Decision-Making Problems in Urban Transport Decarbonization Strategies: Challenges, Tools, and Methods

1. Introduction

The pursuit of sustainable transport system development and the improvement of residents’ quality of life continue to keep these issues relevant and subject to extensive analysis. Researchers are actively seeking methods and tools to assess the implementation of solutions and technologies for transport aimed at reducing the negative environmental impact of mobility. This is particularly relevant to the decarbonization of road transport, given its significant share of overall transport performance [1,2,3,4].

An analysis of this topic reveals several key research challenges, including technical and infrastructure-related issues (see, Patil [4], Noussan et al. [5], Yan et al. [6]), difficulties in selecting appropriate optimization methods (see Kozyra et al. [7]), environmental considerations (see Atabaki et al. [8]), social aspects (see Jaździk-Osmólska et al. [9], Nochta et al. [10]), and legal and regulatory constraints, such as Green Bonds and Funds (see Bhandary et al. [11]). Importantly, research indicates that the methods and tools used for transport decarbonization are closely related to those employed in decision-making processes for sustainable transport development [12].

A critical component of decarbonization efforts is the development of charging infrastructure. Numerous studies emphasize that the deployment of such infrastructure is essential for the widespread adoption of electric vehicles [13].

The planning and placement of charging stations must account for a range of factors, including the following:

• Distance, power demand, station availability, and capacity [14];
• Socio-economic and environmental factors such as population density, employment, income levels, and user preferences [13];
• Vehicle and charging station parameters, including battery state of charge (SOC), expected driving range, and station availability [14];
• Geographic and topological conditions [13,14];
• Mobility policies, especially those promoting green mobility [13];
• Forecasting models for electric vehicle demand, incorporating origin–destination (OD) travel patterns and SOC levels [14];
• Types of chargers and public policy strategies for electromobility development, e.g., private (home) chargers, public chargers in residential areas, slow public chargers, and fast public chargers [13].

To enhance the accuracy of charging station location forecasts and improve the efficiency of electric vehicle (EV) infrastructure, various optimization methods are employed. These include Geographic Information Systems (GIS) [13] or transport planning software VISUM [15], multi-objective optimization algorithms [16,17], as well as artificial intelligence techniques or machine learning (ML) [18,19,20] and the Enhanced Cladistic Neural Network (ECNN) [14].

It should also be noted that the use of zero-emission vehicles, while reducing harmful emissions and noise during travel, also entails negative environmental consequences associated with the vehicle’s life cycle. These include the environmental impact of component manufacturing, energy production for charging [21], energy utilization [22], and end-of-life disposal or recycling [23]. Such factors should be considered when analyzing the structure of the transport system and the modal split. In this context, the integration of electric vehicles with the urban power grid is crucial for energy transition and Smart City development [24,25]. These efforts aim to minimize operating costs and optimize the utilization of renewable energy sources. However, challenges related to battery degradation and the need to manage the state of charge (SOC) remain significant [26]. This necessitates the development of methods and tools for electric vehicle route optimization that account for battery capacity constraints [27,28].

It should also be noted that decarbonizing road transport, particularly through the increased adoption of zero-emission vehicles, presents both challenges and opportunities for transport companies [29]. One of the primary challenges is the high upfront cost of purchasing electric vehicles (EVs) and investing in charging infrastructure. However, with adequate financial support and a long-term strategic approach, both transport companies and municipalities can benefit from enhanced operational efficiency, reduced costs, and improved quality of life for residents. Research by the International Energy Agency (IEA) indicates that despite the higher initial purchase price of electric vehicles, their total cost of ownership (TCO) is comparable to that of conventional diesel vehicles. This is primarily due to lower operating expenses, especially reduced fuel and maintenance costs [30,31].

The primary benefit of introducing zero-emission vehicles is the reduction in air pollution and, consequently, an improvement in air quality. According to data from the World Health Organization (WHO) and the European Environment Agency (EEA), in 2020, as much as 96% of the European Union’s urban population was exposed to concentrations of particulate matter (PM2.5) exceeding WHO guidelines, contributing to approximately 238,000 premature deaths across the EU-27 [32]. This issue was reaffirmed in the EEA’s 2024 report, which highlights its persistence despite ongoing mitigation efforts [33].

In Poland, for example, the widespread adoption of zero-emission vehicles could significantly reduce concentrations of pollutants such as PM2.5 and nitrogen oxides (NOx), directly enhancing urban air quality. Experiences from other European countries demonstrate that, with appropriate investments and supportive public policies, substantial emission reductions are achievable. However, in Poland, the carbon footprint of electricity generation continues to play a major role in determining the environmental performance of electric vehicles. In 2023, it was approximately 660 gCO₂/kWh [34], compared to around 380 gCO₂/kWh in Germany and just 40 gCO₂/kWh in Sweden. Nevertheless, even with higher emissions from electricity generation, life-cycle analyses, such as those based on the methodology of the International Energy Agency, show that electric vehicles can produce up to 30% fewer emissions than internal combustion engine vehicles [35].

In the context of urban energy management and stabilization, it is essential to seek solutions that enhance the energy efficiency of applied technologies or involve reorganizing public transport systems [15]. For instance, in highly urbanized areas, determining optimal traffic control parameters or identifying zones with high parking demand poses a significant challenge. Long-term parking locations, such as shopping centers or workplaces, are particularly well-suited for Vehicle-to-Grid (V2G) hubs, as they offer consistent parking patterns and extended idle times, enabling substantial energy feedback to the grid [36].

There is no doubt that many of these problems are NP-hard due to the vast volume of data and the diversity of user behavior strategies, necessitating the use of advanced heuristic algorithms. Access to detailed and reliable operational data remains a key challenge for many designers and analysts [37]. Support in this area has come from the development of original methods based on heuristic approaches, such as ant colony optimization, genetic algorithms, and hybrid models, which aim to minimize EV energy costs [21]. These approaches also enable the incorporation of uncertainties in charging point operations, such as failure probabilities [38].

Green mobility policies in urban areas require city leaders to adopt a new approach to public transport. Strategic planning for the development of green urban mobility, including the integration of electric vehicles (EVs), is essential for building sustainable and low-emission transport systems. Investments in public transport, such as the modernization of bus fleets with electric vehicles, the expansion of tram and metro networks, and the development of urban rail systems, alongside improvements in service frequency and reliability, are fundamental to reducing traffic congestion. Equally important is the development of safe and inclusive urban infrastructure tailored to the needs of cyclists and pedestrians. In countries such as Denmark and the Netherlands, where public transport is highly developed, residents are significantly more likely to choose sustainable modes of transport over private cars. For instance, in Copenhagen, as of 2020, 49% of all journeys were made by bicycle, an outcome made possible by an extensive network of bike paths and strong pro-cycling policies [39].

Promoting walking and cycling requires continuous investment in infrastructure, including dedicated bicycle lanes, pedestrian crossings, car-free zones, and pedestrian-friendly urban spaces. Cities like Amsterdam and Copenhagen exemplify how the development of comprehensive cycling networks and their integration with other modes of transport can result in cycling becoming the dominant mode for short-distance travel.

Another important step in promoting sustainable urban mobility is the introduction of low-emission zones (LEZs) and restrictions on car traffic in city centers. Such measures have been successfully implemented in cities like London, where the introduction of the Ultra Low Emission Zone (ULEZ) significantly reduced exhaust emissions and car traffic. In fact, the implementation of the ULEZ led to a 10% reduction in the number of vehicles entering central London and a 35% decrease in nitrogen oxide (NOx) emissions within just ten months [40]. These types of regulations can effectively reduce the attractiveness of car use in urban areas while simultaneously promoting alternative modes of transport.

An equally important factor in changing residents’ mobility preferences is the integration of different transport modes into a single, coherent network. This involves facilitating seamless transfers between public transport, cycling, and walking, as well as developing digital platforms that enable efficient travel planning using multiple modes of transport [41]. An example is the “WienMobil” system implemented in Vienna [42,43], which allows users to plan trips using public transport, bicycles, rental cars, and taxis within a single app. This integrated approach provides users with the flexibility to choose the most convenient mode of transport and encourages them to reconsider car ownership.

The implementation of flexible transport services like on-demand mobility also contributes to an increased share of public transport usage. Adapting services to passenger needs, as demonstrated in Helsinki, has led to a significant rise in public transport trips, with a 73% share compared to the 48% average across urban passenger transport [44].

Shifting residents’ travel preferences toward environmentally friendly modes of transport requires the implementation of a comprehensive set of integrated measures. These include infrastructure development, the introduction of low-emission zones, educational campaigns, financial incentives, and the integration of various transport modes. Improving the availability and attractiveness of sustainable mobility options combined with public education on their health and environmental benefits can effectively influence travel behavior and support sustainable urban development.

In light of the issues outlined above, this article is structured into four main sections. The Section 1 identifies the key problems and challenges associated with transport decarbonization, emphasizing not only research aspects but also the regulatory frameworks shaping this process. The Section 2 presents a critical review of the literature, categorizing recent studies according to the main thematic areas and challenges previously identified. This analysis enables the identification of research gaps and highlights necessary future actions. The Section 3 explores the methods and tools essential for identifying optimal solutions, with particular attention to recent advancements in algorithms and optimization techniques. The role of artificial intelligence and its potential in addressing complex NP-hard problems is also discussed. The Section 4 concludes with a summary of key findings and outlines the most pressing challenges and prospective research directions in the field of transport decarbonization.

2. Literature Analysis

2.1. Strategies for Decarbonizing Urban Transport

Urban transport decarbonization strategies discussed in the scientific literature are multifaceted and encompass a wide range of actions. The most significant of these can be grouped as illustrated in Figure 1.

Key actions aimed at reducing carbon dioxide emissions primarily focus on technological and infrastructural investments. In this context, the electrification of transport, encompassing various subsystems, is of critical importance. The widespread adoption of electric vehicles (EVs) requires the development of appropriately located charging infrastructure tailored to demand. Equally essential is the advancement of battery technologies and the implementation of modern solutions based on hydrogen and fuel cell electric vehicles (FCEVs). Increasing attention is being paid to the optimal management of EV charging profiles, which should be integrated with renewable energy sources as part of broader energy management strategies.

As noted in the Introduction, transport electrification should be complemented by additional measures. These include systemic mobility planning, intelligent traffic and data management, multimodal transport integration (encompassing public transport, cycling, walking, and freight), as well as the introduction of innovative business models and urban policies that promote sustainable transport solutions. A key research area involves the development of intelligent transport systems (ITSs), closely linked to the ongoing process of digitalization. Modern decision–support systems for urban transport management increasingly rely on artificial intelligence and digital twin technologies. Another emerging trend in shaping urban transport infrastructure is the implementation of “green infrastructure” and multimodal approaches. The use of diverse transport systems within urban areas is justified by their ability to adapt to the specific spatial characteristics of the city and the travel behaviors of residents and visitors alike.

Financial and legal instruments play a key role in shaping transport decarbonization strategies and promoting their implementation. These include divestments from the fossil fuel sector, subsidies and tax incentives for low-emission transport, green bonds, and investment funds. The introduction of low-emission zones and restrictions on the use of fossil fuel-powered vehicles in city centers are often integrated into transport policies outlined in Sustainable Urban Mobility Plans (SUMPs). Increasingly, there is also an emphasis on viewing urban mobility as a component of integrated urban management.

The effective implementation of carbon dioxide reduction strategies in urban areas requires well-structured communication, education initiatives, and public awareness campaigns. Engaging with residents is particularly important when developing transport policies and promoting low-emission mobility options.

Decarbonization strategies should also be addressed at various levels of governance from an organizational perspective. The integration of spatial development planning with transport systems and the broader concept of mobility management calls for the use of diverse decision-making tools to tackle challenges at strategic, tactical, and operational levels. These actions, often designed and implemented through a variety of approaches, are discussed in detail in the following subsections.

2.2. Infrastructural Strategies

Many authors emphasize that one of the most practical solutions for decarbonizing transport is the widespread adoption of low-emission vehicles, particularly electric vehicles (EVs). Patil et al. [4] highlight the necessity of expanding charging infrastructure, especially in streets, parking areas, and bus depots. They also stress the importance of consumer perceptions and evaluations of EVs, particularly regarding cost, driving range, and service availability.

Infrastructure development for electric transport is currently focused primarily on road and rail modes. Key priorities include building and expanding networks of charging stations for electric buses and private vehicles, as well as modernizing bus depots and service facilities for electric vehicles [13]. In the rail sector, investments in low-emission and energy-efficient tram and metro systems are of particular importance [29]. However, electrification alone is not sufficient unless it is integrated with renewable energy sources such as photovoltaics installed on depot rooftops, smart grids, and vehicle-to-grid (V2G) technologies to supply clean energy to charging infrastructure [4,41].

The importance of infrastructure supporting micromobility is also receiving increasing attention. Research presented in [41] indicates that the construction of new tram lines in peripheral districts can reduce car dependency, while the modernization of existing infrastructure, such as tracks, stops, and traffic control systems can enhance the efficiency and reliability of the urban transport network. The authors emphasize that strengthening mobility for short distances requires the development of dedicated bicycle lanes and pedestrian pathways, separated from car traffic, as well as the establishment of city bike stations and electric bike service points.

A systemic multimodal approach is essential in contemporary urban mobility planning. Integrated transfer hubs that connect public transport with cycling and pedestrian infrastructure, alongside “Park & Ride” and “Bike & Ride” systems, play a key role in this strategy [45]. These hubs enable seamless transfers and reduce the reliance on private vehicles. Buffer parking lots, typically located on the outskirts of cities, allow commuters to switch modes and continue their journeys via public transport. To encourage the use of these systems, cities are implementing infrastructure enhancements such as shelters, surveillance, and EV charging stations [41].

Efforts to reduce carbon dioxide emissions also involve the deployment of intelligent transport systems (ITSs), which support traffic light prioritization for public transport, real-time passenger information systems, integrated ticketing solutions, and mobile applications that facilitate journey planning and multimodal transfers [46]. Musa et al. [47] propose an infrastructure framework for intelligent traffic management, demonstrating its potential to reduce emissions and improve urban quality of life. The authors also advocate for decarbonization strategies based on the development of so-called “green infrastructure” including transport corridors, green roofs, walls, and streets, which contribute to urban resilience in the face of climate change, economic shifts, and public health challenges. A similar perspective is presented in [48], which highlights the potential of such infrastructure to reduce CO₂ emissions by cooling urban environments, improving air quality, and supporting sustainable mobility. Consequently, the integration of spatial planning with ecological transport solutions is essential [49].

2.3. Technological Strategies

The introduction of low-emission transport is widely regarded as the most important technological strategy for decarbonizing the transport sector. Currently, the market offers various types of electric vehicles. Patil et al. [4] highlight battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs) as key solutions. However, these are not the only available options. Noussan et al. [5] examine the advantages and disadvantages of different propulsion technologies, including electric vehicles, hydrogen, and biofuels, and compare life cycle assessment (LCA) emissions for vehicles powered by various energy sources.

Moura et al. [50] analyze the impact of different EV charging profiles on CO₂ emissions and grid load, depending on the energy mix. Alanazi [51] identifies key technological barriers to the large-scale adoption of electric vehicles, emphasizing limited driving range and an underdeveloped charging infrastructure. The author suggests that advancements in lithium-ion batteries and the development of alternative charging technologies such as solid-state batteries could help address these challenges. Such innovations are expected to improve EV range and reduce charging times, thereby enhancing the overall usability of electric vehicles.

A comprehensive overview of hydrogen technologies, their transport applications, and infrastructure challenges is provided by Soleimani [52]. This review discusses the development of fuel cell technologies (PEMFC and SOFC), the storage and distribution of hydrogen produced from renewable sources (so-called green hydrogen), refueling logistics, and the role of hydrogen in urban transport systems.

The integration of EVs with smart city systems is also of growing importance [50,51]. Such integration can support the EV charging process by aligning it with the availability of renewable energy sources, thereby contributing to optimal energy management and real-time charging solutions (e.g., nighttime charging and dynamic pricing). The necessity of integrating renewable energy sources into the development of EV charging infrastructure is also emphasized in the aforementioned study by Patil et al. [4].

Inderwildi et al. [53] discuss predictive traffic and energy management, as well as route and vehicle energy consumption optimization. The authors analyze how decarbonization of key economic sectors (including urban transport) can be supported through artificial intelligence (AI) and cyber–physical systems (CPSs), such as digital twins. AI technologies can enhance energy efficiency, support traffic and fleet management, and enable advanced modeling and simulation of urban transport systems. They also facilitate broader integration with renewable energy sources. An integrated approach to traffic management in smart cities based on Internet of Things (IoT) technologies and big data-driven intelligent transport systems (ITSs) is also recommended by Musa et al. [47]. Such systems aim to reduce emissions and improve transport efficiency, including traffic flow and travel times. Similarly, Archibald et al. [54] present digital twins as a powerful tool for modeling and optimizing transport systems in the context of decarbonization. They emphasize the importance of integrating data from diverse sources and models to develop more sustainable and low-emission urban transport solutions.

2.4. Financial and Legal Strategies

Positive consumer perception of EVs is crucial for their widespread adoption. As a result, various incentives have been introduced in different countries. Patil et al. [4] present examples of policies implemented in Norway, China, the United States, the European Union, and India, highlighting government subsidies and supportive policies as key drivers accelerating EV uptake.

A significant financial and legal instrument supporting decarbonization is fossil fuel divestment, whereby financial institutions and investors withdraw capital from companies involved in fossil fuel extraction and processing, redirecting investments toward more sustainable sectors, including urban transport. Analyses of divestment impacts show that excluding fossil fuel companies from investment portfolios does not negatively affect financial performance, thus reinforcing the viability of capital redistribution as a decarbonization strategy [55].

Financial and political strategies can play a key role in facilitating EV adoption and mitigating the effects of climate change through mechanisms such as fossil fuel divestment. Green financing of urban transport is emerging as a central element of decarbonization strategies, supported by instruments like green bonds and dedicated investment funds [11]. The issuance of such bonds to finance low-emission projects, such as public transport electrification, bicycle infrastructure development, or modernization of rolling stock, is gaining increasing traction.

Moreover, national and local governments are offering subsidies and tax incentives to public transport operators investing in electric vehicles, hydrogen-powered solutions, and biofuel technologies. Ekins et al. [56] highlight the economic benefits of investing in sustainable development, noting that subsidizing low-emission transport technologies represents a cost-effective approach to climate policy.

Among financial and legal strategies, particular attention should be given to restricting car access in city centers through the establishment of low-emission zones and prioritization of pedestrian and cyclist infrastructure. Regulations that limit the entry of vehicles not meeting specific emission standards like Low Emission Zones (LEZs) encourage users to switch to more environmentally friendly modes of transport. Pfoser [57] analyzes the effectiveness of such legal and regulatory measures, including access restrictions and low-emission zones, in the context of freight transport. These conclusions are also applicable to urban passenger transport, as the underlying legal mechanisms are comparable.

Another important strategy involves the mandatory development of sustainable mobility plans. In many jurisdictions, statutory requirements oblige cities or metropolitan areas to prepare and implement Sustainable Urban Mobility Plans (SUMPs) that incorporate climate and environmental objectives. As noted by Kilic and Demirel [41], governance reform is essential in this context. It requires changes in the legal and institutional frameworks governing urban transport management to enable better integration of climate, transport, and urban planning policies.

The authors analyze decision support systems in public transport planning, emphasizing the role of legal and regulatory instruments in enforcing the adoption of sustainable transport solutions. In their conclusions, they argue that the effective decarbonization of urban transport depends not only on technological advances but also on institutional and legal reforms that facilitate the implementation of integrated urban policies [15].

2.5. Behavioral Strategies

Behavioral strategies play a crucial social role and are aimed at shaping sustainable transport behavior. These strategies primarily focus on effective demand management, encouraging changes such as more eco-friendly driving habits and increased vehicle occupancy, often through the promotion of carpooling. Shifting transportation habits can be achieved through targeted education and public awareness campaigns that promote the use of public transport, cycling, walking, and car sharing. Di Ciommo and Shiftan [58] analyzed the impact of social campaigns on the travel behavior of different social groups, noting that one key factor influencing willingness to change is access to private modes of transport. The integration of social and technical dimensions in urban planning, for example, through the use of digital twins, can support more sustainable transport decisions that reflect the needs and preferences of residents.

Nochta et al. [10] emphasize the importance of co-creating transport policies with local communities through participatory planning processes. Involving residents in transport planning enhances public acceptance of measures such as vehicle access restrictions, expansion of cycling infrastructure, and improvements to public transport services, particularly for people with disabilities [59].

Additionally, the growing influence of social media, along with the promotion of environmentally friendly values by social influencers and reward systems for using public transport, should not be underestimated. Gössling et al. [60] examine emission reduction strategies in the tourism sector, such as fleet management, incentive programs for employees and customers, and integration with local policy frameworks, which can be effectively adapted to urban public transport contexts.

2.6. Organizational Strategies

Organizational strategies in transport decarbonization are highly diverse. Some focus on changes in work organization, such as the promotion of teleworking, or the implementation of green human resource management (Green HRM) practices within transport organizations. These practices include recruiting employees with a focus on sustainability, providing training in environmentally friendly practices, and introducing incentive systems that support climate-related goals. Shoaib et al. [61] examine the impact of Green HRM practices on employee organizational commitment in the industrial sector, highlighting their potential applicability to the urban transport sector. The authors underscore the importance of green human capital in achieving environmental objectives.

The implementation of mobility management systems in both public and private organizations also plays a key role. These systems promote low-emission modes of transport among employees and clients, for example, through subsidized employee transport passes, the development of bicycle infrastructure, or carpooling programs [60].

Other organizational measures include route optimization, more efficient use of vehicle space in freight transport, and promoting modal shifts in freight logistics [62,63]. Kilic and Demirel [41] propose the adoption of advanced decision support systems like those based on fuzzy logic in urban transport organizations to enhance route planning, resource allocation, and alignment with climate goals.

Organizational strategies also encompass integrated urban planning based on multi-level governance. This involves coordination across different levels of government (local, regional, and national) as well as cross-sectoral collaboration among transport, environmental, and urban planning authorities to implement coherent decarbonization policies [64]. In some cases, this requires restructuring existing models of transport governance. Argyriou [65] compared two organizational models used in the UK: deregulated and franchised, and analyzed their impact on the deployment of zero-emission buses. His findings indicate that the franchised model (e.g., in London) enables better coordination and stronger investment in decarbonization initiatives.

3. Methods and Tools for Decarbonizing Transport

3.1. Characteristics of Methods and Tools Used in Transport

Given the wide range of issues related to transport decarbonization, such as the electrification of public transport, the literature reveals a broad spectrum of methods and tools applied in this field. Many of these methods are based on artificial intelligence algorithms. For instance, in estimating the energy consumption of electric vehicles, determining optimal routes is essential. Heuristic algorithms are frequently employed to minimize the energy expenditure of electric vehicles [21,66,67].

The literature presents numerous effective methods and algorithms tailored to specific transportation challenges [19,68]. A commonly used approach involves optimization algorithms [38], including genetic algorithms [69] and ant colony optimization algorithms [70]. These techniques are particularly prevalent in vehicle routing problems [71,72,73]. Their main advantage lies in the ability to generate solutions within relatively short computational times. However, a key limitation is that for highly complex decision-making problems, they often yield near-optimal rather than truly optimal solutions.

Additionally, neural networks and machine learning algorithms are increasingly utilized in the transport sector, particularly for applications such as traffic forecasting [74] and vehicle speed prediction [75].

In general, the algorithms and methods applied in transport decarbonization can be grouped into several thematic areas, including sustainable transport development, intelligent transportation systems, autonomous vehicles, smart cities, and electric vehicles.

3.2. Sustainable Transport Development

The introduction of green mobility [76], including electric vehicles, is one of the key strategies for ensuring sustainable transport development in urban agglomerations. Numerous studies confirm the effectiveness of electric vehicles [77], particularly due to their zero local emissions, which is a significant advantage in densely populated urban areas. Unlike conventional vehicles, electric vehicles require charging at designated stations because of their limited battery capacity [26,78]. To evaluate exhaust emissions and fuel consumption under varying thermal conditions during engine start-up, various tests are conducted under real-world traffic scenarios [79].

Advanced artificial intelligence algorithms, such as neural networks [80] and machine learning methods [81], have demonstrated high effectiveness in supporting sustainable transport development [82].

In the domain of transport electrification, two main decision-making challenges are commonly addressed: determining optimal locations for charging stations [16,83] and minimizing the energy consumption of electric vehicles. In both cases, heuristic algorithms are among the most frequently applied artificial intelligence techniques. For instance, studies [84] and Ref. [85] employed genetic algorithms to determine the optimal placement of charging stations, while study [86] utilized a Multi-Objective Particle Swarm Optimization approach. The problem of minimizing energy expenditure was explored in [87], where a genetic algorithm was combined with a neighborhood search technique. The results confirmed the effectiveness of this hybrid method. A detailed overview of algorithms used for electric vehicle route optimization is provided in [88]. Beyond AI-based solutions, several other optimization methods have been applied in the context of transport electrification. These include linear programming, simulated annealing, tabu search, variable neighborhood search, iterated local search, and adaptive large neighborhood search.

Smart cities also play a crucial role in promoting sustainable transport development [89]. Comprehensive research on smart city systems and their integration with AI technologies is presented in [90]. Seven key research areas were identified where AI algorithms are applicable: safety, quality of life, energy, mobility, health, pollution, and industry. Urban mobility, in particular, has undergone significant innovations thanks to AI applications in technologies such as autonomous vehicles (AVs), electric vehicles (EVs), and unmanned aerial vehicles (UAVs). Study [91] presents the findings of a systematic review of 93 articles, showing that the use of AI in smart cities is an emerging area of both research and practical implementation. AI applications in this context are primarily focused on improving business efficiency, data analytics, education, energy systems, environmental sustainability, healthcare, land use, safety, transportation, and urban management. Further comprehensive analyses and examples of practical implementation in smart cities can be found in [92,93].

3.3. Intelligent Transportation System

Popular algorithms used in Intelligent Transportation Systems (ITSs) are artificial intelligence algorithms [94]. Article [95] summarizes various challenges facing the transportation industry, classified under intelligent transportation systems, such as traffic management, safety management, production management, and logistics. The goal of implementing AI techniques in ITSs is to reduce traffic congestion, shorten travel times, and improve the overall efficiency and cost-effectiveness of the transportation system. AI algorithms assist in identifying road hazards, alleviating traffic congestion [96], reducing greenhouse gas and air pollution emissions, designing and managing transport networks, and analyzing travel demand and pedestrian behavior [97].

AI algorithms in ITSs are applied to traffic management, public transportation systems, and urban safety oversight. The use of AI tools to predict road traffic volumes and congestion levels is described in [98]. This paper presents a range of algorithms, including Extreme Learning Machine (ELM), Recurrent Neural Network, Convolutional Neural Network, Deep Machine Learning, Support Vector Machine, Decision Tree, Regression Model, Shallow Machine Learning, Bayesian Network, Hidden Markov Model, Fuzzy Logic, and Probabilistic Reasoning. Traffic light management using AI algorithms is discussed in [99]. Agent-based programming was applied to determine optimal durations for red and green traffic signals. A comprehensive approach to traffic light control using AI is further detailed in [100,101].

AI algorithms are also used to enhance driver safety. Forecasting driver accidents using machine learning is discussed in [102]. The paper presents intelligent driver health monitoring technologies capable of measuring physiological parameters in real time, transmitting data to the cloud, and analyzing it using machine learning, AI, and big data techniques.

In [103], AI algorithms are applied to the control of autonomous vehicles. A similar study is presented in [104], which examines the use of AI in autonomous braking systems. It was found that vehicles equipped with such systems reduce collision rates by approximately 38 percent. Algorithms such as machine learning (ML), deep learning (DL), deep neural networks (DNNs), and natural language processing (NLP) play a central role in autonomous vehicle technology. Machine learning algorithms are extensively discussed in [105,106].

4. Conclusions, Challenges, and Directions for Further Work

As indicated in the article, the problem of transport decarbonization is both difficult and highly complex. The conclusions drawn from the analysis highlight the need for a holistic approach that integrates technical, operational, environmental, social, legal, and political dimensions to effectively accelerate transport decarbonization and support the development of smart cities [107]. Moreover, the coordinated integration of electromobility with distributed renewable energy sources is of key importance, as it enables a reduction in system costs and increases grid flexibility [108].

Despite the conclusions of numerous studies emphasizing the urgency of action, many regulatory and societal barriers continue to hinder the implementation of environmentally friendly solutions. This is due, among other factors, to the absence of a comprehensive approach to assessing the actual life-cycle impacts of electric vehicles, the substantial investment required to modernize power grids, and the need to develop charging infrastructure for electric vehicles [107,108]. Further research should focus on the development of multi-criteria decision models, the application of advanced artificial intelligence algorithms, and improved coordination of policies and regulations [14,109].

The analysis of transport decarbonization strategies and the potential application of various methods and tools to support sustainable transport development reveals several challenges related to the selection of the following:

• Modes of transport (subsystems) for the implementation of sustainable development strategies, including public land transport, public maritime transport, public air transport, bicycle transport, pedestrian transport, and mixed modes of transport such as park and ride [110].
• Appropriate locations for infrastructure, such as vehicle and bicycle sharing points, energy storage facilities, and charging stations [13,36,111].
• Appropriate strategies or the ranking of strategies related to sustainable development [107,109].
• Evaluation methods for innovative power technologies, including electric vehicles, renewable fuels, and energy sources such as solar, wind, hydrogen, and hybrid solutions [112,113].
• Energy management approaches for urban transport systems [66,108,111,114,115,116].
• The rationale for implementing shared mobility systems such as carsharing, bike sharing, and ridesharing [36,109].
• Financing methods for the implementation of sustainable development strategies [108,114].
• The ranking and identification of stakeholders responsible for shaping and implementing these strategies and managing transport decarbonization efforts [107].
• Logistical challenges specific to cold-chain vehicles related to maintaining cargo freshness while simultaneously planning battery charging [115].

The necessary actions should include the following:

• Formulating medium- and long-term green transport development plans, with a clear definition of the status and role of electric vehicles within the urban green transport network [107].
• Increasing investment in research and development related to key battery and electric vehicle technologies in order to improve energy density, charging speed, and battery life [112,113].
• Conducting market and user behavior analyses, along with detailed emissions assessments across the entire life cycle of electric vehicles, from production to end-of-life, to better understand their comprehensive environmental impact [66,107,115].
• Developing digital twin concepts to monitor complex procedures and reduce uncertainty and risk [10,113].
• Expansion of vehicle-to-grid (V2G) services and virtual energy storage systems (VESS) to stabilize the power grid, taking into account the strategic placement of V2G hubs in locations with extended vehicle parking times and their integration with commercial buildings [36,111].
• Expanding charging infrastructure and optimizing its location. Forecasting the optimal placement of charging stations requires a comprehensive approach that significantly reduces range anxiety and ensures equitable access to infrastructure. The use of GIS tools and publicly available spatial data enables efficient identification of suitable locations for new charging stations [13].
• Advancing technological innovations, particularly in the development of sophisticated algorithms such as DHFO and ECNN, as well as artificial intelligence technologies that improve the accuracy of charging station location forecasting and management. For example, wireless power transfer (WPT) technology enables contactless and safe charging, which is particularly important for autonomous systems and smart cities. Further breakthroughs are anticipated in battery technologies, including improvements in energy density, charging speed, and battery lifespan [14,113].
• Design of dynamic tariff systems aimed at maximizing photovoltaic energy self-consumption by EV users, which according to simulations can increase the self-consumption rate by at least 13 percent and reduce charging costs by approximately 25 percent [114].
• Optimization of cold-chain vehicle schedules using advanced algorithms to minimize energy consumption and prevent the degradation of cold-chain quality [115].
• Improvement of energy efficiency in logistics hubs through the optimal selection of management strategies, which helps reduce emissions and operational costs in urban terminals [116].
• Implementation of signal prioritization algorithms in traffic management to reduce energy consumption while maintaining traffic flow continuity in coordinated systems [110].

Considering the above, it is important to emphasize that electric vehicles contribute to reducing air pollution and noise, while also helping to mitigate the urban heat island effect. These benefits lead to a higher quality of life in cities. Additionally, new optimization models and algorithms enable the effective management of electric vehicle fleets.