Low-carbon road freight transport is pivotal in mitigating global warming. Nonetheless, electrifying heavy-duty vehicles poses a tremendous challenge due to high technical requirements and cost competitiveness. Data on future truck costs are scarce and uncertain, complicating assessments of the future role of zero-emission truck (ZET) technologies. Here we derive most likely cost developments for price setting ZET components by meta forecasting from more than 200 original sources. We find that costs are primed to decline much faster than expected, with significant differences between scientific and near-market estimates. Specifically, battery system costs could drop by 64% to 75% and fall below €150 kWh⁻¹ by no later than 2035, whereas fuel cell system costs may exhibit even higher cost reductions but are unlikely to reach €100 kW⁻¹ before the early 2040s. This fast cost decline supports an optimistic view on the ZET market diffusion and has substantial implications for future energy and transport systems.

Globally, road freight accounts for 40% of the CO₂ emissions in the transport sector, mainly from heavy-duty vehicles (HDVs). All the major truck markets have introduced fuel efficiency regulations for HDV, and the more ambitious regulations require the introduction of zero-emission HDV, for which battery electric trucks (BEVs) are a promising candidate. However, frequent long-distance trips require a dense public high-power charging network if BEV are to meet today's operating schedules in logistics. Here, we develop a model for public BEV high-power fast-charging that uses widely available traffic count data as input and combines this with on-site queueing models. We apply the model to Germany and obtain a fast-charging network where average waiting times do not exceed 5 min. For 15% BEV in the truck stock and 50% public charging, the model shows 267 charging locations, each with 2–8 charging points per location, for a dense network with 50 km distance between charging locations. We calculated 142 charging locations with 2–13 charging points for a wider network with 100 km distance between locations. Our results help to design future charging infrastructure for electric road freight transport.

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.