Sources and Use of Bicycle Traffic Data in Research and Urban Mobility Management

Abstract

Understanding cycling mobility is essential for developing sustainable urban transport policies. However, variations in data collection methods and a lack of standardized approaches hinder comprehensive analysis and evidence-based decision-making. This study examines how cycling data are gathered across different cities, the insights they provide, and how existing methods can be improved. The scientific literature and policy guidelines are examined, combined with an empirical study analyzing data collection practices in several Polish cities. This paper reveals inconsistencies in data collection techniques, including differences in sensor-based tracking, manual counting, and survey methodologies. Moreover, while some cities employ advanced technologies such as automated counters, others rely on fragmented or irregular data sources. This study highlights best practices observed in international research and policy recommendations, which could guide the refinement of cycling data collection strategies. By identifying common gaps and challenges, recommendations for improving data standardization, integration, and accessibility are proposed. The results contribute to a better understanding of how cities can enhance their cycling data frameworks to support sustainable mobility planning and policy development.

1. Introduction

Sustainable urban mobility is a crucial component of modern city planning, aiming to balance environmental, economic, and social aspects of transport. The role of cycling as a key element in sustainable mobility strategies has gained increasing recognition, as it contributes to reducing traffic congestion, lowering emissions of toxic substances, and improving public health. However, effective policy-making and infrastructure development for cycling requires robust and standardized data collection methods that provide insights into current trends and future needs. Without reliable data, cities may struggle to implement efficient mobility strategies and measure the impact of their interventions. Thus, understanding how cycling data are gathered, analyzed, and utilized is essential for the advancement of urban mobility planning.

Published research has highlighted the significance of mobility data in shaping urban policies [1]. While various methods exist for collecting data on cycling, including manual counts, sensor-based tracking, and surveys, there is considerable variation in how cities approach this process. Moreover, inconsistencies in data collection standards can limit comparability and the effectiveness of cross-city analyses. Several studies have also noted gaps in data accuracy and accessibility, which can hinder evidence-based decision-making [2]. Despite these challenges, best practices have emerged in both the scientific literature and policy recommendations, particularly in Europe, where many cities have advanced cycling infrastructure and data collection frameworks [3,4].

This study aims to identify and analyze current practices in cycling data collection, with a focus on their applicability to urban mobility development. It specifically examines how cycling data are gathered in Polish cities, what insights can be extracted, and how existing methods can be improved to support sustainable mobility goals. This research includes a review of the global scientific literature, an assessment of non-scientific recommendations, and an empirical investigation into data collection practices in Polish cities. By synthesizing these perspectives, the commonalities, gaps, and opportunities for enhancing data collection methodologies are identified.

The primary contribution of this study is an in-depth evaluation of data collection strategies in several Polish cities, shedding light on the methodologies, objectives, and limitations. By comparing these findings with international research and best practices, the study proposes recommendations for improving data collection standards to better inform urban mobility planning. The results of this paper will be valuable for policymakers, urban planners, and researchers seeking to enhance the role of cycling in sustainable city development. This research contributes to a more data-driven approach to urban mobility, ultimately fostering more effective and equitable cycling policies.

This study contributes to the growing body of knowledge on sustainable urban mobility by providing a detailed overview of cycling data collection practices in Polish cities—a context that remains underrepresented in international research. Unlike studies focused primarily on leading cycling nations, this paper highlights the challenges and adaptations in cities with emerging cycling policies. By combining a literature review, policy recommendations, and empirical findings from Poland, the study offers practical insights for improving data-driven cycling planning in similar urban contexts.

The structure of this article is as follows: a review of publications related to cycling issues can be found in Section 2; Section 3 presents the results of research conducted in Polish cities, checking what data are collected, how, and for what purpose; conclusions and recommendations are gathered in Section 4; and the work is summed up in Section 5.

2. Review of the Scientific Literature and Other Sources

The field of bicycle transportation research has seen a growing interest in recent years, driven by the increasing recognition of the environmental, health, and societal benefits of cycling as a mode of transportation. One crucial aspect of this research is the development of effective survey methods and standards to collect data on cycling behaviour and preferences.

Available publications on the subject of bicycle traffic research differ, among other things, in terms of who collects, for what purpose, and in what manner. Documents can be divided into two main groups: scientific and other. The following sections present publications according to this division.

2.1. Scientific Literature

The scientific articles that were expected to describe the collection and use of data related to cycling were about the data themselves, infrastructure, network, bicycle tracking, infrastructure and cycling planning, and shared systems. The Web of Science database [5] was used to analyze scientific sources. The search was limited to publications in the Open Access system, published in the last 5 years (2020–2024), falling into the Web of Science categories of (i) transportation and (ii) civil engineering. The search terms in the “topic” domain were as follows: bicycle + data, bicycle + infrastructure, bicycle + network, bicycle + Strava, bicycle + GPS tracking, bicycle + urban mobility, bicycle + transport planning, bicycle + traffic, and bike-sharing data.

Based on the abstracts of the collected publications, it can be concluded that the data are collected mainly by (i) researchers, (ii) mobile applications, (iii) cities (infrastructure managers), and (iv) the police.

These entities collect data on the following: (1) travel routes, speed, route choices, and start and end points of the journey (GPS data); (2) analysis of cyclists’ interactions with road traffic and identification of conflicts (video recordings); (3) information on routes and user behaviour (data from mobile applications and crowdsourcing); (4) time of use and bike rotation (data from bike-sharing systems); (5) air quality, noise, road surface condition, and traffic intensity (environmental and infrastructure data); (6) cyclists’ preferences and subjective perception of safety (survey data).

The collected data are mainly used for (a) creating cycling infrastructure/networks, (b) improving the safety of cyclists, (c) cycling travel patterns, and (d) managing bike-sharing systems.

Data are processed mainly using (i) statistical analysis (regression models and cluster analysis), (ii) machine learning (neural networks for predicting bicycle traffic), (iii) predictive models (analysis of cyclists’ trajectories), (iv) GIS analysis (mapping bicycle traffic intensity and analysis of infrastructure availability), and (v) data fusion (combining data from multiple sources).

Due to the large number of responses, the collected publications were divided into groups based on abstracts and keywords. The distinguished groups, ranked by their number, are as follows: (1) cyclists’ interactions with road traffic; (2) bike-sharing systems; (3) data collection and analysis; (4) bicycle monitoring technologies; (5) cyclists’ safety and accident analysis; (6) bicycle infrastructure planning and modelling; (7) the impact of transport policy on cyclists; (8) spatial cycling research; (9) other. Articles thematically related to the subject of this publication are included in groups (3) data collection and analysis and (6) bicycle infrastructure planning and modelling. Publications included in these groups were subjected to detailed analysis.

2.2. Other Sources

The collection and analysis of cycling traffic data play a crucial role in transportation planning, infrastructure development, and policy-making. Various entities, including government agencies, research institutions, non-governmental organizations, and private companies, collect data to improve cycling conditions, enhance safety, and encourage sustainable mobility. The methods and purposes of data collection vary across different types of organizations, leading to a diverse range of datasets and analytical approaches.

Governmental institutions primarily collect cycling data to support urban planning and transport policy. National and local authorities use automated counting stations, roadside surveys, and bicycle traffic monitoring systems to track cycling volumes, travel patterns, and safety indicators. Policy documents and strategic guidelines outline the need for standardized methodologies in data collection, emphasizing the integration of cycling data into broader mobility and environmental strategies. Furthermore, law enforcement agencies contribute by recording bicycle-related accidents and traffic conflicts, providing crucial information for safety assessments and infrastructure improvements.

Non-governmental organizations and advocacy groups also play a significant role in gathering and analyzing cycling data. These organizations often conduct independent surveys to assess cyclists’ needs, perceptions of safety, and the quality of infrastructure. Crowdsourced data collection methods, including online platforms and voluntary participation programmes, enable communities to contribute valuable insights into everyday cycling experiences. Reports published by NGOs frequently highlight gaps in cycling infrastructure, advocate for policy changes, and provide recommendations for future development.

In recent years, mobile applications and digital platforms have emerged as key sources of cycling data. The application tracking tools collect vast amounts of anonymized trip data, offering insights into route preferences, travel speeds, and peak cycling hours. Shared mobility services, such as bike-sharing systems, provide additional datasets on bicycle usage patterns, turnover rates, and system demand. These digital tools enable continuous, large-scale data collection, complementing traditional survey-based approaches and allowing for more dynamic analyses of cycling behaviour.

The collected data undergo various analytical processes to extract meaningful insights. Statistical analyses, including regression models and cluster analyses, are commonly applied to identify patterns in cycling behaviour. Machine learning techniques, such as neural networks, are increasingly used to predict bicycle traffic volumes and optimize infrastructure planning. Geographic Information System tools allow for the spatial analysis of cycling networks, mapping traffic intensity, and assessing the accessibility of cycling facilities. Additionally, data fusion methods integrate multiple sources of cycling information, enhancing the accuracy and reliability of findings.

Overall, the systematic collection and analysis of cycling data are essential for evidence-based decision-making in urban mobility. Standardized methodologies, interdisciplinary collaboration, and technological advancements contribute to a more comprehensive understanding of cycling trends, ultimately supporting the development of safer and more efficient cycling networks.

2.3. Selected Bicycle Traffic Studies

The literature of cycling traffic data has gained increasing attention in recent years, with researchers and practitioners focusing on different aspects of bicycle transportation. These include methods for collecting and analyzing cycling data, cyclists’ behaviour and interactions with road traffic, the planning and impact of cycling infrastructure, and the optimization of bike-sharing systems.

One of the commonly used approaches to analyzing cycling data is the use of GPS tracking, as highlighted in studies such as [6]. Also, in modelling bicycle traffic assignments, GPS data have been used to capture route preferences among different cyclist user groups, which allows for more accurate demand forecasting and infrastructure planning [7]. Similarly, ref. [8] explores data from Bluetooth devices to determine cyclists’ routes. In turn, refs. [9,10] emphasize the importance of data from mobile applications, such as Strava Metro, in assessing the impact of cycling infrastructure on cyclists’ route choices. Additionally, the analysis of video recordings has been employed to assess cyclists’ interactions with other road users, e.g., [11,12]. Additionally, the scientific literature describes multi-source data collection methods, such as combining mobile sensor-based field measurements with environmental monitoring stations to analyze cyclists’ exposure to noise and air pollution in urban settings [13]. These approaches highlight the increasing complexity of data used in cycling-related research.

Cyclists’ behaviour and their interactions with road traffic are an important area of research. Refs. [14,15] focus on analyzing conflict situations at intersections, using behavioural models. Refs. [6,16] examine cyclists’ preferences regarding route selection and their interactions with other road users. Moreover, studies examining the influence of infrastructure stress levels on modal choice demonstrate that design features such as traffic separation can significantly affect cycling uptake, particularly when accounting for socio-economic differences and travel purpose [17]. Video analysis has also been used to assess cycling safety in greater detail in [11,12].

The planning of bicycle infrastructure and its impact on bicycle traffic intensity are the subject of research described in [14,16], where models of assigning bicycle traffic depending on user preferences are analyzed. Refs. [9,18] highlight the key role of bicycle network development strategies, emphasizing the importance of spatial analyses. Additionally, refs. [11,19] focus on methods for assessing the quality of bicycle infrastructure maintenance. A complementary perspective is offered by research developing maturity models for cycling policy planning, which help cities evaluate the integration, quality, and governance of cycling networks [20]. In addition, the use of data-driven methods to assess cycling infrastructure quality based on measurable indicators such as safety and accessibility has been proposed to guide urban planning decisions [21].

Bike-sharing systems and their optimization constitute another area of research. Refs. [11,22] analyze the quality of services in bike-sharing systems, taking into account logistical and utility aspects. In turn, studies [9,23] indicate the possibility of using data from bike-sharing systems, as an indicator of bicycle mobility. The evolution of bike-sharing systems as a research area and bibliometric analyses have been described, revealing trends in demand modelling, fleet logistics, and the growing interest in environmental sustainability of bike-sharing systems [24]. In addition, refs. [14,25] investigate the potential of machine learning methods in forecasting long-term demand for bikes in shared systems.

The research on cycling traffic encompasses diverse methodologies and analytical approaches, providing valuable insights into infrastructure planning, cyclist behaviour, and system optimization. The integration of automated data collection, behavioural modelling, and strategic planning frameworks enhances the effectiveness of cycling policies. By leveraging both scientific research and practical applications, cities can create safer, more efficient, and environmentally sustainable cycling networks.

2.4. Literature Review Conclusions

Cycling has been studied by scientists, urban planners, city institutions and operators of bicycle systems in order to improve infrastructure, safety, and transport efficiency. Key goals include analyzing cyclists’ behaviour in road traffic, identifying barriers to bicycle use and assessing the impact of infrastructure on transport choices. Data are collected in various ways: using bicycle counters, GPS, video recordings, mobile applications, surveys, and econometric models. They are used to analyze traffic volume, exposure to pollution, and cyclists’ interactions with other road users and to forecast demand for bicycle infrastructure. Data analysis includes mathematical modelling, statistical methods, spatial analysis, and the use of artificial intelligence to process large datasets.

Studies indicate that key factors influencing bicycle use are the quality and safety of infrastructure, noise and air pollution, and the level of traffic stress. Better planning of bicycle paths and their separation from car traffic significantly increase the number of cyclists. The results of the analyses are used to optimize the layout of bicycle stations, plan investments in new routes, and improve existing infrastructure. A strategic approach to developing a cycling network can increase its efficiency compared to traditional methods. As a result, cycling analysis plays a key role in shaping city transport policy, reducing emissions, and improving public health.

3. Cycling Data Collected in Polish Cities

The collection and utilization of cycling data have become increasingly important for urban mobility planning, particularly as cities strive to promote sustainable transport and improve cycling infrastructure. Reliable data on bicycle traffic patterns enable municipalities to optimize infrastructure investments, enhance road safety, and integrate cycling with other transport modes. However, the methods and scope of data collection vary significantly across cities, leading to inconsistencies in data comparability and accessibility.

This section examines how Polish cities collect, store, and utilize bicycle traffic data to support transport policy and infrastructure planning. Based on a survey conducted among municipal institutions responsible for cycling infrastructure and mobility management, this study explores the key entities involved in data collection, the frequency and scope of measurements, and the challenges related to data standardization.

3.1. Data Collection Methodology

This study aimed to assess how Polish cities collect and utilize bicycle traffic data to support transport policy and infrastructure planning. To achieve this, a questionnaire survey was conducted among various municipal institutions responsible for bicycle traffic management. The primary respondents included municipal road authorities, urban mobility and transport planning departments, public transport authorities, and municipal services involved in environmental management, spatial planning, and sustainability. In some cases, cities have a dedicated bicycle officer, appointed by the mayor, who oversees cycling policies and infrastructure development. These officers either work independently or within other municipal units such as traffic engineering offices or urban development agencies.

To collect relevant data, in May 2024, the authors reached out via email to 42 municipal units from 37 cities in Poland (all Polish cities with over 100,000 inhabitants), requesting access to public information. The respondents were asked to complete a questionnaire divided into three sections: bicycle traffic in the city, cyclists, and cycling infrastructure within the urban area. For each category, the survey inquired about (i) the type (subject) of data collected, (ii) the format of the collected data, (iii) the frequency of data collection or measurements, (iv) the entity responsible for data collection, (v) the data retention period, and (vi) the intended use of the data. By October 2024, responses were received via email from 39 municipal units across 32 cities. In some cases, multiple municipal units within a single city shared responsibility for bicycle traffic management, leading to multiple responses from the same location. The findings revealed a lack of standardization in data collection, with different institutions gathering and maintaining various datasets depending on their specific responsibilities. However, the scope of responses provided was not always comprehensive. In some cases, municipalities selectively addressed specific questions while omitting others, leading to partial or incomplete data submissions. This variation in the level of detail further complicates the assessment of bicycle traffic data collection practices across cities.

3.2. Entities Involved in Bicycle Traffic Data Collection

Bicycle traffic data in Polish cities are collected and maintained by various public and private entities, each focusing on different aspects of cycling infrastructure and mobility. Municipal road authorities, responsible for the construction and maintenance of cycling infrastructure, commonly collect data on bicycle path networks and road safety statistics. They also frequently gather bicycle traffic volume data, often in collaboration with other municipal departments that have access to automated counting systems. Public transport authorities collect information on the integration of cycling with public transport, such as bike-and-ride facilities, bicycle parking at transit stations, and the impact of cycling on public transport demand.

Urban mobility and transport planning departments focus on analyzing modal share, bicycle flow patterns, and travel behaviour through surveys to optimize transport policies. Meanwhile, departments responsible for transport (in general) monitor cycling’s contribution to reducing CO₂ emissions and improving air quality. Traffic management centres use automated bicycle counters, smart traffic signals, and sensor-based monitoring to assess bicycle traffic volumes in real time. Additionally, private bike-sharing operators collect extensive data on bicycle rental patterns, trip durations, and popular cycling routes, which can supplement official municipal data.

The variation in data collection responsibilities across different municipal units highlights the complexity of integrating bicycle traffic data. While infrastructure-related data are primarily managed by municipal road authorities, traffic volume data are often gathered by multiple institutions with access to different data sources, including automated counting systems. This fragmentation presents challenges in creating a comprehensive and standardized approach to bicycle traffic monitoring and policy-making.

The bar chart in Figure 1 illustrates the distribution of institutions responsible for bicycle traffic data collection in a subset of the surveyed cities in Poland. It highlights the number of cities where specific entities are involved in data collection. The data show that road agencies and city halls (in general) are the most common institutions gathering bicycle traffic information, with seven and six cities, respectively, reporting their involvement. Public Transport Management Boards are responsible in three cities, while external companies handle data collection in two cases.

This distribution further emphasizes the fragmentation of bicycle traffic data management. As different institutions collect data for their respective needs, there is no unified system ensuring comprehensive and standardized data consolidation. The reliance on various entities, each employing different methodologies and focusing on distinct aspects of cycling mobility, contributes to inconsistencies and gaps in data accessibility. This further supports the need for a centralized unit dedicated to bicycle traffic data management, enabling better coordination and integration across municipal departments.

3.3. Scope and Frequency of Bicycle Traffic Data Collection

The survey results indicate that fewer than half (45%) of the examined cities conduct any bicycle traffic volume measurements. The bar chart in Figure 2 presents data related to bicycle traffic monitoring methods. It consists of five categories, each representing a different method of collecting bicycle traffic data. The categories along the x-axis describe different monitoring approaches:

• Any traffic volume counting at selected cross-sections;
• Continuous counting using dedicated counters;
• Data collecting from the city bike system;
• Regular measurement (e.g., once every 5 years) of traffic volumes at selected cross-sections;
• Traffic volume counting ad hoc at selected cross-sections.

Only 25% (8 out of 32) of Polish cities, namely, Bielsko-Biała, Gdańsk, Gdynia, Sopot, Warsaw, Poznań, Kraków, and Wrocław, conduct cyclical urban traffic surveys. Additionally, several cities—Elbląg, Gdańsk, Gdynia, Kraków, Poznań, Sopot, Toruń, and Warsaw—carry out continuous measurements via dedicated bicycle counters equipped with motion sensors. Some cities, such as Gdańsk, Kraków, Poznań, Warsaw, and Wrocław, complement continuous monitoring with more detailed one-time surveys conducted every five years. These efforts often form part of comprehensive traffic studies, though such studies are not always conducted regularly. Among all cities, Warsaw, Gdańsk, Kraków, Poznań, and Wrocław demonstrate the most consistent approach to comprehensive traffic research, ensuring a structured understanding of urban mobility patterns.

The irregularity of data collection across cities poses significant challenges for long-term traffic planning and policy development. While some municipalities prioritize continuous and cyclical monitoring, others rely on sporadic surveys or external data sources, leading to inconsistencies in methodology and data comparability. This disparity affects the ability to track trends over time, assess the impact of infrastructure changes, and develop effective transport strategies.

Furthermore, the purpose of continuously collected data (via fixed sensors) and cyclical studies (typically part of comprehensive traffic research) differs significantly. Continuous monitoring is only conducted in locations where permanent counters are installed, meaning that data are primarily collected from dedicated cycling infrastructure. In contrast, periodic traffic studies cover a much wider range of locations, including areas without dedicated bike lanes. This broader scope allows planners to identify demand for cycling infrastructure in places where it is currently lacking, supporting informed decision-making on future investments and network expansion.

Notably, two cities—Warsaw and Bielsko-Biała—utilize cameras to measure bicycle traffic. However, further investigation suggests that such measurements may not be limited to just these two cities. The absence of explicit responses regarding camera-based measurements highlights a broader issue: due to the fragmented responsibilities for data collection and management, even municipal employees may not always be aware of what data are being collected, where, and how. This lack of awareness can limit access to valuable data and hinder the effective coordination of cycling infrastructure planning.

3.4. Data Storage and Accessibility

The storage methods for bicycle traffic data vary considerably across Polish cities, significantly impacting data accessibility, integration, and analytical potential. As shown in Figure 3, spreadsheet files are the most commonly used format, reported by 10 cities, accounting for approximately 77% of the surveyed cases (cities which answered that question). This format allows for relatively straightforward data processing and organization but lacks advanced capabilities for real-time integration and automated analysis. Despite being a structured format, spreadsheet files may still pose limitations in terms of interoperability and long-term data management.

In contrast, a significantly smaller number of cities—only five—store their bicycle traffic data online, making it available through digital platforms. This represents approximately 38% of the surveyed cases. While online storage provides easier access and facilitates real-time data retrieval, its limited adoption suggests that many municipalities have yet to implement centralized or publicly accessible databases for bicycle traffic monitoring. Cities that have adopted online data-sharing platforms typically integrate them with automated bicycle counters, enabling continuous monitoring and real-time updates.

Equally concerning is the fact that five cities, also representing 38% of the surveyed cases, store their data in text files. This unstructured format makes systematic data retrieval, analysis, and comparison particularly challenging. The reliance on such an outdated storage method increases the risk of inconsistencies, errors, and data loss, further complicating efforts to track long-term trends in bicycle mobility. The use of text files suggests that in many municipalities, bicycle traffic data are collected primarily for internal documentation rather than as part of a broader data-driven urban mobility strategy.

The findings indicate that there is no uniform approach to data storage across Polish cities. While some municipalities have begun implementing modern, accessible systems, the continued reliance on basic spreadsheet files and unstructured text documents highlights significant gaps in data standardization. Establishing guidelines for standardized data storage formats and promoting the adoption of online databases could significantly enhance data accessibility, comparability, and overall effectiveness in urban mobility planning.

3.5. Case Study Conclusions

This study revealed significant challenges related to the collection, storage, and accessibility of bicycle traffic data in Polish cities, primarily due to the lack of standardized methodologies. Various municipal units, including road authorities, transport departments, and private bike-sharing operators, collect and manage cycling data independently, resulting in fragmented responsibilities and inconsistencies in data integration. This institutional fragmentation not only complicates the coordination of cycling policies but also creates disparities in the frequency and scope of data collection. While some cities conduct continuous monitoring through automated bicycle counters and periodic traffic surveys, others rely on irregular studies or external data sources, leading to inconsistencies in data comparability and long-term trend analysis.

Moreover, the research highlighted substantial limitations in data accessibility and transparency. Many municipalities store bicycle traffic data in unstructured formats, such as text documents or simple spreadsheet files, which hinders efficient data processing, integration, and analysis. This lack of standardization limits the ability to conduct comprehensive studies and make data-driven decisions in transport planning. Only a few cities have implemented advanced digital storage solutions, providing online access to structured datasets that support informed policy-making and public transparency. The disparity in data management practices underscores the urgent need for the adoption of uniform guidelines and interoperable digital platforms to facilitate cross-city comparisons and long-term analyses.

An additional challenge identified in this study is the limited awareness among municipal officials regarding the scope and availability of collected data. The fragmented nature of data collection responsibilities means that even within a single city, different institutions may gather relevant information independently, without a clear understanding of how it can be effectively shared and utilized. This lack of coordination restricts the potential use of existing data in strategic transport planning and infrastructure development.

Furthermore, the findings indicate substantial differences in the approach to bicycle traffic monitoring. While some cities employ continuous monitoring technologies, such as automated counters and cameras, others primarily rely on infrequent surveys, which provide only a partial picture of urban cycling trends. The variation in monitoring strategies directly impacts the ability to assess cycling demand, identify gaps in infrastructure, and evaluate the effectiveness of mobility policies. Cities with more structured and continuous data collection processes demonstrate a higher capacity for evidence-based decision-making, whereas those with irregular or inconsistent data gathering face significant challenges in planning future investments.

Overall, this study emphasizes the need for a unified and centralized approach to bicycle traffic data collection in Poland. Establishing standardized methodologies, improving digital data management, and enhancing interdepartmental coordination could significantly improve the quality and usability of cycling data. The integration of consistent and accessible datasets would not only support more effective urban mobility policies but also contribute to scientific research on cycling behaviours and the impact of infrastructure investments. Addressing these issues is essential for creating a data-driven framework that enables Polish cities to develop sustainable and efficient cycling networks aligned with broader transportation and environmental goals.

4. Recommendations for Standardizing Data Collection and Possible Uses

The findings of this study highlight the necessity of standardizing bicycle traffic data collection methods to improve the comparability, reliability, and practical applicability of the gathered information. Addressing current challenges requires expanding the scope of collected data, introducing uniform data collection and storage methodologies, and integrating bicycle traffic data with broader transport and urban mobility datasets.

A more comprehensive approach to data collection should include additional parameters that can provide deeper insights into cycling patterns and infrastructure usage. Detailed information on cyclists’ routes, obtained from GPS data in mobile applications and bicycle sensors, would enable more precise modelling of traffic flows and infrastructure demand. Speed data could support analyses of traffic fluidity and safety assessments, whereas information on stops and delays would help identify congestion points and infrastructure inefficiencies. Furthermore, data on near-miss incidents and collisions, collected through video analysis and automated detection systems, could contribute to improving road safety. The quality of cycling infrastructure, including pavement conditions, lane widths, and the presence of obstacles, is another crucial aspect that should be systematically recorded. Additionally, integrating meteorological data would allow for the assessment of weather-related variations in bicycle traffic, whereas anonymized sociodemographic data on cyclists could help tailor infrastructure development to diverse user needs.

Expanding the range of collected data would enhance its potential applications in transport management, traffic analysis, and urban planning. More precise data on cycling flows could guide the planning of new infrastructure by identifying areas with the highest demand for cycling paths. Bicycle traffic data could also be utilized to optimize traffic light timing at intersections, improving cycling conditions and overall traffic efficiency. Additionally, integrating these data with public bike-sharing systems would allow for better station placement and fleet distribution based on real movement patterns. Evaluating the effectiveness of existing infrastructure investments would become more feasible by assessing whether new cycling facilities have led to increased bicycle usage. A standardized dataset would also facilitate integrated transport planning by improving the coordination between cycling infrastructure and public transport systems. Moreover, understanding the impact of cycling on environmental sustainability—such as reductions in CO₂ emissions and air quality improvements—could support the development of policies promoting active transportation.

The effective utilization of bicycle traffic data requires the adoption of advanced data processing techniques, including machine learning, image recognition, and Geographic Information Systems (GISs). Machine learning algorithms could improve the forecasting of bicycle traffic trends based on historical data and external factors such as weather conditions. Automated image and video analysis could be employed to detect cyclists, track their trajectories, and assess interactions with motor vehicle traffic. GIS-based spatial analysis enables the identification of key cycling corridors and infrastructure gaps, supporting data-driven decision-making in urban planning. Additionally, integrating data from various sources—such as mobile applications, bicycle counters, and public transport databases—would provide a holistic view of urban mobility patterns.

To ensure data comparability and interoperability across cities, it is essential to establish standardized indicators and data collection methodologies. Key indicators should include bicycle traffic volume, average travel speed, temporal patterns (hourly, daily, and seasonal variations), incident and conflict data, infrastructure usage (cycling lanes vs. mixed-traffic roads), and integration with public transport (e.g., bike-and-ride facility usage). Data collection methods should incorporate automated bicycle counters (inductive loops, AI-driven cameras, and motion sensors), GPS-based mobility data from applications, cyclist surveys, and intersection detection systems utilizing video analysis. Additionally, integrating data from bike-sharing systems would provide valuable insights into urban cycling trends.

Harmonization efforts should also focus on data storage and accessibility. Establishing a central bicycle traffic database would facilitate data sharing between cities, supporting nationwide transport research and policy development. Standardized data formats—such as structured databases (e.g., SQL) and API-based data retrieval—should replace unstructured storage methods like text files, improving usability and analysis capabilities. Furthermore, defining minimum requirements for data collection frequency, including continuous monitoring complemented by periodic in-depth surveys (e.g., every five years), would ensure a consistent and comprehensive understanding of bicycle traffic trends over time.

Integrating bicycle traffic data with broader transportation datasets could further enhance urban mobility management. Combining bicycle data with pedestrian and motor vehicle traffic information would support the development of more accurate multimodal transport models. Joint analyses of infrastructure impacts on different transport modes—such as the effect of new cycling facilities on traffic congestion—would inform more balanced transport policies. Additionally, leveraging data from Intelligent Transport Systems (ITSs) could enable the dynamic management of bicycle traffic, optimizing signal control and route recommendations in real time. Finally, integration with urban mobility applications, such as navigation tools that suggest optimal cycling routes based on real-time traffic conditions, could further encourage cycling as a viable mode of urban transport.

Addressing these challenges through standardization, technological integration, and data harmonization would significantly enhance the role of bicycle traffic data in urban planning and transport policy. Establishing a uniform and comprehensive data collection framework would not only improve infrastructure planning and traffic management but also contribute to broader sustainability and public health objectives, supporting the continued development of cycling-friendly cities.

5. Conclusions

The availability and quality of bicycle traffic data play a fundamental role in shaping effective transport policies and urban planning strategies. Accurate and standardized data enable cities to identify high-demand cycling corridors, prioritize infrastructure investments, evaluate the effectiveness of cycling policies, enhance road safety, and promote cycling as a sustainable mode of transport. However, this study highlights several challenges associated with the current state of data collection, which hinder the development of comprehensive and evidence-based cycling policies.

The findings presented in this study clearly show that although Polish cities are increasingly engaging in cycling-related data collection, these practices are often fragmented, inconsistent, or lack strategic direction. The analysis revealed recurring challenges such as insufficient standardization, limited use of automated tools, and minimal integration with decision-making processes. These observations directly support the conclusion that there is a pressing need for coordinated frameworks, more precise guidelines, and better resource allocation to ensure that cycling data are a reliable foundation for urban mobility planning.

One of the primary issues is the fragmentation of data across various institutions. Local governments, law enforcement agencies, cycling organizations, and transport authorities often collect different types of data, but the lack of integration limits their usability for broader analyses. Additionally, inconsistencies in data collection methodologies make it difficult to compare cities, assess the impact of interventions, and track long-term trends in cycling mobility. Without a unified approach, planning decisions may be based on incomplete or incomparable datasets, leading to inefficiencies in infrastructure development and resource allocation.

Another critical challenge is the lack of coordination among stakeholders. Inconsistent data-sharing mechanisms can result in delayed responses to safety hazards, inefficient investment planning, and missed opportunities for optimizing transport systems. Moreover, the absence of standardized data formats increases technological barriers, requiring additional resources for data conversion and integration. Addressing these issues is essential for enhancing the reliability of cycling data and their applicability in urban mobility strategies.

To improve the effectiveness of bicycle traffic management and planning, the following key recommendations should be considered:

• Standardization of data collection methodologies—Defining uniform indicators and measurement techniques to ensure comparability between cities and regions.
• Development of a centralized database—Establishing a shared platform for storing and analyzing bicycle traffic data at the national or regional level.
• Enhanced collaboration between institutions—Encouraging cooperation between local governments, research institutions, and transport agencies to improve data accessibility and integration.
• Implementation of advanced analytical tools—Utilizing machine learning, GIS, and big data techniques to derive deeper insights into cycling patterns and infrastructure performance.
• Continuous monitoring and periodic evaluations—Conducting systematic assessments to measure the impact of cycling policies and infrastructure investments over time.
• Integration with other transport data—Combining bicycle traffic data with pedestrian and motor vehicle movement data to develop holistic urban mobility models.

By implementing these measures, cities can make more informed decisions that enhance cycling infrastructure, improve safety, and promote sustainable urban mobility. The findings of this study provide valuable insights into how cities manage cycling-related data and highlight the potential for improved data integration to support sustainable urban mobility strategies. The differences in data collection approaches indicate a need for standardization and improved coordination between municipal units, which could enhance the effectiveness of bicycle traffic management and contribute to broader urban sustainability and mobility planning goals.

As a next step in scientific research, further analysis should focus on developing a comprehensive framework for standardizing bicycle traffic data collection. Establishing clear guidelines for data formats, measurement techniques, and reporting standards will be crucial in ensuring consistency and comparability across different urban contexts. Future studies should explore the feasibility of implementing such standards at regional and national levels, as well as the potential benefits of integrating them into broader intelligent transportation systems.

Future research will focus on direct consultations with municipal officials responsible for collecting and analyzing bicycle traffic data. The goal is to collaboratively develop a standardized framework for data collection, ensuring consistency and interoperability between cities. This framework will not only facilitate more accurate data-driven decision-making but also enable cross-city comparisons, providing a clearer picture of cycling trends at the national level.

Furthermore, the authors plan to explore how standardized data can be effectively utilized in urban infrastructure development. By integrating these datasets into transport planning models, cities will be able to identify areas requiring new cycling infrastructure, optimize investments in mobility projects, and assess the long-term impact of policy changes. The potential for real-time data analysis and predictive modelling will also be investigated, allowing municipalities to proactively respond to shifts in cycling behaviour.