Geospatial truck parking locations data for Europe.

This data article introduces a comprehensive dataset of real-world truck parking locations across Europe. The dataset comprises N = 19,713 designated parking sites classified according to public accessibility and suitability for heavy-duty trucks (HDTs). More specifically, core information comprises the truck stop category, latitude and longitude information, area size, and country assignment. Furthermore, additional information such as truck traffic flow volumes, proximity to the highway network, and land use information provide supplemental data on ambient conditions and thus enhance the contextual relevance of those locations. The dataset was systematically generated using OpenStreetMap (OSM) data, focusing on parking areas, rest areas, and fueling stations as predominant public truck parking sites. These locations were evaluated and filtered for truck accessibility and suitability and then complemented and validated using commercial truck routing / geocoding software. Further refinement was achieved by Mean-Shift clustering. The further integration of supplementary datasets increased the information level, and all clustered locations were labeled into four archetypal categories. Finally, filtering retained only confidently classified publicly accessible and truck-certified parking and service facilities. This dataset assists in finding real-world stop options for HDTs during national or international operations and identifying suitable and most attractive sites for deploying alternative charging or refueling infrastructures along the European transport network. Accordingly, it can serve as a valuable resource for research in traffic science, future energy systems, and alternative truck powertrains. Its added value extends to diverse stakeholders like Charge Point Operators (CPOs), truck manufacturers, logistics companies, and public authorities.

Synthetic European road freight transport flow data.

This data article describes a dataset on European road freight traffic. The dataset includes truck traffic flows between 1675 regions all over Europe. In addition to the road freight flows in tons as well as number of vehicles, the dataset also contains the shortest path between the respective regions on the European highway network (E-roads). Fifteen columns provide the following information for each pair of regions: (1) ID origin region, (2) name origin region, (3) ID destination region, (4) name destination region, (5) path in the E-road network, (6) distance from origin region to the E-road network, (7) distance within the E-road network, (8) distance from the E-road network to the destination region, (9) total distance, (10) road freight flow in tons for 2010, (11) road freight flow in tons for 2019, (12) road freight flow in tons for 2030, (13) truck traffic flow in number of vehicles for 2010, (14) truck traffic flow in number of vehicles for 2019, (15) truck traffic flow in number of vehicles for 2030. In addition, a table of nodes and a table of edges of the modelled E-road network is available. Finally, a list with supplementary information on the regions under consideration is given. In 2010, the ETISplus project collected Europe-wide freight volumes from various EU sources as well as from the EU countries and calibrated the resulting origin-destination matrices with measured traffic flows. For the dataset described here, the road freight volume was updated using Eurostat data and a forecast up to 2030 was added. The freight volume was converted into vehicles travelling. Subsequently, the highway network relevant for trucks was extracted from the ETISplus project and manually updated with the current E-road network. Finally, each origin-destination freight volume was allocated to the network using Dijkstra's algorithm. This provides a synthetically generated road freight traffic volume for each road section. The generated data provide an extremely relevant basis for the design of future road infrastructure in Europe, for example hydrogen refuelling stations or charging stations for electric trucks. Thus, the data are not only relevant for traffic science studies, but also of high importance for planners in practice.

Where is the EU headed given its current climate policy? A stakeholder-driven model inter-comparison.

Recent calls to do climate policy research with, rather than for, stakeholders have been answered in non-modelling science. Notwithstanding progress in modelling literature, however, very little of the scenario space traces back to what stakeholders are ultimately concerned about. With a suite of eleven integrated assessment, energy system and sectoral models, we carry out a model inter-comparison for the EU, the scenario logic and research questions of which have been formulated based on stakeholders' concerns. The output of this process is a scenario framework exploring where the region is headed rather than how to achieve its goals, extrapolating its current policy efforts into the future. We find that Europe is currently on track to overperforming its pre-2020 40% target yet far from its newest ambition of 55% emissions cuts by 2030, as well as looking at a 1.0-2.35 GtCO2 emissions range in 2050. Aside from the importance of transport electrification, deployment levels of carbon capture and storage are found intertwined with deeper emissions cuts and with hydrogen diffusion, with most hydrogen produced post-2040 being blue. Finally, the multi-model exercise has highlighted benefits from deeper decarbonisation in terms of energy security and jobs, and moderate to high renewables-dominated investment needs.