Electrification of Road Transport Infrastructure in the Context of Sustainable Transport Development and the Deployment of Alternative Fuels Infrastructure on the TEN-T Network in Poland

Abstract

Road transport constitutes a crucial element of the European economy, but it also generates significant external costs. In the process of reducing the impact of road transport on the environment and society, numerous actions are being undertaken to implement the concept of sustainable transport development in the Member States of the European Union. A key measure in this area is the introduction of low- and zero-emission propulsion systems in vehicles intended for passenger and freight transport. This article focuses on electric vehicles powered by battery energy storage systems. An essential component of these efforts is the development of alternative fuels infrastructure, which is expected to enable the operation of such vehicles by providing access to battery charging facilities. The development of infrastructure in the form of electric vehicle charging stations, initially concentrated in urban areas, has been extended to the network of European roads. The driving force behind this expansion is the European Parliament and the Council of the EU, which, on the basis of the Alternative Fuels Infrastructure Regulation (AFIR), stimulate the development of alternative fuels infrastructure along the TEN-T network. The aim of the article is to present selected challenges related to the electrification of road transport infrastructure in the context of the sustainable transport development concept and the construction of alternative fuels infrastructure along the TEN-T network. The research focuses on forecasting the demand for alternative fuels infrastructure along the A1 and A2 motorways, which form part of the TEN-T network within the territory of Poland. The research process stems from the implementation of the AFIR in the EU Member States.

1. Introduction

Road transport has dominated land freight transport in the European Union for decades. Despite numerous initiatives aimed at fostering modal shift and promoting the development of intermodal transport, the share of freight transported by road remains at a very high level. This is accompanied by substantial external costs that are not internalised in transport prices, including, among others, road accidents, congestion, air pollution, climate change, noise, land degradation, as well as the costs of crude oil extraction, refining and liquid fuel distribution. At the same time, the capital-intensive linear infrastructure of road transport, along with a large number of enterprises operating in the road freight transport market, constitutes a foundation for the functioning of the economies of many contemporary EU Member States [1]. The road transport sector cannot be easily or quickly replaced by other transport modes, not only due to its significant dominance but also due to the advantages it offers. The sector is facing increasing pressure to reduce its environmental footprint and align with the European Union’s long-term climate and energy objectives. Numerous strategic analyses confirm that transforming heavy-duty road transport is one of the most demanding and critical components of the EU decarbonisation agenda [2,3].

Transport policy based on the concept of sustainable transport development seeks to reconcile the role of road transport within the national transport system with the need to reduce its negative impact on the environment, public health and the climate. The European Green Deal and the Fit for 55 package outline ambitious goals, including reducing greenhouse gas emissions by at least 55% by 2030 compared to 1990 levels, achieving climate neutrality by 2050, increasing the share of renewable energy in transport, developing alternative fuels infrastructure, accelerating the electrification of transport and reducing the emissions intensity of freight transport through improved energy efficiency and modal shift [4,5]. Achieving these objectives requires a transition towards cleaner vehicle technologies, supported by regulatory instruments, market incentives and coordinated infrastructure development [6,7].

In addition, international research confirms that the decarbonisation of heavy-duty transport requires coordinated actions combining vehicle electrification, infrastructure development and systemic policy adjustments. Sun et al. (2024) [8] provide a comprehensive analysis of global pathways towards climate-neutral freight transport, demonstrating that large-scale deployment of zero-emission trucks must be accompanied by an equally ambitious expansion of charging and energy supply infrastructure. Their findings support the relevance of analysing the Polish TEN-T core network, as the challenges faced by Poland are consistent with broader international trends observed in road freight decarbonisation efforts [8].

The acceleration of transport electrification is facilitated by the development of alternative fuels infrastructure, particularly high-power charging systems for electric heavy goods vehicles (e-HGVs). The Alternative Fuels Infrastructure Regulation (AFIR) imposes the implementation of charging and refuelling infrastructure the TEN-T network, which extends across the territory of Poland and includes the Baltic–Adriatic and North Sea–Baltic transport corridors [9]. The evolution of the TEN-T corridors, including those crossing Poland, reflects the broader EU objective of creating an integrated, efficient and interoperable transport network that supports both economic performance and environmental goals [10].

The transition pathways currently considered for heavy-duty transport include battery-electric trucks, hydrogen fuel-cell vehicles, electric road systems and advanced low-emission internal combustion technologies. Comparative strategic studies highlight that the feasibility and competitiveness of these technologies depend on infrastructural availability, energy system characteristics and the anticipated rate of technological progress [3,11]. Case studies from various regions of Europe and beyond further illustrate that electrification potential and infrastructure requirements vary depending on national conditions, but consistently point to the need for rapid and coordinated infrastructure development to enable long-haul decarbonisation [12,13]. In this context, Poland plays an important role in the European freight system. The development of charging infrastructure on Polish segments of the TEN-T corridors is therefore essential not only for national mobility but also for ensuring the continuity and sustainability of cross-border freight flows. Estimating future infrastructure needs requires integrating transport demand forecasts, assumptions regarding the electrification rate of heavy-duty vehicles and the technical parameters of charging systems [10,12].

Electrification of freight transport is expected to reduce external costs associated with emissions and air pollution. In this context, the aim of the article is to present a forecast of the demand for alternative fuels infrastructure along the TEN-T network in Poland, following the assumptions of sustainable transport development, with an emphasis on external transport costs and the electrification of road freight transport.

The remainder of the article is structured as follows. The second part provides an analysis of the body of knowledge on the environmental impact of transport, including the concept of sustainable transport development, with emphasis on external costs as well as key aspects of charging infrastructure development. The third part presents road transport in the context of external transport costs and the electrification of transport. The fourth part offers a forecast of infrastructure needs for alternative fuels along the A1 and A2 motorways, which form part of the linear infrastructure of the TEN-T network in Poland. A key element of the fourth part is the process of transport electrification in accordance with the AFIR, with a focus on the TEN-T corridors located in Poland.

This paper focuses on the A1 and A2 motorways as a representative case study within the Polish section of the TEN-T core network. These two corridors constitute the fundamental longitudinal and latitudinal axes of the national transport system: the A1 runs vertically, dividing Poland into eastern and western halves while connecting Northern Europe (Sweden, Denmark) with Southern and Southeastern Europe (Czech Republic, Slovakia, Adriatic region), whereas the A2 crosses the country horizontally, linking Western Europe (Germany, Benelux) with Eastern Europe (Belarus, Baltic region). As such, both corridors carry exceptionally high volumes of heavy-duty road transport, including long-distance international freight flows that are structurally responsible for a disproportionate share of external costs—particularly greenhouse gas emissions, air pollution, noise, and congestion. By analysing the required charging infrastructure for battery-electric heavy-duty vehicles on these two critical axes, the study operationalises the broader sustainability framework: it quantifies how the fulfilment of AFIR obligations and the electrification of freight transport along strategic TEN-T corridors can meaningfully reduce externalities in one of the most carbon-intensive segments of the transport sector. Thus, the A1–A2 case study serves as a concrete and policy-relevant demonstration of how infrastructure planning can bridge the gap between EU-level decarbonisation targets and national-level implementation challenges.

2. Review of the Literature on Transport and Sustainable Development

2.1. Sustainable Transport Development

Reflection on the environmental consequences of economic growth gained international attention in the 1960 s, when the “Problems of Human Environment” report [14] highlighted accelerating degradation of natural resources and the need for coordinated global action. These concerns later formed the basis for the “Brundtland Report Our Common Future” report [15], which introduced the concept of sustainable development as meeting present needs without compromising the ability of future generations to meet their own. Since then, sustainable development has become a central principle in United Nations and European Union policy frameworks [15], and its application in the transport sector requires balancing economic efficiency, environmental responsibility and social equity. In practice, this means reducing the harmful impacts of transport operations on ecosystems, public health, and quality of life, while ensuring accessibility and supporting economic activity [16]. Achieving this balance has become even more pressing in the context of the European Green Deal and the Fit for 55 package [4,5], which frames transport decarbonisation as a central policy objective.

Within this framework, one of the most persistent challenges is the structural dominance of road transport in the European modal split, in both freight and passenger transport. This dominance results from short distances between major economic centres, high organisational flexibility, door-to-door delivery capability, evolving logistics structures and rising expectations regarding delivery time and service reliability. In many EU Member States, including Poland, long-term underinvestment in rail and inland waterways has further reinforced the role of road transport, making it the primary generator of negative externalities such as air pollution, noise, congestion, accidents and climate impacts [17,18,19]. The scale of this dominance is illustrated in Figure 1, presenting the share of transport modes in Poland in 2023 [1].

Research conducted by Sobota [20] confirms that progress in sustainable transport strongly depends on the coherence between long-term strategic objectives and the actual deployment of low- and zero-emission vehicle fleets. Furthermore, Murawski et al. [21] indicate that in urban distribution systems, external factors such as fuel price increases and tightening regulatory requirements significantly accelerate the economic competitiveness of environmentally friendly vehicles. At the same time, sustainable transport requires the systematic integration of environmental criteria into operational and planning processes. This has been demonstrated, for example, by modelling approaches that explicitly incorporate emission intensity into traffic organisation and optimisation tasks in national networks. Analyses for Poland and other EU countries confirm this tendency, showing a persistent growth of international and domestic road freight transport over more than a decade [22,23].

In recent years, efforts to reduce the environmental impact and energy demand of road freight transport have intensified. At the European level, Domagała and Kadłubek [12] show that comprehensive assessments of economic, energy and environmental efficiency highlight the scale of transformation needed to align the freight transport sector with climate and sustainability goals. Using geospatial modelling, Samet et al. [13] reveal substantial potential for the electrification of medium- and heavy-duty vehicle fleets in Finland and Switzerland. In the United States. In the United States, Sen et al. [24] report that optimisation-based studies analyse sustainable fleet composition while balancing environmental, economic and operational constraints. In China, Zhang et al. [25] demonstrate that long-term decarbonisation pathways for heavy-duty trucks include a comparative analysis of the performance of battery electric vehicles, plug-in hybrid vehicles, hydrogen fuel cell vehicles and overhead line-powered vehicles. Aryanpur and Rogan [11] also confirm that the large-scale deployment of zero-emission trucks depends on the coordinated provision of adequate charging or refuelling infrastructure prevent carbon emission dependency.

From a technological perspective, numerous studies have concluded that no single solution can achieve deep decarbonization of heavy-duty road transport on its own and require a combination of complementary approaches [2]. Battery electric vehicles (BEVs), as well as e-HGVs, offer high energy efficiency but require dense networks of high-power chargers and a reliable power supply. Feng et al. [26] show that energy consumption in real-world highway conditions is significantly higher than in standard test cycles, with losses in the driveline and ancillary equipment accounting for the majority of the total demand. Hydrogen fuel cell electric vehicles (FCEVs) may be suitable for long-distance operations that require short refuelling times. However, their feasibility depends on access to low-carbon hydrogen and an extensive refuelling infrastructure [27,28]. Electric road systems (ERS), which provide continuous energy via overhead or ground-based solutions, reduce the demand on vehicle batteries and show promising results for high-density long-distance corridors [27,29,30]. Lasota et al. [31] and Wasiak et al. [32] indicate that transitional internal combustion technologies with lower emissions, such as LNG and CNG, can provide only limited emission reductions, while their long-term decarbonization potential remains constrained. Li et al. [3] show that comparative analyses reveal the suitability of individual solutions to depend strongly on national energy mixes, freight demand structures, and operational constraints. Life-cycle studies further show that BET and FCEV offer clear environmental benefits only when energy systems undergo significant decarbonization [33,34]. According to Burchart-Korol and Folęga [35], life-cycle assessments conducted for Poland reveal that the carbon and water footprint of electric vehicles strongly depends on the structure of the electricity mix. Hydrogen production technologies also determine the environmental performance of fuel cell vehicles, which can have both advantages and disadvantages depending on the hydrogen source [28].

Beyond vehicle technologies, operational and systemic factors have a significant impact on the environmental performance of freight transport. Intangible constraints, such as reduced payload, increased charging or refuelling times, and technology acceptance, influence fleet operators’ decisions and can hinder the implementation of zero-emission vehicles [11]. Ghandriz et al. [36] report that automation and advanced logistics systems can reduce the total cost of ownership (TCO) and extend the range of electric trucks, thereby increasing their competitiveness. Selected studies provide additional insights. For example, analyses of vehicle-in-motion weighing in Poland reveal significant variation in load factors, empty runs, and vehicle classes, creating opportunities for targeted efficiency improvements [37]. In Morocco, Jelti and Saadani [38] identify fossil fuel dependence and fleet inefficiency as key drivers of alternative powertrain development, while Tarudin and Adlan [39] confirm that optimised operating strategies in Malaysia reduce costs and support sustainable development goals.

Collectively, these examples highlight that freight transport sustainability and its decarbonisation require coherent policy packages integrating:

• Support for the deployment of low- and zero-emission vehicle technologies;
• Parallel decarbonisation of national energy systems;
• Targeted development of infrastructure aligned with freight movement patterns.

2.2. Road Transport as a Generator of External Costs

The external cost of transport refers to social and environmental impacts that are not included in market prices. These remain one of the main challenges for sustainable transport. Road transport generates the highest external costs because it is characterised by the highest traffic density in urban and interurban areas, emits pollutants and noise near residential areas, and is associated with a high risk of accidents due to the large number of users. Even with improved energy efficiency and stricter emission standards, the total impact of road passenger and freight transport remains higher than other modes of transport, especially during peak hours in cities.

Reliable valuation of external costs requires consistent methods and assumptions. Health impacts are estimated using a cost-of-illness and willingness-to-pay approach, taking into account the value of statistical life and quality-adjusted life years. Environmental costs utilise damage functions for unit emissions, CO₂ equivalent factors, and the social cost of carbon dioxide emissions. Congestion and noise are valued using travel time costs, mobility demand functions, and population exposure. Research on external costs is challenging because it requires sophisticated models integrating data from transport economics, epidemiology, atmospheric science, and welfare economics. It also requires detailed data on traffic volume, fleet structure, traffic conditions, emission sources, population density, and noise exposure, which are not always available or comparable. Each external cost category uses a distinct valuation method, resulting in significant differences in results. Unit costs depend on the discount rate, the human life valuation method, the social cost of carbon dioxide, and local conditions. Such studies are expensive and time-consuming. They are typically conducted by specialised research centres within international institutions, such as the European Commission, the OECD, or the World Bank. They require access to GIS models and databases on emissions and economics. For this reason, comprehensive and comparable studies remain limited, and most available reports provide aggregated values for individual modes of transport. Comprehensive European assessments consistently show that road transport accounts for the vast majority of external costs, driven by high traffic volumes, congestion, and emission levels [40].

Table 1. Selected Studies and Reports on External Transport Costs.

Authoring Body	Report Title	Publication Year
European Commission (DG MOVE); CE Delft, Ricardo, TRT	Handbook on the External Costs of Transport	2019/2020
European Commission (DG MOVE); CE Delft	Handbook on External Costs of Transport (previous edition	2014
CE Delft	External Costs of Transport in Europe	2011
INFRAS; CE Delft; Fraunhofer ISI (for UIC)	External Costs of Transport in Europe: Update study for 2008	2018
OECD/ITF	Decarbonisation and the Pricing of Road Transport	2022
OECD/ITF	Transport Outlook	2021; 2023
World Bank	Urban Road Transportation Externalities: Costs and Choice of Policy Instruments	2010
European Commission (DG MOVE)	Internalisation of Transport External Costs (inventory)	2008–2018
INFRAS (EU project)	Transport-related costs in the EU and other countries	2025 (ongoing)
SUERF Policy Note	External Costs of Freight Transport—Relevance and Quantification	2021

presents the most important studies and reports to date on external transport costs in a European context.

This pattern is reinforced across the scientific literature. This pattern is reinforced across the scientific literature. Multi-criteria and simulation-based analyses also confirm that heavy-duty road transport is a significant contributor to noise, climate impacts, and congestion-related inefficiencies [7,29,41,42]. Even under optimistic electrification scenarios, insufficient infrastructure availability may sustain high levels of externalities [6]. In the area of safety, Izdebski et al. [43] illustrate, through accident probability modelling for the transport of dangerous goods, how risk aspects in road networks can be quantified, highlighting the importance of aligning routing, infrastructure planning, and safety considerations within sustainable transport frameworks. From an environmental perspective, the life-cycle approaches already mentioned are increasingly used to evaluate alternative propulsion systems, including LNG, CNG, battery-electric, and hydrogen technologies [28,31,32]. According to Kemperdick and Letmathe [44] and Syré and Göhlich [34], battery electric long-haul trucks, particularly when integrated with electric road systems, yield markedly lower life cycle emissions than diesel or hydrogen systems, underscoring their capacity to mitigate climate externalities. In this context, already mentioned systems-level analyses by Aryanpur and Rogan [11] also highlight the role of operational barriers, showing that intangible costs, such as recharging time, payload limitations, and user acceptance, can slow the uptake of zero-emission trucks, indirectly prolonging high external climate costs. These studies demonstrate that environmental impacts also depend on the energy mix, operational profile, and supply chain characteristics, which should also be considered in external calculations.

Research on modal comparison highlights road freight transport as a significant generator of external costs. Koba et al. [45], based on analysis using the INCONE60 model, show that road freight produces external costs several times higher than maritime transport, and that modal optimisation can reduce external burdens by up to 80%. A corridor level analysis by Jonkeren et al. [46] confirms that shifting significant shares of freight to rail or inland waterways yields measurable reductions in greenhouse gases, air pollution, noise, accidents and congestion, leading to substantial annual savings in external and infrastructure costs.

Despite these differences, one conclusion is common to all analyses: road transport clearly dominates the modal structure of external transport costs. In every major study, regardless of the methodology used, this mode accounts for the largest share of total external costs, both at national and EU levels. This results from its dominant share in transport performance, extensive spatial coverage, high unit emissions, congestion levels and its strong impact on traffic safety (

Table 2. External Costs by Land Transport Modes in the EU in 2016 (Nominal and Percentage Share).

Land Transport Mode	Total External Costs (mld EUR)	Share in External Costs of Land Transport (%)
Road transport	≈722.7	≈97.1
Rail transport	≈19.4	≈2.6
Inland waterway transport	2.9	≈0.4
Total inland transport	≈745	100

).

Road transport, encompassing both freight and passenger traffic, is therefore the key area of transport-related environmental and societal impact, and its contribution to external costs is several times higher than that of other modes, such as rail. Furthermore, due to the low substitutability of air and maritime transport, its share within the structure of land transport is even greater.

The scale of the external costs highlights the necessity for coordinated and strategically targeted measures to reduce energy consumption, pollutant emissions, noise exposure and accident risks. In this context, the electrification of heavy-duty transport along major motorway corridors becomes an important activity for mitigating externalities. Achieving this requires demand-aligned planning of charging infrastructure that accounts for freight flows, technological requirements and energy system capacities, ensuring progress toward long-term sustainable transport objectives.

2.3. Charger Network Development for e-HGV

The deployment of charging infrastructure for e-HGVs presents a complex challenge, significantly different from that for passenger cars. This complexity arises from the high-power requirements (Megawatt Charging Systems—MCS), the need to coordinate charging windows with mandatory driver rest periods, and the important impact on the power grid. A review of recent literature highlights three main research areas relevant to this issue: optimisation of station location, demand forecasting methods, and the specific regulatory and technological context.

Selecting optimal locations for charging stations requires balancing traffic needs with power grid constraints. Literature proposes various optimisation models that integrate these conflicting objectives. Wang et al. [47] and Zhang et al. [48] suggest multi-objective optimisation models that maximise captured traffic flow while minimising network power losses and voltage deviation. These approaches often employ advanced algorithms, such as binary particle swarm optimisation, to handle the complexity of integrating transportation and energy networks. Other models, such as a multi-period bi-objective optimisation model [49], aim to minimise investment costs and maximise demand coverage using highway traffic data.

In addition to purely mathematical models, Multi-Criteria Decision-Making methods are widely applied to include qualitative factors. Zhao and Li [50] propose a framework that integrates economic, social, and environmental perspectives into the siting process, utilising the Fuzzy Delphi and VIKOR methods. Similarly, Husinec et al. [51] emphasise the role of environmental sustainability criteria in optimising charging locations for freight transport. Improved genetic algorithms [52], combined with simulated annealing and adaptive intersection crossover operators, are used to optimise the siting of charging stations by considering investment, maintenance, and user access costs. Moreover, Monte Carlo Simulation [53] is used to simulate traffic demand and battery data, determining optimal sites for charging stations that ensure vehicles can access them without depleting their batteries.

Recent studies also highlight the shift towards data-driven approaches [54]. The use of GPS trajectory data from electric trucks allows for more precise siting based on actual vehicle movements rather than theoretical models [55]. Ingelstrom et al. [56] demonstrate that agent-based simulations, which track individual trucks, enable the analysis of specific power flows and the optimisation of infrastructure based on real-world energy needs.

Accurate dimensioning of charging hubs depends on precise forecasting of electricity demand. While traditional statistical methods (e.g., time series analysis) are still in use, they often struggle with the spatial and temporal complexities of highway networks [57]. Consequently, researchers are increasingly turning to Machine Learning (ML) techniques. Models such as Graph Convolutional Networks (GCN) and Transformer networks have been developed to capture spatial relationships between charging stations and long-term temporal dependencies [58,59]. A critical aspect of forecasting for e-HGVs is the integration of spatial and temporal analysis. Li et al. [60] and Zheng et al. [61] emphasise that dynamic road network models, which incorporate real-time traffic data, are essential for predicting peak demand times. This is particularly relevant for heavy-duty transport, where charging demand is clustered due to regulation-driven driving schedules.

Furthermore, Geographic Information Systems (GIS) are extensively used to analyse spatial data [62,63], visualise energy demand distribution, and identify optimal grid connection points by fusing traffic patterns with socioeconomic data [64,65] or even weather conditions [66].

In the European context, the siting process is increasingly shaped by AFIR requirements. For example, Mazur et al. [67] propose a law-based method for identifying optimal locations along the TEN-T network that incorporates regulatory constraints, characteristics of the national power system, and existing parking infrastructure. Their approach demonstrates how legal and infrastructural conditions can refine optimisation outputs and highlights 188 strategically significant locations for high-power charging in Poland. The primary barrier identified in the literature is grid capacity [68]. Burges et al. [69] warn that high-power charging stations (especially MCS) will require substantial grid upgrades and, in many cases, direct connections to high-voltage networks. Hassan et al. [68] further note the potential impact of heavy-duty vehicle electrification on distribution system stability. Studies focusing specifically on national regulatory frameworks further highlight the influence of legislation on infrastructure planning. For example, Sendek-Matysiak and Pyza [70] demonstrate that legal requirements alone may result in a very limited spatial distribution of charging sites, underscoring the need to combine regulatory guidelines with broader optimisation and demand-driven analyses. Beyond the technical challenges, a broader systems perspective is increasingly seen as necessary. Chamier-Gliszczynski et al. [71] propose a conceptual model for the energy transformation of road transport infrastructure, including indicators for assessing AFIR implementation, positioning charging infrastructure development as part of a wider, systemic transformation of transport and energy networks.

From an economic perspective, the viability of MCS infrastructure remains a concern. Otteny et al. [72] point out that high investment costs combined with initially low utilisation rates may lead to high charging prices, potentially slowing down adoption. However, technological advancements, such as smart charging systems and microgrids, are being explored to mitigate these costs [73,74].

Finally, simulation studies [75,76] suggest that optimising infrastructure distribution based on multi-agent simulations could reduce the required number of high-power charging points by up to 50%, highlighting the importance of strategic planning over simple coverage.

In conclusion, developing e-HGV infrastructure extends beyond simple siting issues, representing a multidimensional challenge that includes power grid constraints, logistic solutions, and regulatory frameworks. The existing literature suggests that the key to achieving efficiency lies in transitioning from theoretical models to data-driven approaches and advanced optimisation algorithms. The successful deployment of charging infrastructure must be regarded as an integral part of the broader energy-transportation system.

3. Road Transport in the Context of External Transport Costs and Transport Electrification

3.1. Materials and Methods

The electrification of road transport infrastructure analysed in this article refers to infrastructure adapted to serve heavy-duty vehicles performing freight transport operations. The electrification process stems from the requirements set out in the AFIR and concerns the development of alternative fuels infrastructure. The infrastructure considered is intended to support electric heavy goods vehicles (e-HGVs) along the TEN-T network. In accordance with AFIR, charging points for e-HGVs are to be established to meet the demand generated by heavy-duty vehicle traffic operating on the TEN-T network. These activities are consistent with the concept of sustainable transport development, which includes, among other goals, reducing the share of conventional road transport in the European transport structure. Within the range of initiatives undertaken in this area, transport electrification constitutes a key element of actions aimed at reducing the environmental impact of road transport [77].

The objectives of the research process relate to presenting essential aspects of sustainable transport development, including the identification of external transport costs, the process of electrifying road transport infrastructure, and forecasting the demand for alternative fuels infrastructure along the TEN-T network located in Poland. The research method applied in the article is a case study analysis of external transport costs and a forecast of infrastructure needs for alternative fuels along the A1 and A2 motorways, which form part of the TEN-T network in Poland. The implementation of the empirical research was divided into three stages:

• Stage 1—defining the concept of sustainable transport development;
• Stage 2—identifying external costs of road transport;
• Stage 3—forecasting the demand for alternative fuels infrastructure along the road network, with a case study based on the A1 and A2 motorways, which form elements of the TEN-T network in Poland.

The methodological approach used in this study departs from existing European analyses of e-HGV charging infrastructure in several important ways. First, while most EU-level studies—such as those prepared by the ICCT or Transport & Environment—model long-distance freight flows at the continental scale and estimate infrastructure demand on aggregated European corridors, the present research applies a bottom-up corridor-specific methodology tailored to the Polish section of the TEN-T network. This includes integrating national traffic intensity datasets (GDDKiA Traffic Census and TransStat API), distinguishing corridor-level heavy-duty vehicle flows from national averages and calculating charging demand at the level of individual motorway segments. Second, unlike European studies that typically assume a standardised distribution of charging hubs, this analysis incorporates Poland-specific constraints such as the highly uneven distribution of motorway service areas (MOPs), the spatial structure of logistics nodes, and the strategic intersection of the A1 and A2 corridors near Stryków, one of the country’s largest and fastest-growing logistics clusters. Third, the study provides multiple electrification scenarios (5–100%), which goes beyond the single or limited-scenario modelling commonly found in European research. In these ways, the findings complement existing European literature by demonstrating how EU-level regulatory requirements (AFIR) translate into real-world infrastructural needs within the spatial, infrastructural and economic conditions of a specific Member State.

In order to ensure transparency and reproducibility of the analysis, the core assumptions applied in the modelling framework require explicit justification. First, the energy-consumption parameter for electric heavy-duty vehicles (e-HGVs) was set within the range reported by recent European field trials and simulation studies, which typically indicate values between 1.1 and 1.5 kWh/km for long-haul operations. This range reflects tested vehicle architectures, realistic payloads, and motorway speed conditions, and is consistent with large-scale assessments conducted by the European Automobile Manufacturers’ Association (ACEA) and the Joint Research Centre (JRC). The central value used in the model (e ≈ 1.3 kWh/km) therefore represents a widely accepted benchmark for long-distance battery-electric trucking.

Second, the assumed charging window duration is grounded in behavioural and regulatory patterns governing HGV operations. Drivers’ resting time regulations under Regulation (EC) No 561/2006 generate temporal clustering of demand, leading to identifiable peak charging periods. Empirical observations from existing pilot charging sites, as well as modelling studies of long-haul logistics scheduling, indicate that only a limited share of the 24 h cycle is practically available for high-power charging. Consequently, the adopted window reflects an operationally realistic timeframe that captures the constraints associated with mandatory rest breaks, fleet dispatching cycles, and uneven temporal distribution of freight movements. By presenting multiple window lengths, the model also accounts for uncertainty in user behaviour and technological development, enabling scenario-based interpretation of the results.

3.2. External Transport Cost Identification

Total external transport costs are highly dependent on the transport mode; however, within road transport itself, there is significant variation among different vehicle categories. These differences arise from both the nature of vehicle use and technical parameters such as vehicle mass, type of propulsion, load capacity, number of passengers, and typical operating conditions (urban areas, motorways, rural roads). Consequently, unit external costs expressed in euros per vehicle-kilometre (EUR/vkm), passenger-kilometre (EUR/pkm) or tonne-kilometre (EUR/tkm) vary substantially depending on the vehicle type and the transport function it performs.

As shown in

Table 3. External Road Transport Costs by Vehicle Category.

Vehicle Type	Total External Costs (bn EUR)	Share of External Road Transport Costs (%)	Main Sources of External Costs
Passenger cars	477.6	66.1	Congestion, air pollution, accidents, climate change
Motorcycles	46.6	6.5	Accidents, noise, air pollution
Light commercial vehicles (LCVs)	65.9	9.1	Air pollution, noise, climate impact
Heavy goods vehicles (HGVs)	94.0	13.0	Climate change, noise, accidents, infrastructure wear
Buses and coaches	38.6	5.3	Noise, air pollution, climate change
Total	722.7	100	-

, the structure of external costs within road transport in the European Union, showing the extent to which individual vehicle types contribute to the total environmental and social burdens generated by this mode. The data come from the Handbook on the External Costs of Transport. Version 2019—1.1, prepared for the European Commission (DG MOVE) by the CE Delft, Ricardo and TRT consortium, which provides a comprehensive valuation of external transport costs based on 2016 prices [78].

The largest share of external road transport costs is attributable to passenger cars, which account for approximately two-thirds of the total (66.1%). This stems primarily from their widespread use in passenger mobility, especially in urban agglomerations and suburban areas, where high traffic intensity generates considerable congestion, air pollution and accident costs. Despite technological progress and increasingly stringent emission standards, the overall social costs associated with the use of passenger cars remain very high, as the growth in individual mobility offsets the environmental efficiency gains.

The second-largest contributor is heavy goods vehicles (HGVs), responsible for around 13% of total external road transport costs in the European Union. Although this share is much lower than that of passenger cars, it is highly significant from the perspective of marginal cost structures, as each additional kilometre travelled by a heavy-duty vehicle generates relatively higher social and environmental costs compared to other road vehicle categories. The main sources of these costs include greenhouse gas and pollutant emissions, noise, infrastructure wear and tear, and road accidents. This segment is currently at the centre of EU transport and climate policy.

Other vehicle categories (light commercial vehicles (LCVs), buses and coaches, and motorcycles) play a smaller yet non-negligible role in the structure of external costs. Light commercial vehicles (approx. 9%) are gaining importance due to the development of urban logistics and e-commerce, while buses (approx. 5%) play an important role in public transport, where external costs per passenger are significantly lower than in individual motorised transport (Table 3) [79].

The conclusions drawn from this analysis clearly indicate that, despite the dominance of passenger transport in the total external costs, it is road freight transport, as the second largest contributor to external costs, that should constitute the primary focus of further analysis in the context of the implementation of the AFIR and the pursuit of the objectives set out in the European Green Deal and the Fit for 55 package.

Considering the adopted research scope, the following part of the discussion focuses on road freight transport in Poland, as it represents a key component of the national transport system and simultaneously one of the main sources of external costs.

When compared with the average results for the European Union, the structure of external costs of road transport in Poland shows both certain similarities and notable quantitative differences resulting from the specific characteristics of the national transport system. In most EU Member States, road transport accounts for between 70% and 80% of all external costs of the transport sector, and this share is particularly high in countries where road transport dominates the modal structure. Poland is no exception (according to data from the Handbook on the External Costs of Transport, DG MOVE, CE Delft, 2019), the total external costs of road transport in Poland exceed EUR 30 billion annually, representing more than three quarters of all external costs in the national transport system (Figure 2) [80].

The data presented in Figure 2 indicate that road transport clearly dominates the structure of external transport costs in Poland, confirming the high dependence of the national economy on road freight and passenger transport, as well as its significant impact on the environment, public health and road safety. The largest share of external road transport costs is attributable to air pollution emissions, road accidents and congestion—three factors directly linked to high traffic intensity and the low degree of internalisation of environmental costs in transport service prices. A relatively smaller, though still significant, share is generated by noise, environmental degradation and energy production (the so-called well-to-tank phase), which collectively portray road transport as the principal source of negative externalities in Poland.

As mentioned earlier, comparative studies of external transport costs by transport mode are relatively scarce. The most comprehensive study for Poland refers to 2016; therefore, it is essential to apply an inflation adjustment factor to update the values in order to ensure comparability. According to data published by the Ministry of Finance, the price increase coefficient for Poland for the period 2016–2024 amounts to K = 1.51 (

Table 4. External Transport Costs in Poland in 2016 and 2024 (Adjusted Using Inflation Coefficient K = 1.51).

Transport Mode	Costs in 2016 (bn EUR)	K = 1.51	Costs in 2024 (bn EUR)
Road transport	30.2	×1.51	45.6
Rail transport	3.8	×1.51	5.7
Air transport	3.2	×1.51	4.8
Maritime and inland waterway transport	2.3	×1.51	3.5
Total	39.5	-	59.6

).

In the next stage of the analysis, unit external costs of road freight transport in Poland are presented. These costs make it possible to assess the level of environmental and social burdens per unit of transport performance. The data provide an extension of the earlier estimates of total external costs and allow for a more precise comparison of the efficiency of different types of road transport vehicles. They include both heavy goods vehicles, which form the basis of medium- and long-distance freight transport, and light commercial vehicles, which play a key role in urban logistics and the so-called last mile. The inflation-adjusted unit external costs, expressed in euro cents per tonne-kilometre, are presented in

Table 5. Unit External Costs of Road Freight Transport in Poland (Euro cents per tkm).

Vehicle Type	Accidents	Air Pollution	Climate (GHG)	Noise	Congestion	WTT (Well-to-Tank)	Environmental Degradation	Total (ct/tkm)
Heavy goods vehicles (HGVs)	0.48	1.00	0.87	0.25	1.15	0.21	0.13	4.09
Light commercial vehicles (LCVs)	0.36	0.72	0.63	0.19	0.92	0.17	0.11	3.10
Average for road freight transport (HGV + LCV)	0.45	0.89	0.77	0.22	1.05	0.20	0.12	3.70

.

The structure of external cost sources in Poland is similar to that of the European Union as a whole, yet differs in their relative significance. In Western European countries, greenhouse gas emissions and congestion account for the largest share, whereas in Poland a considerable proportion of costs is generated by air pollution and road accidents. This is a consequence of an ageing vehicle fleet, the high share of international transit traffic, and lower infrastructure safety standards. As a result, despite improvements in transport efficiency, road transport in Poland continues to be the largest source of negative externalities in the economy, and its share in environmental and social costs exceeds that of the industrial or energy sectors.

This comparison indicates that Poland is at a stage where the further development of road transport requires systemic support for technological and infrastructural transformation. In the context of achieving the objectives of the European Green Deal, the Fit for 55 package, and the implementation of the Alternative Fuels Infrastructure Regulation (AFIR), this implies the need to accelerate emission reduction in the heavy-duty vehicle segment, expand charging infrastructure, and gradually internalise external costs within fiscal and pricing policies for road transport.

At the same time, it should be emphasised that while the objectives of AFIR have significant potential to reduce external costs related to air pollution, greenhouse gas emissions and, to some extent, noise, they do not cover all cost categories. Transport electrification does not directly affect congestion levels or the number of road accidents, which also constitute a substantial share of external costs in Poland.

In the next step of the analysis, only external costs related to road freight transport are considered, divided into two main vehicle segments: light commercial vehicles (LCVs) and heavy goods vehicles (HGVs). This approach allows for a precise assessment of the cost structure generated by both categories, which perform different functions within the logistics system—LCVs in distribution and urban transport, and HGVs in long-distance and international freight transport. The application of the inflation adjustment factor (K = 1.51), reflecting cumulative inflation in Poland, enabled the recalculation of values to 2024 price levels, thus enhancing the current analytical relevance of the data. The results are presented in

Table 6. Total external costs in Poland—road freight transport (LCV, HGV; mld EUR).

Cost Category	LCV 2016	HGV 2016	Total 2016	LCV 2024 (K = 1.51)	HGV 2024 (K = 1.51)	Total 2024
Accidents	0.012	2.598	2.611	0.018	3.924	3.942
Air pollution	0.415	1.494	1.909	0.627	2.256	2.883
Climate (GHG emissions)	0.461	1.182	1.643	0.696	1.785	2.481
Noise	0.197	3.037	3.234	0.297	4.587	4.883
Congestion *	2.640	2.373	5.013	3.987	3.584	7.570
WTT (Well-to-Tank emissions)	0.108	0.431	0.539	0.162	0.651	0.814
Habitat degradation	0.074	0.247	0.321	0.111	0.373	0.485
Total	3.907	11.362	15.270	5.897	17.160	23.058

* Congestion cost values estimated for freight vehicles using passenger car equivalents.

, including both total values (in billion EUR) and the breakdown of external cost categories.

As shown in Table 6, the inflation-adjusted external costs related to air pollution, noise, climate change, and the production and distribution of fuels amount to approximately EUR 11.061 billion annually, which highlights the substantial environmental and social burden generated by road freight transport in Poland.

3.3. Electrification of Road Transport

The electrification of the heavy-duty vehicle fleet offers the prospect of at least a partial reduction in external costs associated with the combustion of fossil fuels. The potential for electrifying road freight transport is based on several competing and substitutable energy supply technologies. The most important include hydrogen fuel cells, battery-electric systems, inductive charging while driving, and direct power supply from overhead electric traction. The latter two solutions are still at an experimental stage, as their large-scale deployment remains difficult to predict due to high infrastructure costs and the absence of technological standardisation. In contrast, hydrogen fuel cells and battery-electric propulsion are relatively well-developed technologies already used in practice, for example, in urban bus transport.

Despite their advantages, hydrogen fuel cells face significant limitations from a decarbonisation perspective. The key challenge concerns the origin of hydrogen. At present, the majority is produced from fossil fuels (so-called grey hydrogen), which limits potential environmental benefits. The production of so-called green hydrogen, generated through electrolysis using renewable energy, is currently characterised by low energy efficiency and a negative energy balance per unit of energy delivered to the vehicle.

One of the main barriers to the development of zero-emission propulsion technologies in transport is the energy density of alternative energy carriers compared to conventional fuels. Petroleum-based fuels such as diesel remain the standard in road freight transport due to their high gravimetric and volumetric energy density, which is critical given the high energy demand of this transport segment.

Liquid hydrogen offers approximately three times higher energy density per unit of mass than diesel but requires around three times more volume. This creates substantial challenges in storage and integration of tanks into vehicle design. Other alternative fuels such as methanol, ethanol, ammonia or methane fall between hydrogen and fossil fuels, offering a compromise between gravimetric and volumetric energy density; however, they still require further development of storage technologies and refuelling infrastructure.

Lithium-ion batteries, however, reveal the greatest limitations. Despite the high efficiency of electric drive systems, they have the lowest energy density among all analysed energy carriers—approximately 0.9 MJ/kg and 2.5 MJ/L. This implies that to store an amount of energy equivalent to that of a conventional fuel tank in a heavy-duty vehicle or tractor unit, the mass and volume of the batteries would far exceed the limits allowed under road regulations (Figure 3) [81].

In heavy goods transport, characterised by high energy demand and long-distance journeys, the low energy density of batteries constitutes a major technological barrier affecting vehicle weight, operational range, and transport efficiency. In the longer term, it also determines the need for extensive charging infrastructure and is one of the key reasons behind the introduction of regulations such as the AFIR.

4. Forecast of Alternative Fuel Infrastructure Needs Along the Motorway Network—A Case Study of the A1 and A2 Motorways in Poland

The transformation of road freight transport towards zero-emission mobility cannot be limited solely to the replacement of the vehicle fleet with electric or hydrogen-powered trucks. A key prerequisite for the success of this transition is the provision of a sufficiently dense and high-capacity alternative fuels infrastructure (electric vehicle charging stations) capable of servicing heavy-duty vehicles with high electrical energy demand. While in the case of passenger cars the charging infrastructure is developing in a dispersed manner (in cities, on public parking lots, or private properties), freight transport requires the use of very high-power stations—up to 3.6 MW—located along major transport corridors, enabling energy replenishment during the mandatory, relatively short 45 min daily driver break. This challenge arises from the aforementioned very low energy density of batteries compared with diesel fuel, which means that vehicles must charge more frequently, and every break must enable the intake of a large amount of energy within a short time frame.

Planning charging infrastructure for heavy-duty vehicles must therefore be based on different assumptions than in the case of passenger cars. The key factor is not the number of registered vehicles, but the actual flow of vehicles within the road network, in particular the traffic volume of heavy goods vehicles (HGVs) over 3.5 tonnes gross vehicle weight on major transport routes. It is essential to account for the structure of traffic divided into domestic, international, and transit operations, as long-haul vehicles have different energy needs and stopping patterns compared to local or distribution vehicles. Additionally, infrastructure must be designed in compliance with EU regulations on driving time and driver rest periods (Regulation (EC) No 561/2006), which require a 45 min break after a maximum of 4.5 h of driving. This means that charging points should be located in places enabling simultaneous energy replenishment and the mandatory rest period, such as parking areas, motorway service areas (MSAs), or logistics terminals. These requirements are further reinforced by AFIR, which define the minimum density and connection power of charging infrastructure. Consequently, forecasting the needs for alternative fuel infrastructure along road corridors used by heavy-duty vehicles becomes an essential element of the analysis.

The forecast of the spatial distribution of alternative fuel infrastructure (electric vehicle charging stations) is carried out using the A1 and A2 motorways in Poland as a case study. The selection of the A1 and A2 motorways is justified by both functional and strategic considerations. These motorways are components of the core TEN-T network in Poland. The A1 motorway forms part of the Baltic–Adriatic corridor, linking the Tri-City area, Łódź, Upper Silesia and the Czech Republic. The A2 motorway is part of the North Sea–Baltic corridor, connecting the German border at Świecko with Warsaw and further with Belarus. These routes also carry the highest intensity of international heavy-duty vehicle traffic in Poland and are the main arteries servicing flows between seaports, distribution centres, intermodal terminals and major industrial agglomerations. A substantial share of freight flows between Germany, the Baltic States, the Czech Republic, Slovakia and within Poland itself is concentrated along these corridors. Therefore, the A1 and A2 motorways should be considered priority transport routes where the development of alternative fuel infrastructure should take place first, in line with AFIR requirements.

Given the above, the aim is not to provide a general assessment of the availability of public charging stations in Poland, but to attempt a quantitative determination of the minimum number of charging points for heavy-duty vehicles required to meet AFIR standards on selected sections of the TEN-T network. The analysis includes estimating the required number of charging bays and the necessary connection power along successive sections of the A1 and A2 motorways, under different scenarios of electric vehicle shares in traffic. The calculations take into account data on average daily traffic volumes of heavy-duty vehicles (ADT), corridor lengths, freight structure and the specificity of driver working patterns. As a result, it becomes possible to determine the scale of infrastructure investment required to carry out the energy transition in heavy road transport in a manner consistent with European regulations.

Government data indicate that by 2030, 166 charging locations for heavy-duty vehicles should be operational on the core TEN-T network, whereas currently only 29 are in operation, meaning that their number must be increased more than sixfold. At the same time, only a part of existing MSAs have access to sufficient grid connection capacity, and in many locations new stations will need to be built outside traditional motorway service areas (e.g., at logistics centres, transport operators’ parking areas). This situation is also confirmed by PSNM (New Mobility Association, Warsaw, Poland), which points out that despite formal infrastructure deployment plans, the real challenge lies in the availability of power in the electricity system and the technical feasibility of connecting stations with a capacity of 3.6 MW or more (

Table 7. AFIR requirements for charging infrastructure for heavy-duty electric vehicles (eHDVs) on the TEN-T core network.

TEN-T Network	2025	2027	2030 (Target)
Core network	15% of the network; total power: 1400 kW; ≥1 charging point ≥350 kW; maximum distance ≤120 km	50% of the network; total power: 2800 kW; ≥2 charging points ≥350 kW; ≤120 km	100% of the network; station power: 3600 kW; minimum 2 charging points ≥350 kW; maximum distance ≤60 km

).

Accurate dimensioning of charging infrastructure along TEN-T corridors requires estimating the Average Daily Traffic (SDR) of heavy-duty vehicles. The General Traffic Count (GPR) 2020/21 [82] provides reference data (national and regional reports), while the TranStat database [83] offers more recent traffic profiles based on administrative records. The discrepancies between these two sources are methodological in nature: GPR is a periodic measurement averaged annually, whereas TranStat relies on registration data and is therefore more sensitive to daily and seasonal fluctuations. Consequently, averaged values were adopted for the calculations, with the caveat that project-level analyses should be based on segment-specific data for individual nodes in accordance with the formulae provided in [82,84].

$${SDR}_{model}={\displaystyle \frac{{SDR}_{GPR}+{SDR}_{TransStat}}{2}}$$

(1)

where

• ${SDR}_{model}$—average daily heavy-duty vehicle traffic;
• ${SDR}_{GPR}$—average daily heavy-duty vehicle traffic estimated in the GPR;
• ${SDR}_{TransStat}$—average daily heavy-duty vehicle traffic estimated in the TranStat.

The first step in estimating the demand for charging infrastructure along the A1 and A2 motorways is to determine the number of potential electric heavy goods vehicles (e-HGVs) expected to operate on these routes in 2030. Five model scenarios are applied, assuming the share of e-HGVs in the total HGV fleet at the levels of 5%, 25%, 50%, 75% and 100%. Since the AFIR does not impose a mandatory share of e-HGVs by 2030, these scenarios are theoretical in nature and consistent with methodologies used by the European Environment Agency (EEA), the International Council on Clean Transportation (ICCT), and DG MOVE (European Commission).

Before calculating the number of e-HGVs, it is necessary to determine the Average Daily Traffic (SDR) of heavy goods vehicles (HGVs) on the A1 and A2 motorways and subsequently multiply the SDR by the assumed share of e-HGVs in the fleet. For the A1 and A2 motorways, average traffic volumes of HGVs were determined based on two sources: the General Traffic Count 2021/2022 by GDDKiA and the TranStat API (automatic road traffic measurement system, 2023). Although SDR values derived from both sources are similar, local variations occur depending on specific road sections; therefore, an average of both datasets was adopted to improve the robustness of the estimates (

Table 8. Average Daily Traffic (SDR) of Heavy Goods Vehicles (HGVs) on the A1 and A2 Motorways—GDDKiA and TranStat data (HGV/day).

Motorway	SDR GPR (HGV/Day)	SDR TranStat (HGV/Day)	SDR Model
A1	18,000	20,500	19,250
A2	17,000	19,200	18,100

).

It is assumed that the share of electric heavy goods vehicles will increase in the coming years. Therefore, five scenarios were applied (5%, 25%, 50%, 75%, and 100%) to represent the level of electrification within the total number of heavy-duty vehicles operating on the analysed motorways. For each e-HGV share scenario, the number of electric heavy goods vehicles travelling daily on the A1 and A2 motorways was calculated using the following formula:

$$NeHGV=SDRmodel·U$$

(2)

where

• $NeHGV$—average daily heavy-duty vehicle traffic adjusted by the fleet penetration rate;
• $SDRmodel$—average daily heavy-duty vehicle traffic;
• $U$—penetration of the fleet by electric heavy goods vehicles (e-HGVs—0.05, 0.25, 0.50, 1).

In accordance with Equation (2), a summary was prepared showing the daily number of electric heavy goods vehicles travelling on the A1 and A2 motorways, taking into account scenarios of the share of battery-electric vehicles in the total number of heavy-duty vehicles operating on the analysed routes. It is assumed that this share will increase in the coming years (

Table 9. Daily number of e-HGVs on the A1 and A2 motorways considering fleet penetration of e-HGVs.

e-HGV Share	A1: 19,250⋯U	A2: 18,100⋯U
5%	963	905
25%	4813	4525
50%	9625	9050
75%	14,438	13,575
100%	19,250	18,100

).

The number of e-HGVs travelling along the A1 and A2 motorways is not equivalent to the number of vehicles that will use en route charging infrastructure. In practice, when planning charging infrastructure for heavy-duty transport, it is assumed that a portion of vehicles:

• Charge at logistics depots or terminals (depot charging);
• Operate on short or medium distances within regional distribution systems;
• Have sufficient battery capacity to complete the journey without en route charging;
• Use overnight charging, during rest periods or at cross-dock warehouses.

Studies by the National Renewable Energy Laboratory (NREL, U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, 2024) [85] and Fraunhofer ISI (Germany, 2024) [86] indicate that only 20–35% of e-HGVs require regular charging on long-distance routes, while the remaining vehicles rely on off-route charging solutions. For this reason, both in the literature and in TEN-T infrastructure planning, the en route charging coefficient φ is commonly applied, defining the share of vehicles requiring charging during the journey. Methodologically, this value typically ranges from 0.15 to 0.35. In this study, following the recommendations of JRC, ICCT and the TENTec/AFIR methodology, a baseline value of φ = 0.25 is adopted. This means that 25% of e-HGVs travelling on motorways will require en route charging, i.e.:

$${NeHGV}_{en-route}=NeHGV·\phi$$

(3)

where

• ${NeHGV}_{en-route}$—number of e-HGVs requiring en route charging on the A1 and A2 motorways;
• $NeHGV$—number of e-HGVs running on the A1 and A2 motorways;
• $\phi$—en route charging coefficient.

The adjusted data for the daily number of e-HGVs on the A1 and A2 motorways, applying the en route charging coefficient φ = 0.25, are presented in

Table 10. Number of e-HGVs requiring en route charging.

e-HGV Share	A1 (e-HGV/Day)	A1 En Route Charging (φ = 0.25)	A2 (e-HGV/Day)	A2 En Route Charging (φ = 0.25)
5%	963	241	905	226
25%	4813	1203	4525	1131
50%	9625	2406	9050	2262
75%	14,438	3609	13,575	3394
100%	19,250	4813	18,100	4525

.

After determining the number of e-HGVs requiring en route charging, the next step was to estimate the daily electricity demand generated by this group of vehicles on the analysed segments of the TEN-T core network in Poland (motorways A1 and A2). This step is crucial, as the number of vehicles alone does not reflect the required capacity of the charging infrastructure. Therefore, the values were converted into energy demand (kWh), and subsequently into power demand (kW/MW), which allows for determining the necessary number of high-power charging stations compliant with AFIR (≥350 kW for HDVs) [87].

For the purpose of the calculations, parameters reflecting the average energy consumption of zero-emission 40-tonne heavy-duty vehicles (battery-electric articulated trucks) were adopted, based on recommendations by ICCT (2021), ACEA, and the European Alternative Fuels Observatory (EAFO, 2023). The methodological assumptions are presented in

Table 11. Input parameters adopted for the calculations.

Parameter	Adopted Value	Source
Average energy consumption of e-HGV	1.3 kWh/km	ICCT (2021), Volvo Trucks, Daimler eActros
Average daily driving distance in long-haul operation	400 km/day	JRC, Fraunhofer ISI, ACEA
En route charging correction factor (φ)	0.25	JRC, ICCT, TEN-Tec
Average charging power (DC megacharger)	350 kW	AFIR (Art. 5), Daimler, MAN, Scania

.

The following formula was applied to calculate daily electricity demand (kWh/day):

$$E_{day}={NeHGV}_{en-route}·e·L$$

(4)

where

• $E_{day}$—daily energy demand (kWh);
• $e$—average vehicle energy consumption (1.3 kWh/km);
• $L$—average daily driving distance per vehicle (400 km/day).

After performing the calculations, the results of the daily electricity consumption of heavy-duty electric vehicles requiring en route charging are presented in

Table 12. Daily en route energy consumption of e-HGVs.

e-HGV Share	A1—Vehicles Requiring Charging	A1—Daily Energy Demand (MWh)	A2—Vehicles Requiring Charging	A2—Daily Energy Demand (MWh)
5%	241	125.3	226	117.5
25%	1203	625.6	1131	587.9
50%	2406	1251.2	2262	1175.9
75%	3609	1876.8	3394	1763.8
100%	4813	2502.5	4525	2351.8

.

Determining the daily energy demand for e-HGVs requiring en route charging does not in itself define the size of the necessary infrastructure. The dimensioning and technical parameters of charging stations are determined by the peak power that must be supplied within a specific charging time window. In the peak scenario, it is assumed that the entire $E_{day}$ must be delivered within a relatively short period. According to the adopted operational assumption, this period is 1.5 h; therefore, the required charging power is calculated as:

$${\mathit{P}}_{required}={\displaystyle \frac{E_{day}}{t_{charging}}}$$

(5)

This result should be interpreted as the upper bound of system load in a situation where charging sessions are concentrated within a short time window. Therefore, the daily energy demand ($E_{day}$) for the 5–100% scenarios was converted into peak power by dividing it by the charging time $t_{charging}=1.5 \mathrm{h}$. The resulting values, expressed in megawatts (MW), represent the minimum total power that charging stations along each motorway must provide to meet the demand under the assumed temporal conditions (

Table 13. Required peak charging power on the A1 and A2 motorways.

e-HGV Share	A1: Daily Energy (MWh)	A1: Required Peak Power (MW)	A2: Daily Energy (MWh)	A2: Required Peak Power (MW)
5%	125.3	83.53	117.5	78.33
25%	625.6	417.07	587.9	391.93
50%	1251.2	834.13	1175.9	783.93
75%	1876.8	1251.20	1763.8	1175.87
100%	2502.5	1668.33	2351.8	1567.87

).

These values reflect a time-critical, extreme scenario. In practical implementation, instantaneous power demand can be reduced by extending the operational window through demand management, nighttime charging, or fleet-based charging profiles.

The peak power demand was subsequently converted into the number of high-power charging (HPC) points required. A nominal charging power of 350 kW per charging point was assumed, in line with AFIR requirements for heavy-duty vehicles. The number of charging points was calculated by dividing the peak power by 0.35 MW and then rounding the result up to the nearest whole number. This value should be interpreted as the minimum number of simultaneous charging points, rather than the number of charging stations (

Table 14. Number of Required 350 kW HPC Points on A1 and A2.

e-HGV Share	A1: Number of 350 kW Charging Points	A2: Number of 350 kW Charging Points
5%	239	224
25%	1192	1120
50%	2384	2240
75%	3575	3360
100%	4767	4480

).

On the core TEN-T network, by 2030 the AFIR requires the availability of charging infrastructure for e-HGVs at least every 60 km. In the first step, the minimum number of charging locations was calculated for each of the analysed motorways. For the A1 motorway, an approximate length of 565 km was adopted, and for the A2 motorway—approximately 480 km. The minimum network of locations therefore amounts to 10 points on A1 and 8 points on A2.

Table 15. Number of charging points required per station.

e-HGV Share	A1: Charging Points per Location	A2: Charging Points per Location
5%	24	28
25%	120	140
50%	239	280
75%	358	420
100%	477	560

presents the number of charging points assuming the construction of high-power charging stations every 60 km, in accordance with AFIR minimum requirements, for fleet electrification scenarios of 5–10%.

The largest heavy-duty vehicle charging stations currently under construction in Europe offer 22 charging bays, each with a capacity of 350 kW [88]. This means that even with a 5% share of e-HGVs, the required number of charging locations exceeds the AFIR minimum (one station every 60 km).

To convert the total number of required charging bays into the number of charging stations, the demand for charging bays was divided by 22 and rounded up. This yields the minimum number of charging stations necessary to meet demand in each scenario for both motorways (

Table 16. Required Number of Charging Stations on Motorways A1 and A2 Assuming 22 Charging Bays per Location.

e-HGV Share	Number of Charging Points—A1	Number of Charging Stations—A1	Number of Charging Points—A2	Number of Charging Stations—A2
5%	239	11	224	11
25%	1192	55	1120	51
50%	2384	109	2240	102
75%	3575	163	3360	153
100%	4767	217	4480	204

).

As demonstrated in Table 16, at a 5% share of e-HGVs, a charging network spaced at 60 km intervals meets the minimum AFIR requirements. However, to accommodate actual charging demand, it would already be necessary to construct at least one additional charging station along the A1 motorway and three along the A2 motorway, assuming each location is equipped with 22 high-power charging points. In the 25–50% penetration scenarios, the scale of demand requires a significant increase in the number of charging locations beyond the AFIR minimum or, alternatively, the development of substantially larger charging hubs with more than 22 charging points, including the deployment of MCS ultra-fast chargers (750–1000 kW), replacing part of the 350 kW chargers [89].

The calculations were based on a maximum-demand scenario, incorporating the following assumptions:

• Total required charging time for the fleet is concentrated within a 1.5 h operational window per day;
• Uniform charging power of 350 kW per charging point;
• No use of 1 MW MCS chargers, which would increase station throughput;
• 22 charging points per station.

According to the Regulation of the European Parliament and of the Council, a heavy-duty vehicle driver may drive for a maximum of 9 h per day (extendable to 10 h twice a week), after which a minimum rest period of 11 h must be taken, which can be reduced to 9 h no more than three times per week. To better reflect real-world operational conditions, the calculation was therefore updated using an extended charging window of 12 h. All other parameters were kept unchanged due to current market conditions.

Based on these assumptions, the required number of charging stations along the A1 and A2 motorways was estimated (Figure 4).

The results presented on Figure 4 indicate that the number of required high-power charging stations for heavy-duty vehicles on the A1 and A2 motorways increases in line with the share of e-HGVs in traffic. Under a 12 h charging window—which should nonetheless be considered a highly demanding operational scenario—even at a 25% electrification level of the heavy-duty fleet, approximately 14 hub-type stations (each equipped with 22 charging points of 350 kW) would be needed along both corridors. Under full electrification, this number rises to more than 50 stations.

This demonstrates that shortening the available charging time from a full day to a 12 h window, as dictated by driver working time regulations, significantly increases the pressure on the number of infrastructure locations, not only on their connection capacity. In practice, this implies that with a high share of e-HGVs, expanding individual charging stations at existing motorway service areas (MOPs) will be insufficient; instead, a denser network of charging sites will be required, including those outside traditional service areas.

Although the 100% penetration scenario is a useful upper-bound modelling construct, its results imply very substantial infrastructure requirements that exceed current planning and implementation capacities. Such an extreme level of electrification would require a structural redesign of the TEN-T corridor infrastructure, including not only the expansion of charging hubs but also substantial reinforcement of medium- and high voltage grid connections, coordinated spatial planning for logistics facilities, and the development of large scale energy storage systems to stabilise peak demand. From a policy perspective, this scenario demonstrates that full electrification of heavy-duty transport cannot be achieved solely through AFIR-compliant corridor charging. Instead, it would require a complementary mix of depot-based charging, opportunity charging at logistics nodes, and a gradual restructuring of freight flows. These results also highlight that realistic medium-term strategies should prioritise partial electrification trajectories (e.g., 25–50%), where infrastructure requirements remain proportionate to feasible investment volumes and grid capacity expansion timelines.

In medium-range scenarios (around 50% e-HGV penetration), the deployment of approximately 25–30 stations along the two main TEN-T axes (A1–A2) appears to be the minimum necessary to maintain the continuity of heavy road transport operations without the risk of charging-induced congestion.

5. Discussion

The analysis confirms that road transport remains the primary source of external costs within Poland’s transport system, dominating both passenger and freight mobility. Despite advancements in vehicle efficiency and stricter emissions standards, its environmental and social impacts are disproportionately high compared to other transport modes. Road freight transport alone generates over EUR 17 billion in annual external costs, largely due to air pollution, noise, congestion, and accidents. These findings highlight the urgent need for structural changes in the sector, as further growth in road freight activity without effective mitigation would intensify these burdens. In this context, road transport electrification—supported by the Alternative Fuels Infrastructure Regulation (AFIR)—stands as a crucial instrument for reducing environmental impacts and advancing decarbonisation.

The forecast for the A1 and A2 motorways reveals that transitioning to zero-emission freight transport requires both fleet replacement and a large-scale expansion of charging infrastructure along the TEN-T corridors. Even a conservative scenario—with only a 5% share of electric heavy-duty vehicles—exceeds the minimum requirements set by the AFIR. Calculations shows that the A1 motorway will need at least 11 high-power charging stations, each with 22 bays of 350 kW, and the A2 will require a comparable number. As the share of electric trucks rises, infrastructure demands grow exponentially: nearly 30 stations for 50% electrification, and over 50 for full electrification. These results underscore the scale of the infrastructural challenge and the widening gap between current deployment levels and the expected demand over the next decade.

The study identifies a major constraint: the immense power demand from high-capacity truck charging. Modelling shows that the peak charging power required on key transport corridors will range from approximately 80 MW with 5% fleet electrification to over 1.6 GW with full electrification. This level of demand heavily burdens the national grid and surpasses the existing connection capacity of most motorway service areas. The current energy infrastructure is unprepared for the simultaneous operation of multiple megawatt-scale chargers, as a single truck charging hub can require tens of megawatts. This underscores a fundamental challenge—the electrification of road transport is inextricably linked to the parallel modernization and expansion of the electricity grid.

The analysis further reveals a strong spatial and temporal dimension to the charging challenge. Charging operations are concentrated within specific daily windows due to regulated driving times for heavy-duty vehicle drivers, which amplifies peak energy demand. Even when assuming an extended 12 h charging window, the number of required stations and chargers remains substantial. This implies that the future network must not only meet technical requirements, such as the AFIR-defined maximum distances between stations, but also accommodate the temporal clustering of demand caused by logistics schedules. Without adequate network planning and demand management, there is a risk that charging congestion could emerge as a new form of systemic inefficiency in road transport.

Decarbonising road freight transport in Poland is technically and infrastructurally feasible, yet hinges on unprecedented coordination between transport, energy, and industrial policy. Electrification offers significant potential to reduce greenhouse gas emissions, air pollution, and other external costs of fossil fuel combustion. However, the scale of required infrastructure investment—encompassing both the number of charging sites and the capacity of the power grid—poses a profound challenge. Consequently, achieving the objectives of the AFIR and the European Green Deal will depend not only on technological readiness but also on the successful integration of transport electrification into a broader, systemic framework for sustainable transport and energy development.

The electrification of heavy-duty road transport along the Polish TEN-T corridors entails profound implications for the national power system, which must be fully considered in future research. As shown by the infrastructure demand estimates developed in this study, the large-scale introduction of electric heavy-duty vehicles (e-HGVs) will generate substantial and geographically concentrated electricity loads, particularly at high-power charging hubs located along long-distance motorway corridors. A key challenge concerns the local grid’s ability to supply multi-megawatt charging clusters without destabilising regional power flows. This issue is especially relevant for the central Polish node around Stryków, where the A1 and A2 motorways intersect and where one of the country’s largest agglomerations of logistics warehouses and distribution centres is emerging (Figure 5).

The co-location of intense freight activity, growing e-HGV flows, and high industrial electricity consumption suggests that this area will experience disproportionately high peak demand, requiring detailed assessment of grid hosting capacity, local transformer capabilities, and the need for distribution and transmission upgrades.

Future research should therefore prioritise the development of advanced simulation models capable of estimating spatiotemporal electricity demand from e-HGVs at individual TEN-T nodes. These models should incorporate dynamic traffic profiles, differentiated charging patterns (including en route and depot charging), seasonal variations and operational constraints stemming from drivers’ regulated rest times. Moreover, further studies should explore technically and economically optimal configurations of high-power charging hubs, examining different charging windows, power mixes (e.g., 350 kW + 1 MW chargers), redundancy levels and feasible grid connection architectures. Such analyses would support investment planning by indicating when connection to the transmission grid (110 kV or 220 kV) becomes necessary.

Another important direction for future work is the integration of long-haul charging demand with the rapidly expanding sector of urban and last-mile logistics. The overlap of these profiles could significantly reshape daily load curves, intensify peak demand and influence the location and sizing of charging hubs at the periphery of major metropolitan areas. Finally, research should also address the economic dimension of infrastructure deployment, including financing frameworks, regulatory incentives, tariff design and public–private partnership models. Comparative studies benchmarking Poland’s transition path against leading practices implemented in other EU and OECD countries would help identify efficient governance approaches and accelerate the implementation of AFIR-compliant infrastructure.

6. Conclusions

The study yields several key insights into the electrification of heavy-duty road transport along the Polish segments of the TEN-T network. First, road freight transport continues to constitute the dominant contributor to external transport costs, and electrification—supported by the regulatory framework of AFIR—represents one of the few viable pathways for substantial reductions in climate- and air pollution-related externalities. Second, traffic volumes observed on the A1 and A2 motorways indicate rapidly escalating infrastructure needs: even at a 5% penetration of e-HGVs, charging demand exceeds AFIR’s minimum thresholds; at 50%, approximately 25–30 high-power charging hubs are required; and under a full-electrification scenario, more than 50 hubs must be deployed across both corridors.

Third, the associated peak-power requirements range from roughly 80 MW (5% scenario) to over 1.6 GW (100% scenario), substantially surpassing the current grid capacity at most motorway service areas. This underscores the necessity for coordinated grid reinforcement and proactive planning of high-capacity energy connections. Fourth, temporal clustering of charging events—stemming from legally mandated driver rest periods—further amplifies peak load pressures, demonstrating that charging-station density, rather than nominal station capacity alone, will be a defining factor in network performance.

A particularly critical area is the A1–A2 interchange near Stryków, which is emerging as one of the most logistically intensive regions in Poland. The concentration of distribution centres and warehousing facilities in this zone will intensify local electricity demand and necessitate targeted grid upgrades and site-specific planning strategies.

Overall, the findings indicate that while long-haul electrification is technically feasible, it requires an integrated approach involving transport infrastructure development, logistics sector adaptation, and national-level energy system planning. Partial electrification scenarios (25–50%) appear most achievable within medium-term investment and grid expansion horizons and align with realistic deployment capacities. These results highlight the importance of long-term strategic coordination among transport, energy, and policy stakeholders to ensure the successful implementation of AFIR-compliant charging infrastructure and to advance the broader objectives of sustainable, low-emission road freight transport in Poland.