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ETISplus Database Content and Methodology
Abstract
This technical report describes the content of the ETISplus database, and the methodologies used to create each dataset. It embraces the following datasets at European scale and regional level (NUTS-3):
Socio-economic Data; Networks (road, rail, waterway, air); Terminals (airports, freight terminals, ports); Impedances (air passengers, air freight, road passengers, rail passengers, freight transport (all modes)); Impacts; Freight demand (road, rail, waterway, aviation, maritime, Input/Output data, trade data); Passenger demand (aviation, bus and coach, car, rail, tram and metro, cycling and waterway).
... Finally, but most importantly, in order to support the transport-demand-centric charging infrastructure design according to the concept in Section 2.2, the layers include 100 s of millions of synthetic annual freight and long-distance private car trips across 26 countries in Europe based on the ETISplus project [37,38]. The details of the information sources and the synthetic route data generation process are described in the following paragraphs. ...
... During the ETISplus project, 17 participants gathered the annual flow volumes of passengers and freight transported throughout Europe from different EU and national sources. Then, they iteratively calibrated the parameters of a logistics and a travel model until the models' outputs closely aligned with observed data, e.g., known totals, average distances per ton, and measured traffic flows on the roads [38]. The main output of the calibrated ETISplus models are the origin-destination (OD) matrices per transport mode and trip purpose between 1,675 Nomenclature of Territorial Units for Statistics [40] (NUTS-3) regions [41]. ...
... 48 GWh/year to 867. 38 GWh/year, with the customer network demand rising substantially from 380.55 GWh/year to 503.04 GWh/year. The customer's gain in the BEV enablement is also highlighted in the horizontal stacked charts. ...
Charging infrastructure is the backbone of electromobility. Due to new charging behaviors and power distribution and charging space constraints, the energy demand and supply patterns of electromobility and the locations of current refueling stations are misaligned. Infrastructure developers (charging point operators, fleet operators, grid operators, vehicle manufacturers, and real-estate developers) need new methodologies and tools that help reduce the cost and risk of investments. To this extent we propose a transport-energy-demand-centric, dynamic adaptive planning approach and a data-driven Spatial Decision Support System (SDSS). In the SDSS, with the help of a realistic digital twin of an electrified road transport system, infrastructure developers can quickly and accurately estimate key performance measures (e.g., charging demand, Battery Electric Vehicle (BEV) enablement) of a candidate charging location or a network of locations under user-specified transport electrification scenarios and constraints and interactively and continuously calibrate and/or expand their network plans as facts about the deep uncertainties about the supply side of transport electrification (i.e., access to grid capacity and real-estate and presence of competition) are gradually discovered/observed. This paper describes the components and the planning support of the SDSS and how these can be used in competitive and collaborative settings. Qualitative user evaluations of the SDSS with 33 stakeholder organizations in commercial discussions and pilots have shown that both transport-energy-demand-centric and dynamic adaptive planning of charging infrastructure planning are useful.
... In order to ensure the transferability of the methodology to other sites and to take into account international traffic and the direction of travel of the trucks, a data set with synthetic traffic flows [44,45] of European road freight transport with origin-destination traffic is used. The resulting aggregated truck traffic flows of the synthetic traffic flows are based on ETISplus data [46,47] and Eurostat data [48] for truck traffic volumes in Europe. The origin-destination traffic describes the traffic flows with The number of trucks R(t) in the car park at a given time t is represented by the sum of all incoming trucks, which are still in the car park at that time t. ...
... In order to ensure the transferability of the methodology to other sites and to take into account international traffic and the direction of travel of the trucks, a data set with synthetic traffic flows [44,45] of European road freight transport with origin-destination traffic is used. The resulting aggregated truck traffic flows of the synthetic traffic flows are based on ETISplus data [46,47] and Eurostat data [48] for truck traffic volumes in Europe. The origin-destination traffic describes the traffic flows with the number of trucks per year between NUTS3 regions; thus, regional trips within the individual NUTS3 region are not considered. ...
In the forthcoming years, it is expected that there will be a notable increase in the market penetration of electrically powered trucks with the objective of reducing greenhouse gas emissions in the transport sector. It is therefore essential to implement a comprehensive public charging infrastructure along highways in the medium term, enabling vehicles to be charged overnight or during driving breaks, particularly in the context of long-distance transportation. This paper presents a simulation model that supports the planning and technical design of truck charging parks at German highway rest areas. It also presents a transferable mobility model for the volume of trucks and the parking times of long-distance trucks at rest areas. Subsequently, a simulation is offered for the purpose of designing the charging infrastructure and analysing peak loads in the local energy system. The potential of the models is demonstrated using various charging infrastructure scenarios for an exemplary reference site. Subsequently, the extent to which the charging infrastructure requirements and the service quality at the location depend on external conditions is explained. In addition, the influence of the range of offers and the business models on the efficiency of infrastructure use is established. Based on the findings, general recommendations for the design of truck charging parks at rest areas are then given and discussed.
... The dataset used in the context of this paper is gathered from the ETISPlus European FP7 research program (Szimba et al., 2012). The OD matrices for the year 2010 at the NUTS-2 regional level are used here. ...
Elasticities can be used in the context of transport policy decisions to estimate the impact of changes in transport costs, or the impact of new infrastructure on traffic or modal split, for example. Their estimation is often made difficult by the lack of available data, especially when the study area represents a large international territory. In this context, a modal choice model with three explanatory variables is presented. In a fairly standard way, transport costs and transit times are used. The originality of the model presented is that it also incorporates two accessibility measures and that the values for the three explanatory variables are computed using origin-destination matrices and digitized networks. Transport costs and transit times are calculated for each origin-destination relationship, type of commodity and transport mode, using a transport network model. The latest also provides the length of each relationship, which is used, along with the annual transported tons, to calculate two accessibility measures. The article explains why accessibility finds its place in the utility function used in the modal choice model and shows how it improves the model's performance. The model is further used to compute a set of direct and cross freight demand elasticities for road, inland waterways and rail transport with respect to a change in the total cost of transport on the European territory. The computed elasticities are compared with other values identified in the literature. The presented values can be considered as being credible and robust.
... The dataset represents an update of the train flows published in the European Transport Policy Information System (ETIS) project [24] and includes detailed information on the goods transported to or from Russia during the period from January 1 to January 5, 2012. ...
Graph neural networks (GNNs) have shown great promise in a variety of tasks involving graph data, including recommendation systems. However, as GNNs become more widely adopted in practical applications, concerns have arisen about their vulnerability to adversarial attacks. These attacks can lead to biased recommendations, potentially causing economic losses and safety risks. In this work, we consider an industrial application of recommendation systems for transport logistics and study their vulnerability to membership inference attacks. The dataset represents real train flows in Russia, published in the ETIS project. Experiments with three popular GNN architectures show that all of them can be successfully attacked even when the adversary has minimal background knowledge. Specifically, an attacker with access to only 1-2% of the actual data can successfully train their own GNN model to infer the membership of a shipper-consignee association in the training set with an accuracy over 94%. Our study also confirms that overfitting is the primary factor that influences the attack performance of recommendation systems.
... In total, more than 1.5 million directed traffic flows are available in the used dataset. The flow data are based on an EU project from 2010 (Szimba et al 2012) and have been updated using more recent statistics on road transport. Beyond that, a volume flow forecast for 2030 has been added. ...
Battery electric trucks (BETs) are the most promising option for fast and large-scale CO2 emission reduction in road freight transport. Yet, the limited range and longer charging times compared to diesel trucks make long-haul BET applications challenging, so a comprehensive fast charging network for BETs is required. However, little is known about optimal truck charging locations for long-haul trucking in Europe. Here we derive optimized truck charging networks consisting of publicly accessible locations across the continent. Based on European truck traffic flow estimates for 2030 and actual truck stop locations we construct a long-term charging network that minimizes the total number of required locations. Our approach introduces an origin-destination (OD) pair sampling method and includes local capacity constraints to compute an optimized stepwise network expansion along the highest demand routes in Europe. For an electrification target of 15% BET share in long-haul and without depot charging, our results suggest that about 91% of electric long-haul truck traffic across Europe can be enabled already with a network of 1,000 locations, while 500 locations would suffice for about 50%. We furthermore show how the coverage of OD flows scales with the number of locations and the size of the stations. Ideal locations to cover many truck trips are at highway intersections and along major European road freight corridors (TEN-T core network).
... Since homogeneous traffic counts for all European countries are rarely available and the standardization of country-specific data would be very burdensome, modelled European traffic flows [23] serve as our basis. The dataset represents an update of the truck traffic flows published in the European Transport policy Information System (ETIS) project [24] and contains projections between NUTS3 (Nomenclature des unités territoriales statistiques. Level 3: small regions, large cities)-regions in Europe. ...
Heavy-duty trucks account for 27% of the European greenhouse gas emissions in the transport sector. To decarbonize road freight transport, the European Union plans to build a fast charging network for trucks. This paper presents two scenarios, covering European highways with charging stations at regular intervals every 50 or 100 km along the most important highways. For each location, the required number of charging points at 15% battery electric trucking is calculated individually using queueing theory. A third scenario takes into account the infrastructure ramp-up in 2025 and assumes a share of 5% battery electric trucking in a network with a 100 km distance. We define a network of 660 (100 km distance) or 1468 stations (50 km distance). Depending on the scenario and the individual station, the projected number of charging points per station varies between 1 and 18 in 2030. The results give a first insight into what a fast charging infrastructure for trucks in Europe might look like. In particular, we show that large charging stations with more than ten charging points could be necessary in the next few years. This knowledge might help to design future charging infrastructure for electric road freight transport.
... The dataset used for the case presented in this paper is gathered from the ETISPlus European FP7 Research Program (Szimba et al., 2012). Combining data, analytical modeling with maps and an online interface for accessing the data, it provides an information system useful for assessing European transport policies. ...
Inland Waterway Transport (IWT) is pivotal for hinterland freight logistics, connecting numerous inland terminals with major deep-sea ports. Despite its significant advantages in cargo capacity and environmental sustainability, it remains underutilized due to operational inefficiencies, diminishing its competitive edge compared to road and rail transport. Addressing these challenges requires innovative solutions, yet existing studies often neglect to evaluate these innovations and their interactions within the broader IWT logistics framework and other transport modes.
This study bridges these gaps by introducing a comprehensive Discrete Event Simulation (DES) model to capture the dynamic interactions within the IWT logistics system, its interplay with other transport modes, and the potential impact of innovative measures on IWT performance. The model simulates the current container logistics system along the Rhine-Alpine (RALP) corridor, incorporating key elements such as cost-time calculations, transport mode selection, network flow allocation, and the assessment of IWT innovations.
The methodological framework and architecture of the DES model are presented, emphasizing verification and validation processes to ensure accuracy and reliability. The model outputs a network-wide analysis of the current IWT logistics system, examining mode split, cost, time, emissions, distance, and the interrelationships among these factors. This analysis serves as a benchmark for evaluating the effects of various innovative strategies on IWT performance.
By employing the DES methodology, this research advances the understanding of container IWT logistics, providing critical insights for stakeholders and policymakers. It evaluates the current performance of container IWT in the RALP corridor and identifies opportunities to improve the efficiency and competitiveness of container IWT.
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