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Over the past decade, the electric vehicle (EV) industry has experienced unprecedented growth and diversification, resulting in a complex ecosystem. To effectively manage this multifaceted field, we present an EV‐centric knowledge graph (EVKG) as a comprehensive, cross‐domain, extensible, and open geospatial knowledge management system. The EVKG encapsulates essential EV‐related knowledge, including EV adoption, EV supply equipment, and electricity transmission network, to support decision‐making related to EV technology development, infrastructure planning, and policy‐making by providing timely and accurate information and analysis. To enrich and contextualize the EVKG, we integrate the developed EV‐relevant ontology modules from existing well‐known knowledge graphs and ontologies. This integration enables interoperability with other knowledge graphs in the Linked Data Open Cloud, enhancing the EVKG's value as a knowledge hub for EV decision‐making. Using six competency questions, we demonstrate how the EVKG can be used to answer various types of EV‐related questions, providing critical insights into the EV ecosystem. Our EVKG provides an efficient and effective approach for managing the complex and diverse EV industry. By consolidating critical EV‐related knowledge into a single, easily accessible resource, the EVKG supports decision‐makers in making informed choices about EV technology development, infrastructure planning, and policy‐making. As a flexible and extensible platform, the EVKG is capable of accommodating a wide range of data sources, enabling it to evolve alongside the rapidly changing EV landscape. [ABSTRACT FROM AUTHOR]

Availability of more accurate Crash Modification Factors (CMFs) is crucial for evaluating the effectiveness of various road safety treatments and prioritizing infrastructure investment accordingly. While customized study for each countermeasure scenario is desired, the conventional CMF estimation approaches rely heavily on the availability of crash data at specific sites. This dependency may hinder the development of CMFs when it is impractical to collect data for recent implementations. Additionally, the transferability of CMF knowledge faces challenges, as the intrinsic similarities between different safety countermeasure scenarios are not fully explored. Aiming to fill these gaps, this study introduces a novel knowledge-mining framework for CMF prediction. This framework delves into the connections of existing countermeasure scenarios and reduces the reliance of CMF estimation on crash data availability and manual data collection. Specifically, it draws inspiration from human comprehension processes and introduces advanced Natural Language Processing (NLP) techniques to extract intricate variations and patterns from existing CMF knowledge. It effectively encodes unstructured countermeasure scenarios into machine-readable representations and models the complex relationships between scenarios and CMF values. This new data-driven framework provides a cost-effective and adaptable solution that complements the case-specific approaches for CMF estimation, which is particularly beneficial when availability of crash data imposes constraints. Experimental validation using real-world CMF Clearinghouse data demonstrates the effectiveness of this new approach, which shows significant accuracy improvements compared to the baseline methods. This approach provides insights into new possibilities of harnessing accumulated transportation knowledge in various applications. • Introduces a novel method for CMF estimation that maximizes existing knowledge use. • Provides an effective and transferable CMF solution for data-constrained scenarios. • Developed an NLP-based data-driven framework, and verified on real-world CMF data. • Gained insights into leveraging transport knowledge to improve road safety analysis. [ABSTRACT FROM AUTHOR]

Abstract Increasing availability of data related to air quality from ground monitoring stations has provided the chance for data mining researchers to propose sophisticated models for predicting the concentrations of different air pollutants. In this paper, we proposed a hybrid model based on deep learning methods that integrates Graph Convolutional networks and Long Short-Term Memory networks (GC-LSTM) to model and forecast the spatiotemporal variation of PM 2.5 concentrations. Specifically, historical observations on different stations are constructed as spatiotemporal graph series, and historical air quality variables, meteorological factors, spatial terms and temporal attributes are defined as graph signals. To evaluate the performance of the GC-LSTM, we compared our results with several state-of-the-art methods in different time intervals. Based on the results, our GC-LSTM model achieved the best performance for predictions. Moreover, evaluations of recall rate (68.45%), false alarm rate (4.65%) (both of threshold: 115 μg/m3) and correlation coefficient R 2 (0.72) for 72-hour predictions also verify the feasibility of our proposed model. This methodology can be used for concentration forecasting of different air pollutants in future. Highlights • A hybrid model is proposed for hourly PM 2.5 forecasting. • Considering spatiotemporal dependency can improve model performance. • Our model shows better performance especially for long-term prediction. Graphical Abstract [ABSTRACT FROM AUTHOR]

High utility pattern mining (HUPM) plays a significant role in data mining technologies. Traditional HUPM algorithms may produce a large number of high utility patterns (HUPs) when the database is dense or the data is massive. To address this issue, closed high utility pattern (CHUP) mining was proposed, providing a high-level overview of the HUPs and helpful information for decision-makers. However, CHUPs do not consider the factors of period and recency. Therefore, this paper is the first to introduce period and recency into closed high utility pattern mining and proposes the CPR-Miner algorithm to mine closed periodic recent high utility patterns. These patterns have more practical value since they are closed sets of HUPs. Due to the increasing number of factors to be considered, new upper bounds and pruning strategies are also proposed, significantly improving the algorithm's efficiency. To test the performance of our algorithm and our new pruning strategies, we improved the PHM algorithm to generate the PR-Miner algorithm. Experimental results show a significant efficiency of the new pruning strategies and demonstrate that CPR-Miner outperforms the PR-Miner algorithm in all aspects. • Periodicity and recency are introduced within closed high utility pattern mining. • New data structures are designed to store the utility, period, and recency information. • Novel pruning strategies are proposed to avoid unnecessary list joint operation. • The proposed algorithm gets significant results on several datasets. [ABSTRACT FROM AUTHOR]