The Role of Intelligent Transport Systems and Smart Technologies in Urban Traffic Management in Polish Smart Cities

Abstract

Today’s cities are facing the challenges of increasing traffic congestion, emissions, and the need to improve road safety. The solution to these problems is the use of artificial intelligence (AI) and the Internet of Things (IoT) in intelligent traffic management. The purpose of the article is to analyze and evaluate AI- and IoT-based solutions implemented in Polish cities and to identify innovative proposals that can improve traffic management. The study uses a mixed-method approach, including the analysis of crowdsourced mobility data (from GPS, smartphones, and municipal reports), GIS tools for mapping congestion, big data analytics, and machine learning algorithms, to evaluate trends and predict traffic scenarios. The evaluation focused on seven major Polish cities—Warsaw, Krakow, Wroclaw, Gdansk, Poznan, Katowice, and Lodz—where intelligent transportation systems such as dynamic traffic lights, intelligent pedestrian crossings, accident prediction systems, and parking space management have been implemented. The effectiveness of these solutions was assessed using the following six key indicators: waiting time at intersections, travel time, congestion level, CO₂ emissions, energy consumption, and number of traffic incidents. The article provides a comprehensive analysis of these solutions’ impacts on traffic flow, emissions, energy efficiency, and road safety. A key contribution of the paper is the presentation of new proposals for improvements, such as the inclusion of behavioral data in traffic modeling, integration with GPS navigation, and dynamic emergency and public transport priority management. The article also discusses further digitization and interoperability needs. The findings show that the implementation of intelligent transportation systems not only improves urban mobility and safety but also enhances environmental sustainability and residents’ quality of life.

1. Introduction

With rapid social and economic development, the modern world is facing a number of challenges related to urbanization, traffic safety, and demographics. Population growth, increasing urbanization, and the growing number of vehicles on the road are all contributing to an increased risk of traffic accidents, which poses a serious challenge to urban transportation systems and public policies around the world [1]. At the same time, predictions about the future demographic structure of cities point to the need to adapt urban infrastructure to the growing population and the changing needs of residents [2,3].

This is supported by data from the World Health Organization (WHO), according to which there will be about 1.19 million road fatalities worldwide in 2021, a decrease of 5% compared to 2010, when the number was 1.25 million [4]. Road accidents remain one of the leading causes of death worldwide, especially in the context of growing urbanization and an increase in the number of vehicles on the road.

United Nations (UN) projections indicate that by 2050 about 68% of the world’s population will live in cities, a significant increase from 55% in 2018 [5]. As of 1 January 2025, the world’s population was about 8.09 billion people [6]. In 2024, the world’s population will increase by more than 71 million, an increase of 0.9% from the previous year [7].

The increase in the number of residents and thus the number of vehicles on urban roads continually leads to increased risks of congestion and traffic accidents and contributes to worsening environmental problems [8,9,10]. Transportation is one of the key sources of carbon dioxide emissions worldwide. According to the International Energy Agency (IEA), the transport sector accounts for about 24% of global CO₂ emissions, almost half of which come from road transport, mainly cars and light commercial vehicles [11]. The situation is similar in the European Union, where road transport accounts for about one-fifth of total CO₂ emissions and as much as 60.6% of emissions are generated by passenger cars, significantly affecting air quality and contributing to climate change [12,13,14,15,16,17].

In Poland, issues related to road safety and urbanization are equally challenging. According to data from the Central Statistical Office (CSO), in 2023 there were about 21,000 road accidents in Poland, with more than 1800 deaths and more than 24,000 injuries [18]. Most of these incidents occurred in urban areas, highlighting the need for further investment in infrastructure to improve road safety.

In addition, it is worth noting the number of vehicles in Poland, which is steadily increasing; in 2023, the number of registered passenger cars will reach more than 27 million, an increase of 3% compared to the previous year [19]. Increasing traffic volume affects not only safety but also air quality as road transport is responsible for about 15% of national CO₂ emissions, most of which come from large cities such as Warsaw, Krakow, and Wroclaw.

Urbanization in Poland is also progressing—currently about 60% of the country’s population lives in cities and forecasts indicate that this percentage will rise to 68% by 2050 [20]. As a result, it is crucial to implement modern transportation solutions, such as the development of public transportation systems, intelligent traffic management, and the promotion of low-emission modes of transportation.

Without appropriate measures, such as, but not limited to, the implementation of intelligent traffic management systems based on AI and IoT, cities could face serious problems related to air pollution, noise pollution, and a decline in the quality of life for residents. Smart technologies enable real-time dynamic traffic monitoring and management to optimize vehicle flow, reduce emissions, and improve road safety [21,22,23,24,25,26,27,28,29,30].

Against the backdrop of growing urbanization and an increase in the number of vehicles on the road, the application of artificial intelligence and the Internet of Things is becoming not only a solution to improve traffic flow but even a necessity for sustainable urban development. The introduction of advanced analytical systems, using data from IoT sensors, surveillance cameras, and navigation systems, makes it possible to forecast road congestion, automatically adjust traffic lights, and dynamically manage public transportation [31,32,33,34,35,36,37].

Traffic management using AI and IoT in smart cities is a key element of forward-looking urban policy. Only through the implementation of modern technological solutions will cities be able to effectively meet the challenges posed by increasing urbanization, increased traffic congestion, and the need to reduce the negative impact of transportation on the environment. Investment in intelligent transportation systems is not only a step towards greater transportation efficiency but also a significant contribution to improving the quality of life of residents and achieving sustainable development goals [38].

The article consists of several key sections that comprehensively present the topic of traffic management in smart cities and the use of artificial intelligence (AI) and the Internet of Things (IoT) in the process. The introduction discusses the main challenges of urbanization, traffic safety, and demographic change, highlighting the impact of population and vehicle growth on urban infrastructure and the need to adapt transportation policies to dynamic socioeconomic changes. Drawing on data from the World Health Organization (WHO) and UN projections on urbanization, the scale of the problem and the need for modern solutions are shown. Next, the methodology section presents the tools and approaches used in the analysis of transportation systems and technologies supporting traffic management, including a review of the literature and analytical tools for evaluating the effectiveness of AI- and IoT-based solutions. The next section of the article, the analysis of results, discusses existing technological solutions used in smart cities, highlighting their impact on optimizing traffic flow, reducing emissions, and improving road safety, with the effectiveness of implemented policies assessed based on reports from the International Energy Agency (IEA) and the European Parliament. The next discussion section focuses on current technology trends and future directions in the development of intelligent transportation systems, addressing the challenges of urban system integration, interoperability, and data privacy. This article concludes with a summary and conclusions, highlighting the study’s key findings and the need for further investment in intelligent traffic management systems. It is pointed out that without modern technology cities will not be able to effectively cope with increasing traffic volumes, which could lead to increased emissions, air pollution, and reduced quality of life for residents, and the article concludes by pointing out the limitations of the study and suggesting future research activities.

Although there is a growing body of research on the application of artificial intelligence (AI) and Internet of Things (IoT) technologies in transport systems, most of the existing literature focuses on isolated technological solutions or case studies from Western European or Asian cities. For example, studies by Verma et al. [30] and Ouallane et al. [31] examine the use of AI in adaptive traffic signals or vehicle routing, but they do not address the broader systemic integration of such technologies in urban environments. Similarly, Wang et al. [32] and Padhiary et al. [34] explore the use of the IoT in smart mobility but limit their scope to conceptual models or experimental applications.

There is a clear lack of comparative and practice-oriented research analyzing the real-world implementation of intelligent transport systems (ITSs) in Central and Eastern European cities, particularly in Poland. Moreover, few studies comprehensively evaluate the cumulative impact of multiple smart city technologies—including ITSs, AI, IoT, smart grids, and smart lighting—on traffic flow, safety, emissions, and urban mobility efficiency.

This study addresses an important gap in the current literature by providing a comparative, real-world analysis of the implementation of intelligent transport systems (ITSs) and smart mobility technologies across seven major Polish cities: Warsaw, Cracow, Wroclaw, Gdansk, Poznan, Katowice, and Lodz. Each of these cities has adopted distinct strategies based on AI, IoT, and smart infrastructure to improve urban traffic management. By analyzing local implementations such as Warsaw’s TRISTAR traffic control system, Cracow’s intelligent transport system (ITS), Gdansk’s smart parking initiative (SOLEZ), Wroclaw’s smart street lighting, or Katowice’s AI-based accident prediction system, this paper demonstrates the measurable impact of these solutions on congestion, emissions, travel times, and safety [35,36,37].

This paper contributes actionable, city-specific recommendations to enhance the effectiveness of existing systems, such as integrating behavioral data analytics, implementing dynamic public transport priority corridors, or adopting predictive weather-based traffic control.

As such, the paper contributes not only to the academic discourse on AI- and IoT-based urban mobility but also to the development of practical policy frameworks for cities facing similar challenges related to urbanization, congestion, and sustainability in the European periphery. In light of these objectives and contributions, the guiding research question of this study is as follows: How effectively have intelligent transport systems (ITSs), including AI- and IoT-based solutions, been implemented in major Polish cities, and what measurable impact do they have on urban traffic efficiency, safety, and sustainability?

2. Materials and Methods

In order to optimize routes for all types of vehicles in smart cities, this study uses crowdsourcing data that provides detailed information on the mobility of residents. These data are a powerful tool for urban planners to make decisions based on real traffic patterns. Analysis of mobility data provides an accurate overview of how urban infrastructure is used and helps in the planning of more efficient and citizen-friendly transportation solutions [39,40,41,42]. In the context of dynamic urbanization and an increase in the number of vehicles, whose impact on the quality of life of the population is becoming more and more noticeable, effective traffic management is a key challenge [43,44,45,46,47,48,49,50,51,52,53]. The article seeks to present practical smart transportation solutions that can help improve transportation efficiency, reduce emissions, and increase road safety.

Crowdsourcing data come from a variety of sources, such as traffic participants’ smartphones, GPS-equipped vehicles, and navigation apps (e.g., Google Maps, Waze). They provide information on traffic volumes, average speeds, travel times, and the number of stops on key road sections. Integration of these data with urban traffic management systems enables ongoing analysis of the traffic situation and implementation of solutions tailored to current transportation needs.

From an ethical standpoint, the use of crowdsourced mobility data in this study raises important considerations. Although the data used were anonymized and aggregated, ethical concerns remain regarding user consent, privacy, and data security. In compliance with EU General Data Protection Regulation (GDPR), all collected data sources, such as those from GPS, navigation apps, and municipal reports, were only accessed through platforms operating under transparent privacy policies. No personally identifiable information (PII) was collected. Furthermore, this study acknowledges the potential risks associated with algorithmic profiling and the re-identification of anonymized data, which necessitate careful system design, robust encryption protocols, and public communication strategies to build trust and social acceptance. These aspects are crucial to ensuring the ethical application of AI and IoT technologies in urban traffic management and highlight the importance of legal, technical, and social safeguards in future deployments.

The methodological framework of this study consists of three main stages:

• Data collection: We used crowdsourced mobility data obtained from GPS-equipped vehicles, public transportation operators, and smartphone-based navigation apps (e.g., Google Maps, Waze). Additionally, municipal reports and technical documentation from local traffic authorities (e.g., ZDM in Warsaw) were consulted to obtain before-and-after indicators of system implementation;
• Data processing and analysis: The collected data were analyzed using the following:

  (a)

  GIS tools to map congestion levels, road use patterns, and emissions;

  (b)

  Big data analytics to process large sets of movement data in different time frames (pre- and post-implementation of ITSs);

  (c)

  Machine learning algorithms to identify traffic trends, predict congestion peaks, and simulate the impact of new technologies on mobility;

• Evaluation and interpretation of results: The effectiveness of the implemented smart transport systems was evaluated based on the following six key indicators:

  (a)

  Average waiting time at intersections;

  (b)

  Overall travel time;

  (c)

  Level of traffic congestion;

  (d)

  CO₂ emissions;

  (e)

  Energy consumption of infrastructure;

  (f)

  Number of traffic incidents.

This study focuses on seven major Polish cities—Warsaw, Cracow, Wroclaw, Gdansk, Poznan, Katowice, and Lodz—selected based on their documented implementation of smart transport solutions and participation in national and EU-funded smart city initiatives.

The selection of the seven Polish cities, Warsaw, Cracow, Wroclaw, Gdansk, Poznan, Katowice, and Lodz, was based on their advanced level of implementation of smart city technologies, documented participation in national- and EU-funded smart mobility projects, and availability of empirical performance data. These cities are recognized as leaders in Poland in deploying AI- and IoT-based transport systems and their inclusion allows for a meaningful comparative analysis across diverse urban contexts.

These metrics were compared before and after the implementation of smart solutions using official statistics and traffic management system reports from 2020 to 2024. The empirical approach aims to quantify the functional impact of AI- and IoT-based transport systems on urban mobility, environmental sustainability, and safety.

3. Results

3.1. Poland and Smart City—Analysis of Results

The development of technology and society’s growing demands for an increasing quality of life are causing cities around the world to strive to meet the challenges of modern times by implementing the smart city concept. A smart city is one that uses modern technologies and innovative solutions to efficiently manage resources, improve the lives of residents, and promote sustainable development [54,55,56,57,58,59,60,61,62,63]. The evaluation of a city as a smart city is based on a number of factors that differ from the traditional approach characteristic of conventional cities. A comparison between a smart city and a conventional city is shown in

Table 1. Comparative analysis of smart cities and conventional cities.

ASPECT	SMART CITY	CONVENTIONAL CITY
Transport	Intelligent traffic management systems, electric communications, and integration of public transportation with mobile applications.	Traditional transportation with limited access to technology and a lack of synchronization between transportation modes.
Energy	Use of renewable energy sources and smart grids.	Mainly fossil fuels with no optimization of energy consumption.
Management of municipal waste	Intelligent segregation and recycling systems and waste disposal optimization.	Manual segregation and uncoordinated waste management.
Communications	Platforms for citizen participation and apps that allow people to submit problems and ideas.	Traditional methods of communication (e.g., town meetings, paper forms).
Safety of residents	AI-based monitoring, smoke and pollution detectors, and alarm systems.	Basic monitoring systems and no advanced security technologies.
Environment	Air quality monitoring systems and green infrastructure projects.	No advanced environmental protection systems.
Education and innovation	Support for startups, development of new technologies, and cooperation with scientific centers.	Limited support for innovation and a lack of technological development in education.

Source: own study based on: [54,55,56,57,58].

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Key areas of smart cities include the economy, mobility, the environment, governance, and community engagement [64,65,66,67,68,69,70]. In the context of mobility, traffic management plays a special role, which through the use of artificial intelligence (AI) and the Internet of Things (IoT) offers innovative, practical solutions to support the interactivity and efficiency of urban infrastructure.

Modern technologies, such as AI and the IoT, make it possible to monitor and optimize traffic in real time, resulting in smoother transportation, reduced traffic congestion, and reduced emissions [71]. By integrating with other elements of urban infrastructure, intelligent traffic management systems also allow for an increased awareness of citizens and their active involvement in decision-making processes. As a result, the implementation of such solutions not only improves the comfort of movement but also supports the achievement of sustainable development goals [72,73,74,75,76,77].

The active involvement of civil society in the implementation of smart city solutions is key to successfully addressing the growing challenges of urbanization [78]. Moreover, the smart city framework takes into account the environment as an important element in achieving sustainable development goals, as reflected in practices such as promoting green transportation and minimizing the negative impact of infrastructure on the environment.

Figure 1 depicts a proposed roadmap for the implementation of smart city principles, with a particular focus on traffic management solutions that are already being applied in Polish cities.

The proposed scheme for implementing the smart city program shows the key stages of its implementation and the interdependencies between them. The process begins with the definition of the vision, goals, and strategies that form the foundation of a smart city. This is followed by an assessment of the city’s capabilities and a gap analysis, which identifies areas for improvement such as transportation infrastructure, traffic management systems, or communication technologies. At the same time, measurement of the effectiveness of activities and the level of development is carried out, allowing the effectiveness of implemented solutions to be monitored and the strategy to be adapted to the city’s current needs. A key step in the process is the development and implementation of the smart city program, which integrates modern technologies, including artificial intelligence (AI) and the Internet of Things (IoT), to optimize city operations. The follow-up includes the activation of opportunities, i.e., the practical implementation of solutions to improve traffic management such as the use of smart traffic signal control systems. At the same time, a transformation of urban services is taking place, including the modernization of transportation infrastructure, the introduction of dynamic monitoring systems, and the improvement of public transportation [79,80,81,82].

The whole process is part of the idea of sustainability, and the use of AI and IoT makes it possible to effectively manage traffic, reduce road congestion, reduce emissions, and increase road safety.

In Poland, there is no single central body that officially grants “Smart City” status. However, the evaluation and certification of cities as smart is carried out on the basis of various standards, rankings, and certifications, both national and international. Polish cities are recognized as smart based on the activities of institutions and initiatives involved in assessing and supporting the smart city concept, as shown in

Table 2. Institutions and initiatives supporting the smart city concept in Poland.

Institution/Program	Scope of Activity
Ministerstwo Rozwoju i Technologii (MRiT)	Supporting smart city projects, sustainable development strategies [80].
Polski Komitet Normalizacyjny (PKN)	Development of ISO smart city standards [81].
Stowarzyszenie “Miasta w Internecie” (SMWI)	Promoting digital transformation of cities and educational projects [82].
Fundacja Smart City Polska	Supporting cities to implement new technologies and certifications [83].
ISO 37120	International Certificate for Quality of Life in Cities [84].
Smart City Index	Ranking of cities in terms of innovation and technology [85].
European Smart Cities Initiative	EU initiatives to promote sustainable urban development [86].
Horizon Europe	Support for smart city projects in sustainable mobility and energy [87].
URBACT	European cities’ cooperation program on smart governance [88].

Source: own study based on [83,84,85,86,87,88,89,90,91].

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In Poland, the development of cities towards the concept of a smart city is based on the implementation of modern technologies, sustainable development, and the improvement of residents’ quality of life. The integration of artificial intelligence (AI) and the Internet of Things (IoT) plays a crucial role in optimizing urban traffic management, reducing congestion, and enhancing mobility. Although there is no single central authority that grants the official status of a ‘Smart City’, there are international and national standards, rankings, and certifications that help to assess the level of intelligence and innovation in Polish cities. The adoption of AI-driven traffic systems, smart sensors, and real-time data analytics contributes to more efficient and eco-friendly transportation networks, ultimately shaping the future of urban mobility.

Based on the activities of institutions and initiatives that support the smart city concept, such as ISO standards, smart city index rankings, or European programs URBACT and Horizon Europe, Polish cities that meet all key aspects of smart cities include Warsaw, Cracow, Wroclaw, Gdansk, Poznan, Katowice, and Lodz. These cities benefit from a variety of funding sources, including national and European subsidies, private investment and public–private partnerships, which facilitate the implementation of smart city solutions. These initiatives not only make cities more efficient but also contribute to environmental sustainability, economic growth, and improved quality of life for all residents. The characteristics of smart solutions in Polish cities, along with their benefits and sources of funding, are shown in

Table 3. Smart cities in Poland.

City	Main Solutions Smart City	Benefits	Funding Source
Warsaw	TRISTAR traffic management system	Reduce traffic congestion by 15% and improve the efficiency of public transport	EU funds and city budget
Cracow	Traffic monitoring—ITS	CO2 emissions reduced by 10% and pedestrian safety improved	EU funds and public–private partnerships
Wroclaw	Smart lighting	Energy savings of 40% and improved occupant comfort	EU funds and national programs
Gdansk	SOLEZ smart parking system	Reducing congestion in downtown and reducing emissions	EU funds and city budget
Poznan	Intelligent active pedestrian crossings called “SeeMe”	Increase pedestrian safety and improve access to urban information	City budget and EU grants
Katowice	Katowice Intelligent Monitoring and Analysis System—road accident prediction system	Predicting a place with a high risk of collision	EU funds and own funds
Łódź	Smart energy management—smart grid	Improves safety and saves energy resources	Domestic funds and private investment

Source: own study.

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3.2. Analysis of Smart Cities in Poland—Implementations of AI and IoT Technologies in Traffic Management and Recommendations for Further Development

By implementing modern AI and IoT technologies in major Polish cities such as Warsaw, Cracow, Wroclaw, Gdansk, Poznan, Katowice, and Lodz, traffic management has been significantly improved. Innovative solutions have helped to reduce congestion, reduce CO₂ emissions, and increase traffic flow.

Warsaw has implemented the TRISTAR integrated traffic management system, which dynamically adjusts traffic light cycles to the current traffic volume. The system uses AI for real-time traffic analysis and the IoT in the form of sensors and cameras to monitor the traffic situation. The differences before and after the implementation of AI and the IoT are shown in

Table 4. Comparison of the efficiency of traffic management in Warsaw before and after the implementation of the integrated TRISTAR system using AI and the IoT.

Indicator	Before TRISTAR	After TRISTAR	Effect
Waiting times at intersections	∼100 s	∼80 s	Reduction of 20%
Travel time on key routes	∼30 min	∼25.5 min	Reduction of 15%
Length of congestion during peak hours	Long traffic jams in all directions	Shortened by dynamic signaling	A marked improvement in traffic flow
Green light optimization	No dynamic control	Extended green light in loaded directions	Effective reduction in congestion
Exhaust emissions	High, due to long standstill time	Reduced by shorter waiting time	Reduction in the negative impact on the environment
Response time to changes in traffic volume	Manual and slow	In real time thanks to AI	Faster adaptation to the traffic situation

Source: own study based on [35,36,37,92].

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Additionally, from the company’s own research, among operators of urban transportation systems in Warsaw and traffic participants, it can be indicated that AI and IoT solutions have contributed to the following:

(a)

Less fuel consumption: By reducing stopping time at intersections, drivers burn less fuel.

(b)

Smoother rush hours: The system reduces traffic jams, improving driving comfort during peak hours.

(c)

Greater predictability of travel times: The system helps residents better plan their routes by improving key transportation corridors.

Zarząd Dróg Miejskich w Warszawie, based on a report on the implementation of the TRISTAR integrated traffic management system, indicated that at intersections with the highest traffic volumes waiting times during rush hour were reduced by up to 30 s. In addition, during the busiest hours (7:00–9:00 a.m. and 4:00–6:00 p.m.) there was a reduction in traffic congestion of about 10–15% [92].

Based on the research conducted and the results of the implementation of the TRISTAR integrated traffic management system in Warsaw, additional improvements can be identified that could further increase the efficiency of the system and improve the quality of urban traffic management. These improvements are shown in

Table 5. Suggestions for improving the traffic management system in Warsaw.

Area for Improvement	Suggestions for Improvement
Expanding data analysis to include driver behavioral data	Using AI to analyze data on driver behavior, such as average speed, reactions to traffic signal changes, or patterns of using different traffic corridors, to better adjust traffic control algorithms.
Integration with GPS navigation and mobile applications	The TRISTAR system could work with popular navigation applications (Google Maps and Waze) to inform drivers in real time about traffic changes, alternative routes, and estimated travel times.
Implementation of dynamic priority corridors	Based on current data, the system could dynamically change priorities at intersections for public transportation, ambulances, or vehicles traveling on key routes during rush hour.
Automatic management of failures and random events	Expanding the system to include functionality for the rapid response to emergency events (vehicle breakdowns, accidents, and road works) by automatically adjusting traffic signals and suggesting detours.
Education and communication with traffic participants	Regular information campaigns and real-time notifications about the benefits of complying with the system’s rules (e.g., appropriate speed for a green wave) can increase the effectiveness of the solutions.
Monitoring and optimization of key nodes	Introducing additional sensors and cameras to monitor the busiest hubs, which will allow the system to react to congestion in real time and help to better plan investments.
Zonal speed and emission limits	Introducing a dynamic speed management system in specific zones to reduce CO2 emissions and improve road safety.
Integration with intelligent vehicle systems (V2X—vehicle-to-everything)	The TRISTAR system could be compatible with vehicle-to-everything (V2X) technology, enabling communication between vehicles and road infrastructure to improve traffic flow and increase safety.

Source: own study.

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The proposals presented for improving the traffic management system in Warsaw are based on the use of modern technologies, such as AI and the IoT, and on analysis of the experience of implementing the TRISTAR system. The use of these solutions can significantly increase the efficiency of traffic management, improve the fluidity of transportation, reduce emissions, and increase the comfort of road users.

Cracow, as another smart city in Poland, has implemented the intelligent transport system (ITS), which informs drivers of the optimal speed so that they do not have to stop at traffic lights [37]. This technology, aided by AI and the IoT, has improved the city’s road transportation system and reduced CO₂ emissions, which has been the city’s biggest concern. An analysis of the detailed differences before and after the implementation of AI and the IoT in Cracow is presented in

Table 6. Comparison of traffic management efficiency in Cracow before and after ITS implementation.

Indicator	Before ITS	After ITS
CO2 emissions	No optimization and higher emissions	Reduce emissions by 12%
Fuel consumption	Greater fuel consumption	Reduction in consumption by 10%
Smoothness of travel	Stopping at traffic lights and more frequent traffic jams	Smoother journeys and less frequent stops
Use of IoT technology	Lack of application of modern technologies	Real-time data collection and analysis
Use of AI	No predictive analytics.	Automatic optimization of traffic lights
Response to emergency situations	Manual management and no automatic response	Immediate adaptation of the system to events on the road

Source: own study based on: [37,93].

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The implementation of the ITS system in Cracow represents a breakthrough step towards smart city management, demonstrating how modern technologies can support sustainable development and respond to the challenges of urbanization. The ITS system is an example of the use of advanced technological tools that significantly improve the functioning of transportation in urban agglomerations. The implemented solutions have contributed to noticeable environmental benefits, such as reduced carbon dioxide emissions and reduced fuel consumption. Such effects are of great importance, both for the protection of the environment and for improving the quality of life of residents who feel the changes in the form of cleaner air and greater comfort of movement.

The ITS system in Cracow was designed based on artificial intelligence (AI) and Internet of Things (IoT) technologies, which enabled dynamic traffic management in real time. This made it possible to not only optimize the flow of vehicles within the city but to also manage the transportation infrastructure more efficiently, including both public transportation and individual car transport. As a result, road congestion has been reduced, travel times have been shortened, and the capacity of major thoroughfares has been increased. These benefits are the result of comprehensive analyses of reports and public consultations, which clearly indicate that the use of AI and IoT technologies in the ITS system has contributed to achieving goals such as the following:

(a)

Real-time traffic optimization, which allows traffic signals to be better adapted to current road conditions;

(b)

Better management of transportation infrastructure, including prioritizing public transportation and monitoring the technical condition of key infrastructure components;

(c)

Raising residents’ quality of life by reducing traffic noise and improving air quality, which is particularly important in the context of urban pollution challenges.

Despite significant technological advances and numerous positive effects resulting from the implementation of ITS, Cracow still faces challenges that require further action. There are still areas for further improvement in the urban transportation system. Proposed directions for further development are presented in

Table 7. Suggestions for improving the traffic management system in Cracow.

Area for Improvement	Suggestions for Improvement
Expansion of the system to suburban areas	It will ease commuting from the outskirts of the city and reduce traffic congestion in the center. As a result, the benefits of the ITS system will be felt on a larger scale.
Integration with other modes of transport	Combining the system with buses or streetcars will allow for more comprehensive traffic management, which will improve the fluidity of urban transportation.
Driver education	Awareness campaigns will introduce the benefits of following ITS recommendations. Better awareness of road users will translate into more efficient use of the technology.
Analysis of historical data	The implementation of predictive traffic models will enable better preparation for periodic changes in traffic volumes, such as during vacations or festivals.
Implementation of additional smart city solutions	Smart parking lots that work with ITS can help drivers find parking spaces quickly, improving traffic flow.

Source: own study.

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Wrocław, as another leading smart city in Poland, has implemented a modern smart street lighting system [94]. This is a key element of the city’s sustainable development strategy, which not only supports efficient management of urban infrastructure but also significantly improves the comfort and safety of residents’ lives.

The project involved a comprehensive upgrade of the existing lighting infrastructure. Traditional sodium lamps, characterized by high energy consumption and limited luminous efficiency, were replaced with energy-efficient LED fixtures. LED technology is characterized not only by significantly lower power consumption but also longer life, which translates into reduced maintenance and lamp replacement costs. In addition, the use of LEDs makes it possible to precisely control light intensity, which, combined with intelligent management systems, creates an advanced, energy-efficient lighting system [95,96,97].

An analysis of the detailed differences before and after the implementation of AI and the IoT in Wroclaw is presented in

Table 8. Comparison of traffic management efficiency in Wroclaw before and after implementation of smart lighting.

Indicator	Before the Implementation of Smart Lighting	After Implementation of Smart Lighting
Number of road accidents	~1200 per year	Decrease of 15% (~1020 per year)
Number of criminal incidents on lit streets	High levels in low-light areas	20% drop due to better lighting
Energy consumption for city lighting	~22.5 GWh per year	Reduction of 60% (~9 GWh per year)
Electricity costs	~13.5 million a year	Reduction of about 5.5 million per year
CO2 emissions associated with lighting	~10,000 tons per year	Reduction of 6000 tons per year

Source: own study based on [94,96].

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The implementation of an intelligent street lighting system in Wroclaw has brought clear benefits in many areas. The modernized infrastructure has improved safety on the city’s streets, reducing traffic accidents by 15% and reducing criminal incidents by 20%, especially in previously poorly lit areas. In addition, the use of energy-efficient LED luminaires and intelligent management systems has reduced energy consumption by 60%, resulting in financial savings of PLN 5.5 million per year and a reduction of CO₂ emissions by 6000 tons per year.

While these results are impressive, it is worth pointing out further opportunities for development. As shown in

Table 9. Suggestions for improving the traffic management system in Wroclaw.

Area for Improvement	Suggestions for Improvement
Traffic signal control	Implement an adaptive traffic signal control system based on real-time traffic volume analysis. There are places in Wroclaw with variable traffic volumes (e.g., the Grunwaldzki Square intersection) where an adaptive system would reduce congestion and improve traffic flow.
Smart parking management	Expanding the existing parking system with additional sensors and systems to direct vehicles to vacant spaces.
Infrastructure for pedestrians and cyclists	Expanding the network of bicycle routes and introducing traffic lights dedicated to cyclists. The number of cyclists in the area of Wroclaw Market Square and Słodowa Island is growing. Dedicated infrastructure would increase the safety of all traffic participants.
Integration of public transport	Introduce priority lanes for buses and streetcars on the city’s main arteries.
Monitoring air quality in the context of traffic	Installation of air quality sensors and dynamic traffic control in the most congested areas to reduce emissions.

Source: own study.

, the use of advanced AI- and IoT-based technologies can further enhance system efficiency and bring further improvements to urban infrastructure management.

Gdansk, as another smart city in Poland, has implemented a smart parking system that uses AI and IoT technologies to improve parking management in the city [98].

The project included the monitoring of parking spaces. The system provides drivers with real-time information on parking space availability via a mobile app that uses the phone’s built-in navigation to indicate the fastest route to a selected free parking space.

Through the use of smart cameras placed on streetlights the system performs rapid video analysis, providing data on the status of occupied parking spaces to both local government institutions and residents. Such a solution not only increases convenience for drivers but also contributes to reducing traffic and emissions as it reduces the need to circulate in search of a vacant space.

The project is being implemented as part of SOLEZ, an EU program that funds smart solutions and low-carbon strategies in EU cities. Gdansk, as one of the program’s ten partners, has decided to use this type of solution to analyze parking space occupancy. In this way, the city fits in with European standards promoting sustainable development and reducing emissions in urban zones.

The implementation of this system is an example of modern smart city technologies, which aim to improve the quality of life of residents and effectively manage city infrastructure. Through the use of artificial intelligence (AI) and the Internet of Things (IoT), it is possible to automatically monitor and optimize various aspects of the city’s operations, such as transportation, energy management, waste management, and public safety. The system not only increases the comfort of residents but also contributes to reducing operating costs and minimizing negative environmental impacts. An analysis of the detailed differences before and after the implementation of AI and the IoT in Gdansk, taking into account both technological and social aspects, is presented in

Table 10. Comparison of traffic management efficiency in Gdansk before and after implementation of smart parking system.

Indicator	Before the Implementation of the Smart Parking System	After the Implementation of the Smart Parking System
Average time to find a parking space	~15 min	~5 min
Site search emissions (CO2)	High—about 50 kg per day per 100 vehicles	Reduced by 30%
Number of empty trips (vehicles without a destination)	Significant, about 20% of the traffic	Reduced to 10%
Information on availability of places	None in real time	Available via mobile app
Traffic congestion near parking lots	High	Reduced

Source: own study based on [98,99,100].

.

Gdansk’s smart parking system, based on AI and the IoT, has significantly improved the efficiency of parking management in the city. Thanks to its implementation, drivers can find a vacant spot faster, minimizing search time and reducing the stress of parking in congested areas. Reducing empty trips not only reduces emissions but also benefits the environment and the quality of life of residents by improving air quality.

The introduction of the mobile app enables drivers to make informed decisions about their parking choices, which in turn improves traffic flow in the city’s hot spots. Despite these positive developments, the system still needs further improvements, as suggested in

Table 11. Suggestions for improving the traffic management system in Gdansk.

Area for Improvement	Suggestions for Improvement
Expanding the system to more areas of the city	Install smart cameras and sensors at strategic points such as train stations, industrial zones, the airport, and popular tourist districts (Old Town and Brzeźno).
Integration with public transport systems	Adding an option to reserve parking spaces at interchanges and discounts on public transportation tickets for system users.
Introducing a dynamic parking fee policy	Introduce lower fees for off-peak parking and higher fees in the most heavily trafficked locations during peak hours.
Improving data analysis technology	Implement machine learning to predict peak parking demand at specific times and locations.
Driver education and promotion of the system	Organize educational campaigns on social media, websites, and in local media to encourage drivers to download the app and use the system.
Development of electric vehicle charging infrastructure	Installation of smart chargers at covered parking lots, monitoring energy consumption and optimizing charging times.
Real-time notification system	Expanding the app to include the function of notifications about predicted parking load, warnings about nearby traffic jams, and recommendations for alternative locations.
Improving infrastructure for bicycles and scooters	Include information about the availability of bike racks and scooter parking zones in the app.

Source: own study.

.

Poznan, as part of its smart city development strategy, has implemented state-of-the-art smart pedestrian crossings that use IoT technologies and intelligent traffic management systems [101]. The goal of this initiative was to improve pedestrian safety, manage traffic more efficiently, and increase the comfort of urban space users.

In addition, in Poznan, the installation of SeeMe active pedestrian crossings has been implemented in 10 locations where dangerous situations were previously common [102]. This system improves safety through better lighting and signaling the presence of pedestrians. An analysis of the detailed differences before and after the implementation of AI and the IoT in Poznan is presented in

Table 12. Comparison of traffic management efficiency in Poznan before and after implementation of intelligent pedestrian crossings.

Indicator	Before Implementation of Smart Pedestrian Crossings	After Implementation of Smart Pedestrian Crossings
Number of accidents involving pedestrians	Frequent accidents, especially at night and in areas with poor visibility.	The number of accidents decreased by 30%.
Visibility of crossings	Poor visibility and no adapted lighting.	Intelligent sensor-activated LED lighting has improved visibility.
Pedestrian waiting time for green light	Constant, regardless of the number of pedestrians.	Waiting times have been reduced by 15% thanks to dynamic signaling.
Energy efficiency	High energy consumption of traditional systems.	The use of energy-efficient LED lamps has reduced energy consumption by 20%.
Adaptation for different users	No consideration of people with limited mobility.	The system extends the green light for slower-moving pedestrians.

Source: own study based on [101,102,103,104].

.

By using smart solutions such as IoT sensors and energy-efficient LED lighting, pedestrian safety was significantly improved, reducing accidents by 30%. The introduction of dynamic traffic lights made it possible to reduce the waiting time for a green light by 15%, which increased pedestrian comfort and improved traffic flow in the urban area. In addition, the system has been adapted to the needs of people with limited mobility, significantly increasing its functionality and accessibility.

Despite the successes achieved, there are still areas for further improvement. Suggestions for solutions that can improve the efficiency and functionality of smart crossings are presented in

Table 13. Suggestions for improving the traffic management system in Poznan.

Area for Improvement	Suggestions for Improvement
Introduce priority for pedestrians and cyclists	Integrate traffic signals with AI algorithms that prioritize pedestrians and cyclists at certain times, such as during commuting.
Real-time security monitoring	Installation of IoT cameras with the function of detecting dangerous situations, such as vehicles not stopping before a crossing. Automatic notification of relevant services.
Creation of a system of dynamic light paths	Install dynamic light lines on the roadway to visually indicate to drivers that they are approaching an active crossing.
Use of data from mobile applications	Integration with city apps that allow pedestrians to report problems at crosswalks, such as lighting that does not work or signaling times that are too short.
Increase accessibility for people with disabilities	Equip passageways with additional features, such as audible announcements tailored to people with visual impairments and push-button panels at the right height.

Source: own study.

, including expanding the system to more locations, better integration with the city’s traffic management system, and further enhancing energy efficiency. These measures could further enhance the positive effects of implementing the technology in Poznan.

Katowice is another example of a smart city in Poland. It was one of the first cities in Poland to introduce an innovative traffic accident prediction system based on artificial intelligence (AI).

The Katowice intelligent monitoring and analysis system (KISMiA) is one of the most modern city monitoring systems in Poland [105]. More than 300 cameras watch the city’s streets around the clock, and the work of the operators is supported by artificial intelligence. The system was created based on Milestone XProtect solution with IBM IOC and AI technology, with an integrated license plate recognition system and Vix Vizion Imagus facial analysis software (https://www.milestonesys.com/products/expand-your-solution/milestone-extensions/license-plate-recognition?utm_source=chatgpt.com (accessed on 11 May 2025)). The Katowice intelligent monitoring and analysis system also supports road inspections in the city. LPR (license plate recognition) camera points, located on the city’s entrance gantries, do not monitor the speed of cars in the city but are used to monitor the volume of vehicular traffic on Katowice’s main road arteries [105].

According to statistics, such an advanced solution brings tangible benefits. Thanks to the use of these cameras by the police, the fight against vehicle theft has become more effective. According to data from the Katowice police, the number of car thefts in Katowice fell from 337 in 2016 to 83 cases in 2021. At the same time, the detection rate in this category of crime increased from 16.4% to 55.8% [105].

An analysis of the detailed differences before and after the implementation of the traffic accident prediction system in Katowice is presented in

Table 14. Comparison of the efficiency of traffic management in Katowice before and after the implementation of the traffic accident prediction system.

Indicator	Before the Implementation of the Road Accident Prediction System	After Implementation of the Road Accident Prediction System
Number of traffic collisions	100% (baseline)	80% (down 20%)
Average response time of emergency services	X minutes (base value)	90% of base time (down 10%)
Traffic flow during rush hour (congestion)	High levels of traffic jams	Reduced traffic jams thanks to better traffic optimization
Pedestrian safety (number of accidents)	High risk of accidents at crossings	Better protection with smart transitions

Source: own study based on [105].

.

The project was a response to growing road safety challenges, including a high number of accidents and traffic flow problems in a rapidly growing city. Through the use of advanced technology, the city has not only improved its safety statistics but also demonstrated how modern tools can be effectively integrated into the daily operation of the metropolis.

Despite its success, the system’s implementation was not without its challenges. A key problem was integrating various data sources while ensuring residents’ privacy. In response to concerns about data protection, advanced data anonymization methods and strict data processing procedures were introduced.

In the future, there are plans to significantly expand the system with additional functionalities, such as advanced forecasting of the effects of weather changes on road safety and real-time analysis of weather conditions and their impact on traffic volume and flow. In addition, integration with autonomous vehicle systems will allow for even better coordination of transportation and increased safety by automatically adjusting speeds and routes depending on road conditions.

Katowice is also considering close cooperation with other cities in Poland to create a nationwide network for sharing data and best practices in improving road safety. This will enable the development of uniform traffic management standards and the implementation of modern technologies in different regions of the country. The cooperation may also include the development of intelligent road infrastructure management systems, joint analysis of accident data, and implementation of solutions based on artificial intelligence to help predict and minimize the risk of traffic incidents.

Additional solutions can be proposed to expand the traffic accident prediction system, as detailed in

Table 15. Suggestions for improving the traffic management system in Katowice.

Area for Improvement	Suggestions for Improvement
Expansion of sensor and camera networks	Installing additional cameras at key points in the city and weather sensors to monitor icing.
Integration with public transport	Link to data from public transportation vehicles and prioritize buses at traffic lights.
Predictive analysis of weather conditions	Include weather forecasts in system analyses to predict accident risk.

Source: own study.

.

The last classified smart city in Poland is Lodz. The city is described as one of the rapidly developing cities in Poland, implementing modern smart city solutions, including smart energy management through smart grid systems [106]. These advanced systems not only support efficient energy management but also play a key role in integrating traffic management, using AI and the IoT.

Smart grid systems in Lodz enable dynamic management of electricity distribution in real time. With smart meters and sensor networks deployed throughout the city, the system is able to analyze energy consumption, optimize energy delivery, and predict demand based on historical and current data [107,108,109,110,111,112,113]. Integrating these technologies with the city’s energy infrastructure provides robust support for traffic management applications.

Combining the smart grid with AI- and IoT-based traffic management systems opens up new opportunities for improving urban transportation operations. With advanced artificial intelligence algorithms and IoT sensor networks deployed on the city’s key arteries, real-time dynamic traffic management is possible.

Smart grid systems provide power to key devices such as smart traffic lights, traffic monitoring cameras, and IoT sensors [114]. Smart energy management makes it possible to power these devices sustainably, even during peak hours, minimizing the risk of failure. IoT systems collect real-time data on traffic volumes, delays, accidents, and weather conditions. AI analyzes these data and makes recommendations to the smart grid, which adjusts the power supply to devices according to current needs. Smart grid systems in Lodz support electric public transportation vehicles, such as buses and streetcars, by providing them with priority power and the ability to charge at dynamically managed charging points. AI helps optimize schedules so that public transportation can run smoothly even when traffic is heavy.

An analysis of the detailed differences before and after the implementation of the smart energy management system and smart grid in Lodz is presented in

Table 16. Comparison of the efficiency of traffic management in Lodz before and after the implementation of the smart energy management system and smart grid.

Indicator	Before Implementation of Smart Grid	After Implementation of Smart Grid
Traffic volume	Frequent congestion during rush hour and a lack of dynamic traffic signal management.	Reducing traffic jams through dynamic traffic control and traffic signal optimization.
Public transport management	No priority for buses and streetcars, which caused delays.	Prioritizing public vehicles through smart energy management systems.
Energy efficiency	Inefficient energy consumption by traffic signals and roadside equipment.	Sustainable energy consumption through the smart control of urban systems.

Source: own study based on [115,116].

.

The implementation of the smart grid system in Lodz has brought significant improvements in traffic management, as reflected in the improvement of many key performance indicators [116,117]. Prior to the deployment, the city was facing significant challenges, such as frequent traffic jams during rush hour, delays in service responses to incidents, and a lack of prioritization of public transportation. The implementation of the smart grid, combined with IoT technologies and artificial intelligence, has allowed the city to build a dynamic traffic management system that responds in real time to changing road conditions.

Key changes include the dynamic control of traffic signals, which significantly reduces traffic jams and improves traffic flow. Intelligent monitoring systems have enabled faster detection and management of traffic incidents, reducing response times for emergency services. In addition, the prioritization of public transportation vehicles, such as buses and streetcars, has helped reduce their delays and improve punctuality.

In Lodz, the smart grid system has also played a key role in improving the energy efficiency of the city’s infrastructure. Sustainable energy management has reduced energy consumption by traffic lights and other road equipment. As a result, the city has not only reduced operating costs but has also contributed to lower emissions through smoother traffic.

Additional solutions can be proposed to expand the smart grid system, as detailed in

Table 17. Suggestions for improving the traffic management system in Lodz.

Area for improvement	Suggestions for Improvement
Dynamic toll system	Introduce a system of dynamic road user fees based on data collected by the smart grid. Higher fees could apply during peak hours to reduce traffic congestion.
Smart city parking lots	Development of smart parking systems, which combined with the smart grid would enable dynamic management of parking availability by directing drivers to the nearest vacant spaces and reducing traffic jams generated by the search for parking spaces.
Integration with autonomous vehicles	Preparing the infrastructure to communicate with autonomous vehicles, enabling smoother traffic coordination and more precise management of energy and road infrastructure.
Weather threat prediction system	Introducing systems that analyze weather data in real time and adjust traffic and energy management, such as by warning drivers of icy or heavy rainfall.

Source: own study.

.

The proposed solutions, such as dynamic energy management, integration with autonomous vehicles, smart city parking, and weather hazard prediction systems, will allow the system to be even better adapted to changing conditions and residents’ needs. Special attention has been paid to solutions that enhance pedestrian and bicycle safety, improve traffic flow, and reduce emissions.

In addition, the expansion of smart city parking lots in the vicinity of Manufaktura or Atlas Arena could reduce traffic jams generated by vehicles seeking parking spaces, especially during mass events. The implementation of smart pedestrian crossings, for example, around schools and universities such as the Technical University of Lodz, would increase pedestrian safety in high-traffic areas. By integrating the smart grid with air quality sensors, Lodz could better monitor and reduce pollutant emissions in high-traffic neighborhoods such as Downtown.

These solutions, combined with the development of predictive systems for weather hazards, would allow the city to be better prepared for emergencies such as icy conditions in winter, which is particularly important for traffic management on arterial roads such as the Upper Route. Implementing these improvements in Lodz could make the city a leader in smart energy and traffic management, helping to improve the quality of life for residents and increase the efficiency of the city’s infrastructure.

While the benefits of implementing AI and IoT technologies in traffic management are evident, it is also important to consider the potential risks and challenges associated with these innovations. Among the most critical concerns are those related to data privacy and cybersecurity. Intelligent transport systems often rely on the continuous collection of real-time geolocation data, vehicle behavior, and user preferences. Without appropriate safeguards, such data may be vulnerable to misuse, unauthorized access, or breaches, raising concerns among citizens and privacy watchdogs. Ensuring compliance with EU General Data Protection Regulation (GDPR) and implementing strong encryption standards must, therefore, be a priority.

Another major challenge is the financial burden of deploying and maintaining such advanced systems. The implementation of AI-based traffic controls, smart sensors, or IoT-connected street lights each requires significant upfront investment, which may be a barrier for smaller municipalities. Furthermore, the dependence on foreign technologies and proprietary platforms—often provided by global tech corporations—may limit local autonomy and pose geopolitical or operational risks.

Human capital constraints also play a crucial role. Municipal traffic agencies must be equipped not only with modern infrastructure but also with well-trained personnel capable of managing and updating these systems. The lack of such expertise may hinder both the effective deployment and long-term adaptation of smart technologies.

Moreover, interoperability challenges frequently emerge when integrating different systems across urban and suburban areas. Vendors use diverse protocols and architectures, making cross-system communication and scalability problematic. This issue becomes particularly relevant when cities attempt to scale-up solutions beyond core urban zones.

In parallel, public perception and social acceptance should not be underestimated. The increasing presence of surveillance-based technologies—such as smart cameras, facial recognition, or AI-driven predictive policing—may provoke resistance if introduced without adequate transparency, inclusivity, and public consultation. Concerns over algorithmic bias, lack of explainability, and unequal access to benefits may exacerbate digital divides or erode trust in local governments.

Acknowledging these risks provides a more comprehensive understanding of smart city development in Poland. It highlights the need for integrated legal, technical, and ethical frameworks that support responsible innovation while safeguarding citizen rights and ensuring system resilience. A balanced approach that weighs both opportunities and limitations will ultimately foster more sustainable, equitable, and trustworthy smart urban environments.

4. Discussion

The presented results confirm and extend previous findings in the literature. For example, the implementation of an AI-based accident prediction systems in Katowice mirrors similar successes in global studies, such as those by Ouallane et al. [31] and Lilhore et al. [46], where intelligent monitoring led to significant reductions in accident rates. Furthermore, the smart lighting system in Wroclaw aligns with international efforts reported by Elassy et al. [28], emphasizing both energy efficiency and safety improvements in urban settings. These results highlight not only the effectiveness but also the scalability of such technologies in different urban contexts, confirming the potential of AI and the IoT in transforming city traffic systems.

Building on these findings, the integration of artificial intelligence (AI), the Internet of Things (IoT), and smart grid systems in traffic management emerges as a groundbreaking approach to addressing long-standing challenges related to urban mobility, sustainability, and safety. Results from studies conducted in cities such as Warsaw, Cracow, Wroclaw, Gdansk, Poznan, Katowice, and Lodz further reinforce this potential, demonstrating how these technologies can significantly improve traffic operations, reduce pollution, and enhance the safety of traffic participants through intelligent control systems.

Interpretation of the Results of the Conducted Research

The data presented in this study support the hypothesis that the implementation of AI, the IoT, and smart grid systems significantly improves the efficiency of urban traffic management. In Katowice, for example, the introduction of an AI-based accident prediction system reduced the number of collisions by 20% and improved the response time of emergency services by 10%. This result aligns closely with the overarching goal of making cities safer and more efficient.

Similarly, the smart grid system implemented in Lodz has demonstrated the ability to optimize energy distribution while supporting traffic infrastructure. By dynamically managing the energy consumption of smart traffic signals, public transport vehicles, and IoT sensors, the system helped reduce congestion in key areas of the city and minimize energy waste. These improvements support the hypothesis that these technologies can lead to more sustainable urban development.

The results are in line with global studies highlighting the transformative potential of AI and the IoT in urban environments. For example, similar systems implemented in Barcelona and Amsterdam have yielded comparable benefits in terms of traffic optimization and energy savings. However, the unique features of Polish cities, such as the integration of weather forecasting in Katowice and priority energy allocation in Lodz, show how local adaptations of these technologies can yield specific benefits.

On the other hand, some results diverge from global trends. For example, the integration of smart pedestrian crossings in Poznan reduced the number of pedestrian accidents by 30%, which is lower than in Scandinavian cities where the reductions reached more than 40%. This discrepancy underscores the impact of contextual factors such as public awareness and urban design.

The results of this study have both theoretical and practical implications. On a theoretical level, they contribute to the body of knowledge on the role of AI and the IoT in traffic management, providing a framework for future research in this area. Practically, the demonstrated benefits, such as reduced emissions, improved safety, and increased energy efficiency, underscore the viability of these technologies as tools for sustainable urban development.

It is worth pointing out that the smart parking system in Gdansk, which has reduced the average time to find parking spaces from 15 to 5 min, not only alleviates congestion but also reduces CO₂ emissions by 30% in the areas covered by the system. Such results demonstrate the technology’s direct impact on the environment and residents’ quality of life, making it an attractive choice for city governments.

5. Conclusions

This article examines in detail the application of artificial intelligence (AI) and the Internet of Things (IoT) in traffic management in the context of smart cities in Poland. In an era of dynamic urbanization and increasing numbers of vehicles on the road, the main challenges are congestion, rising emissions, and improving road safety. This article hypothesizes that the integration of artificial intelligence (AI), the Internet of Things (IoT), and smart grid technologies in traffic management contributes to significant improvements in transportation efficiency, reduced emissions, and increased road safety. Accordingly, the thesis is that these technologies are key elements in the construction of modern traffic management systems, enabling the dynamic adaptation of urban infrastructure to changing traffic conditions and residents’ needs.

Despite the comprehensive nature of this study, several limitations must be acknowledged. Firstly, the selection of cities was based on available smart infrastructure projects, which may introduce selection bias and limit generalizability to smaller or less digitized municipalities. Secondly, the analysis focused primarily on quantitative metrics derived from secondary sources and official reports, without incorporating qualitative data from residents, stakeholders, or field-based observational studies. Thirdly, the study does not provide longitudinal tracking of system performance beyond the year 2024, which may affect the robustness of conclusions over time. Fourthly, while technical effectiveness was emphasized, broader sociopolitical factors such as public trust, governance capacity, and legal constraints were not addressed in depth. Lastly, due to the fast-evolving nature of AI and the IoT, some solutions analyzed may become outdated or require re-assessment within a short period. These limitations suggest areas for future investigation, particularly in mixed-methods evaluations and real-time longitudinal studies.

The following seven major Polish cities implementing innovations in line with the smart city concept were selected for analysis: Warsaw, Cracow, Wroclaw, Gdansk, Poznan, Katowice, and Lodz. Each of these cities have implemented innovative projects based on AI and the IoT that have contributed to the improvement of urban transportation operations.

In Warsaw, the TRISTAR system has significantly reduced traffic jams, reduced waiting times at intersections, and optimized traffic lights. Cracow implemented an intelligent transport system (ITS) which reduced emissions, streamlined traffic flow, and improved safety. In Wroclaw, the lighting infrastructure was modernized with smart LED luminaires, which increased energy efficiency and improved safety in urban zones. Gdansk, implementing a smart parking system, reduced the time spent searching for parking spaces, which helped reduce emissions and congestion in the city center. Poznan relied on smart pedestrian crossings, which improved pedestrian safety with dynamic lighting and reduced waiting times for green lights. Katowice used a state-of-the-art traffic accident prediction system that relies on cameras and artificial intelligence to reduce collisions and speed up the response of the emergency services. Lodz, by integrating smart grid technologies with traffic management systems, has improved energy and public transport management, reducing congestion and promoting environmentally friendly modes of transportation.

AI and IoT technologies have contributed to significant benefits in each city, including reduced emissions, reduced traffic congestion, energy savings, and improved safety for road users. The solutions have also made it possible to better adapt urban infrastructure to the needs of residents, promoting sustainability and a green approach to transportation. This article emphasizes the need for further measures, such as integrating systems with autonomous vehicles, implementing dynamic road pricing, expanding the network of smart sensors, and developing education for residents on the benefits of modern technology.

In conclusion, the implementation of AI and IoT in traffic management is crucial to the sustainable development of cities and the efficiency of their infrastructure. Implemented in Poland’s largest cities, the technologies have proven their effectiveness in improving traffic flow, reducing the negative impact of transportation on the environment, and increasing the comfort of residents. Conclusions from the analysis highlight the potential for further development of such systems, which can further contribute to the dynamic adaptation of urban infrastructure to changing traffic conditions and residents’ needs, making cities more modern, environmentally friendly, and safe.

It is worth pointing out that future research in this area should focus on long-term analyses to assess the lasting impact of these systems. In addition, examining the role of citizen participation in the success of such technologies could provide valuable insights. The integration of real-time data from autonomous vehicles and the development of predictive models for extreme weather conditions are promising directions for further research. In the future, it will be necessary to further develop the interoperability of systems and invest in public education in order to realize the full potential of these technologies.

(a)

Greater citizen involvement: The development of mobile applications that allow residents to report road incidents and proposals for infrastructure improvements.

(b)

Integration with autonomous vehicles: Preparing city infrastructure to support autonomous transportation systems.

(c)

Advanced pedestrian safety measures: Expanding the use of AI to detect pedestrian movement and improve safety at crosswalks.

(d)

Dynamic toll systems: Implementation of AI-based systems for peak toll management.

To continue the success of these deployments, several future improvements should be considered. From a policy and practice perspective, the findings of this study suggest a number of strategic implications for enhancing traffic management efficiency in Polish smart cities. Firstly, national and municipal authorities should prioritize the standardization of data exchange protocols to improve interoperability between different AI and IoT systems. Secondly, public funding programs (e.g., EU funds or regional development grants) should more explicitly support the integration of smart mobility solutions, especially in mid-sized cities. Thirdly, it is crucial to develop capacity-building initiatives aimed at training municipal employees and transport planners in data analytics, AI system operations, and cybersecurity management. Fourthly, cities should establish public engagement platforms to involve citizens in the co-creation of smart transport policies, increasing the transparency and social acceptance of data-driven governance. Lastly, inter-city collaboration networks can facilitate the sharing of best practices, allowing Polish municipalities to scale successful traffic management models across various urban contexts.

Additionally, city governments should consider creating dedicated units responsible for coordinating the implementation and maintenance of intelligent transport systems (ITSs), including AI-based signaling and real-time IoT monitoring. Urban policy should also support the deployment of multi-modal platforms that unify data from public transport, car-sharing services, bike lanes, and pedestrian traffic into a single, integrated system. These implications can serve as guidelines for other emerging smart cities across Central and Eastern Europe seeking to build robust, resilient, and sustainable transport systems.

It is important to note that unexpected changes in the effectiveness of these technologies can result from factors such as varying levels of public awareness, the quality of existing infrastructure, or local climatic conditions. For instance, the higher-than-expected reduction in traffic congestion in Lodz may be attributed to the city’s relatively well-planned urban layout, which facilitated the integration of smart grid technologies.

Taken together, these insights highlight the need for a holistic, forward-thinking approach that combines technological innovation, citizen engagement, and institutional capacity to fully realize the transformative potential of AI and the IoT in urban traffic management.