· Smart Cities· 0 citations
The rapid growth of urbanization triggers chronic congestion and deteriorating air quality in urban areas. Objective: This study aims to design an Enterprise Architecture (EA) for a Smart Traffic Management Information System based on Big Data IoT using the TOGAF ADM 9.2 framework. Methods: The research employed a qualitative-descriptive approach combined with system engineering methodology based on the TOGAF ADM 9.2 framework, limited to five main stages: Preliminary Phase, Architecture Vision, Business Architecture, Information Systems Architecture, and Technology Architecture. Findings: The design results in a blueprint for business architecture, data architecture, application architecture, and technology architecture that can reduce traffic data latency and sectoral emissions. The Dual-Stream Data Pipeline model enables simultaneous processing of traffic and emissions data to support efficient public transportation operational decisions. Conclusion and Implication: This EA provides strategic guidance for local governments in realizing efficient and sustainable public transportation. The novelty of this research lies in the integration of highway corridor architecture (Dual-Stream Data Pipeline) which brings together public transportation optimization and environmental emission monitoring simultaneously, addressing the gap between Smart Traffic and Smart Environment systems that typically operate in silos. Keywords: enterprise architecture, TOGAF ADM, big data IoT, smart traffic, smart environment.
I. Setiawan· Jurnal Indonesia Sosial Sain...· 0 citations
The fast pace of urbanization has made smart and sustainable infrastructure management more important than ever. Because of their inherent silos, traditional urban management systems are unable to adapt in real-time to shifting demands in areas such as water distribution, public safety, energy consumption, traffic flow, and energy consumption. This study found that smart cities may use AI and the internet of things to adapt and manage their infrastructure using data. Sensors throughout the city’s infrastructure for transportation, power, buildings, and the environment provide data into Internet of Things devices. Analytics systems powered by AI can automate decision-making, enhance resource allocation, discover anomalies, and forecast demand using massive amounts of data. For predictive maintenance and real-time monitoring, the framework places an emphasis on interoperability, scalability, cybersecurity, and sustainability. By replacing reactive systems with proactive ones, adaptive algorithms and machine learning models can increase dependability, save costs, and revolutionize urban planning. Topics covered in the research include data privacy, infrastructure integration, and data governance. The convergence of AI with the Internet of Things (IoT) creates robust, efficient, citizen-centric urban ecosystems, as shown by comprehensive design and performance evaluation metrics. Smart cities that can adjust to changes in the environment, population, and economy are made possible by these discoveries.
P. Kumaresan, Hayel Khafajeh, R. Latha et al.· International Conference on...· 0 citations
The high rate of urbanization has caused a high growth in the number of vehicles, which has produced a congestion, wastage on time, and fuel, as well as pollution to the environment. No longer applicable because of the dynamic character of modern urban traffic, the traditional traffic management systems based on the use of the non-informative control mechanisms and low real-time flexibility. The Intelligent Traffic Management Systems (ITMS) have become a very important part of an intelligent city system, as it intends to use the latest technologies that include Artificial Intelligence (AI), Internet of Things (IoT), machine learning, cloud computing and big data analysis to make traffic flow in the city more efficient and safer. In this paper, complete research on Intelligent Traffic Management Systems in smart cities has been made. It dwells upon the development of traffic management, the enabling technologies, system architecture, and methodologies. An elaborate literature review indicates the latest developments and outlines the gaps in research. The proposed approach will combine real-time data collection, predictive analysis, and responsive signal modulation to improve the traffic flow. The mathematical models and performance evaluation measures have been addressed to measure the system effectiveness. The findings indicate that intelligent systems are very effective in minimizing congestion, travelling time as well as emissions over traditional methods. Lastly, issues, constraints, and research prospects are given to facilitate long-term and viable implementation of ITMS in intelligent city setups.
Grace Ndlovu· International Journal of Mod...· 0 citations
Rapid urban growth, the rise of online shopping, and increasing demand for fast deliveries are putting significant pressure on city logistics, particularly on the “last mile” of delivery. These pressures contribute to traffic congestion, higher emissions, and inefficient use of urban space. This paper examines how smart urban logistics solutions can make city deliveries more sustainable. Such solutions include the use of micro-hubs (small local distribution centers), consolidation centers, low-emission delivery vehicles, digital technologies, and improved coordination between logistics planning and urban planning. The study is based on a review of relevant literature, as well as examples from European cities. It considers initiatives such as micro-hub networks, clean air zones, car-free city centers, and the 15-minute city concept, supported by selected data from previous studies. The findings demonstrate that effective regulations and appropriate infrastructure can significantly reduce pollution and spatial challenges associated with urban deliveries. When integrated with urban planning, digital technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Blockchain, and Digital Twins can improve route planning, enhance transparency, and support better decision-making. However, smart urban logistics also faces challenges, including high infrastructure costs, fragmented regulations, technical complexities, and resistance from stakeholders. To address these issues, the paper proposes a phased approach for implementing smart urban logistics solutions. Overall, the research concludes that a successful transformation requires a comprehensive strategy combining urban planning, policy measures, infrastructure development, digital technology adoption, cooperation between public and private actors, and active citizen involvement.
Luka Pavlović, M. Jardas, A. Agatic· 0 citations
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