EVKG: An interlinked and interoperable electric vehicle knowledge graph for smart transportation system

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.     

Accuracy assessment of digital elevation models by means of robust statistical methods

Measures for the accuracy assessment of Digital Elevation Models (DEMs) are discussed and characteristics of DEMs derived from laser scanning and automated photogrammetry are presented. Such DEMs are very dense and relatively accurate in open terrain. Built-up and wooded areas, however, need automated filtering and classification in order to generate terrain (bare earth) data when Digital Terrain Models (DTMs) have to be produced. Automated processing of the raw data is not always successful. Systematic errors and many outliers at both methods (laser scanning and digital photogrammetry) may therefore be present in the data sets. We discuss requirements for the reference data with respect to accuracy and propose robust statistical methods as accuracy measures. Their use is illustrated by application at four practical examples. It is concluded that measures such as median, normalized median absolute deviation, and sample quantiles should be used in the accuracy assessment of such DEMs. Furthermore, the question is discussed how large a sample size is needed in order to obtain sufficiently precise estimates of the new accuracy measures and relevant formulae are presented.     

Recent advances in accessibility research: Representation,methodology and applications

 In this article we examine recent advances in accessibility research and their implications for future studies. We base our discussion on three intersecting dimensions that are useful for evaluating the contribution of recent studies: representation, methodology and applications. Various examples are selected to show that research concerned with representation and methodological problem solving is often applied to issues of broad concern in policy and planning. It is, however, not clear that the simultaneous treatment of representation, methodological and application issues has ever been fully worked out. The questions raised in this article may serve as a foundation for addressing issues pertinent to accurate representation, improved model building, and more rigorous applications in accessibility research. Received: 9 December 2002 / Accepted: 10 February 2003     

Using DORIS measurements for modeling the vertical total electron content of the Earth’s ionosphere

The Doppler orbitography and radiopositioning integrated by satellite (DORIS) system was originally developed for precise orbit determination of low Earth orbiting (LEO) satellites. Beyond that, it is highly qualified for modeling the distribution of electrons within the Earth’s ionosphere. It measures with two frequencies in L-band with a relative frequency ratio close to 5. Since the terrestrial ground beacons are distributed quite homogeneously and several LEOs are equipped with modern receivers, a good applicability for global vertical total electron content (VTEC) modeling can be expected. This paper investigates the capability of DORIS dual-frequency phase observations for deriving VTEC and the contribution of these data to global VTEC modeling. The DORIS preprocessing is performed similar to commonly used global navigation satellite systems (GNSS) preprocessing. However, the absolute DORIS VTEC level is taken from global ionospheric maps (GIM) provided by the International GNSS Service (IGS) as the DORIS data contain no absolute information. DORIS-derived VTEC values show good consistency with IGS GIMs with a RMS between 2 and 3 total electron content units (TECU) depending on solar activity which can be reduced to less than 2 TECU when using only observations with elevation angles higher than \(50^\circ \) . The combination of DORIS VTEC with data from other space-geodetic measurement techniques improves the accuracy of global VTEC models significantly. If DORIS VTEC data is used to update IGS GIMs, an improvement of up to 12  % can be achieved. The accuracy directly beneath the DORIS satellites’ ground-tracks ranges between 1.5 and 3.5 TECU assuming a precision of 2.5 TECU for altimeter-derived VTEC values which have been used for validation purposes.