Search results

This article examines the contribution of artificial intelligence to augmenting Intelligent Transportation Systems (ITS) to enhance traffic flow, safety, and sustainability.

The research investigates using AI technologies in ITS, including machine learning, computer vision, and deep learning. It analyzes case studies on ITS projects in Poznan, Mysore, Austin, New York City, and Beijing to identify essential components, advantages, and obstacles.

Using AI in Intelligent Transportation Systems has considerable opportunities for enhancing traffic efficiency, minimizing accidents, and fostering sustainable urban growth. Nonetheless, issues like data quality, real-time processing, security, public acceptability, and privacy concerns need resolution.

This article thoroughly examines AI-driven ITS, emphasizing successful applications and pinpointing significant difficulties. It underscores the need for a sustainable economic strategy for extensive adoption and enduring success.

This study aims to introduce a fully automatic process for publishing and leveraging open government data (OGD) as linked OD (LOD), called the LOD-GePEx for LOD generation, publication and exploitation. It empowers developers to harness open-source data, creating practical and beneficial citizen applications.

The LOD-GePEx approach is a three-step process. First, it transforms OGD into LOD, adhering to the four LD principles. Second, it publishes the generated LOD in the OGD portal, following accessibility best practices. Third, it exploits the published LOD through an interface that offers multiple services. In addition, this paper carried out functional and performance tests to prove the proposed approach’s effectiveness and performance.

The evaluation phase demonstrated that the LOD-GePEx approach is effective and efficient. It transforms OGD into LOD with exploitation services to enable insightful data analysis for stakeholders without intensive human workloads. Besides, it is generic; it can be used for several data sets and any case study.

The approach struggles with speed index and interactivity time in resource description framework and LOD publication generation, particularly with large government data sets. However, this work has prioritized the comprehensive exploration of the end-to-end process and handling of diverse data domains. This work implies that adopting this approach will facilitate the publication and exploitation of OGD.

The proposed approach surpasses the limitations of related works, mainly focusing on particular use cases and manual data linking, which require intensive human workloads and few or no data insights. Besides, the proposed approach is the only approach that addresses the end-to-end process of transforming, publishing and exploiting OGDs as LODs.

The purpose of this paper is to evaluate ontologies with respect to the linked data principles. This paper presents a concrete interpretation of the four linked data principles applied to ontologies, along with an implementation that automatically detects violations of these principles and fixes them (semi-automatically). The implementation is applied to a number of state-of-the-art ontologies.

Based on a precise and detailed interpretation of the linked data principles in the context of ontologies (to become as reusable as possible), the authors propose a set of algorithms to assess ontologies according to the four linked data principles along with means to implement them using a Java/Jena framework. All ontology elements are extracted and examined taking into account particular cases, such as blank nodes and literals. The authors also provide propositions to fix some of the detected anomalies.

The experimental results are consistent with the proven quality of popular ontologies of the linked data cloud because these ontologies obtained good scores from the linked data validator tool.

The proposed approach and its implementation takes into account the assessment of the four linked data principles and propose means to correct the detected anomalies in the assessed data sets, whereas most LD validator tools focus on the evaluation of principle 2 (URI dereferenceability) and principle 3 (RDF validation); additionally, they do not tackle the issue of fixing detected errors.