Promoting Sustainable Transport: A Systematic Review of Walking and Cycling Adoption Using the COM-B Model

Abstract

Walking and cycling, as active modes of transportation, play a vital role in advancing sustainable urban mobility by reducing emissions and improving public health. However, widespread adoption faces challenges such as inadequate infrastructure, safety concerns, socio-cultural barriers, and policy limitations. This study systematically reviewed 56 peer-reviewed articles from 2004 to 2024, across 30 countries across five continents, employing the Capability, Opportunity and Motivation-Behaviour (COM-B) framework to identify the main drivers of walking and cycling behaviours. Findings highlight that the lack of dedicated infrastructure, inadequate enforcement of road safety measures, personal and traffic safety concerns, and social stigmas collectively hinder active mobility. Strategic interventions such as developing integrated cycling networks, financial incentives, urban planning initiatives, and behavioural change programs have promoted increased engagement in walking and cycling. Enhancing urban mobility further requires investment in pedestrian and cycling infrastructure, improved integration with public transportation, the implementation of traffic-calming measures, and public education campaigns. Post-pandemic initiatives to establish new pedestrian and cycling spaces offer a unique opportunity to establish enduring changes that support active transportation. The study suggests expanding protected cycling lanes and integrating pedestrian pathways with public transit systems to strengthen safety and accessibility. Additionally, leveraging digital tools can enhance mobility planning and coordination. Future research is needed to explore the potential of artificial intelligence in enhancing mobility analysis, supporting the development of climate-resilient infrastructure, and informing transport policies that integrate gender perspectives to better understand long-term behavioural changes. Coordinated policy efforts and targeted investments can lead to more equitable transportation access, support sustainability goals, and alleviate urban traffic congestion.

1. Introduction

Urbanisation, economic growth, and technological advancements in recent decades have contributed to significant changes in the world’s transportation systems. Although improved mobility has driven economic growth and increased accessibility, the global dependence on motorised transport has resulted in substantial environmental, social, and public health issues [1,2]. Transport systems remain major sources of greenhouse gas emissions, which generate about 25% of worldwide CO₂ emissions, while estimates predict that these emissions will potentially double by 2050 without significant policy changes [3,4,5]. The combination of traffic congestion, worsening air quality and noise pollution, and rising traffic-related deaths makes urgent issues that particularly harm marginalised populations in cities [6,7]. The rise of sustainable transport solutions represents a major global policy focus to address environmental challenges, with walking, cycling, and public transit being promoted as alternatives to private car use [8,9]. Walking and cycling are primary transportation alternatives that provide a low-cost option that reduces emissions while improving public health as an alternative to driving [10,11]. Urban areas prioritise pedestrian and cycling infrastructure, while seamless multimodal transportation connectivity improves traffic flow, reduces emissions, and enhances overall quality of life [8,12].

The urban transport system of leading cities, such as Copenhagen, Amsterdam, and Berlin, places active mobility at their centre, which is reinforced by extensive cycling networks and pedestrian areas along with policy incentives [13,14]. The examples of these cities show that targeted infrastructure spending combined with policy measures and coordinated transport planning leads to substantial growth in walking and cycling as preferred modes of transport [1,11]. In North America and Asia, cities like New York, Montreal, Beijing, and Tokyo have established bike-sharing schemes alongside car-free zones and pedestrian-friendly developments, fostering a culture of active transportation [15,16]. Though certain regions have achieved success in active transportation systems, numerous urban areas globally struggle with systemic obstacles to incorporating walking and cycling into their primary transport strategies [17,18]. Car-focused urban planning models in numerous cities have resulted in inadequate pedestrian facilities, discontinuous cycling routes, and unsafe pedestrian crossings [19,20,21]. The threat to traffic safety continues to be a significant issue in urban areas where vehicle speeds are high and the enforcement of pedestrian and cyclist rights remains inadequate [22,23,24]. The adoption of active mobility faces substantial challenges from cultural attitudes, socio-economic disparities, and institutional barriers. Urban cycling is commonly viewed as transportation for economically disadvantaged people, which creates social stigma and prevents middle- and high-income groups from adopting it [25,26]. Research demonstrates gender inequalities exist in cycling participation because women experience safety issues and harassment while lacking proper inclusive facilities [11,27]. However, beyond these physical and social barriers, behavioural factors play a crucial role in shaping walking and cycling adoption.

Active mobility research applies multiple behavioural models to gain insights into how people choose and use active transportation options like walking and cycling. The Capability, Opportunity, and Motivation-Behaviour (COM-B) model is widely used to design effective interventions by identifying key factors that influence behaviour [28,29,30]. Capability refers to an individual’s physical and psychological ability to engage in active travel, encompassing factors such as physical fitness, cycling proficiency, and the knowledge of safe travel practices. Opportunity considers external factors, including environmental and social influences, that either facilitate or hinder active mobility, such as infrastructure availability, traffic conditions, and social norms [31]. Motivation involves both reflective and automatic processes that shape an individual’s decision to participate in active travel. Reflective motivation includes conscious intentions and evaluations, while automatic motivation involves habitual behaviours and emotional responses [32]. By analysing these interconnected components, the COM-B model provides a comprehensive framework for understanding and promoting active mobility. The Theory of Planned Behaviour (TPB) proposes that individual intentions to participate in walking or cycling depend on their attitudes, subjective norms, and perceived behavioural control. Studies utilising the TPB in active travel research demonstrate that favourable cycling perceptions, social support, and urban navigation confidence lead to higher participation rates [33]. Another widely used model, the Transtheoretical Model (TTM), describes behaviour change as a multi-stage process: the TTM represents behaviour change as a five-stage process consisting of pre-contemplation, contemplation, preparation, action, and maintenance [34]. Research using the TTM for cycling and walking behaviours indicates that interventions must be customised based on each person’s current stage of behavioural change [35,36]. Awareness campaigns for those in the contemplation stage should highlight active mobility health benefits, infrastructure upgrades, and incentive schemes that should assist in the preparation and action stage for individuals to adopt walking or cycling [37]. Policymakers who combine behavioural models with transport planning can develop superior interventions, which surpass traditional infrastructure investments. To promote sustainable walking and cycling habits in urban areas, addressing psychological and social behavioural determinants is essential, thereby making active mobility an appealing choice for diverse populations.

This research employs the COM-B model as its primary analytical framework, as it effectively captures the dynamic interaction between personal capability, social and environmental opportunity, and intrinsic motivation that affect walking and cycling adoption. Unlike other behavioural models, COM-B offers a systematic yet adaptable approach that integrates individual-level decision-making with broader environmental and systemic elements driving mobility choices. Although TPB and TTM offer partial insights into motivational and behavioural processes, they are limited in their ability to account for the environmental and economic factors that influence mobility choices [29,34]. TPB primarily focuses on the formation of behavioural intentions through attitudes, subjective norms, and perceived behavioural control, but it underrepresents the role of the physical infrastructure and social environments in shaping actual behaviour. Similarly, TTM provides a valuable framework for understanding the stages of behavioural readiness, yet it does not adequately address how external systems or social conditions facilitate or constrain progression through these stages. In contrast, the COM-B model is more closely aligned with the objectives of this review, as it explicitly integrates three essential components, capability, opportunity, and motivation, to explain how behaviour is either enacted or restricted. This integrative framework allows researchers to map both internal and external barriers (e.g., fitness, safety, infrastructure, social norms) within a single framework. Furthermore, COM-B serves as the foundation for the Behaviour Change Wheel, which links behavioural diagnosis to policy design. Given this study’s focus on identifying multi-level barriers and designing real-world interventions, COM-B offers a particularly suitable foundation for connecting behavioural theory with urban policy application.

The recognised advantages of walking and cycling remain unincorporated into the long-term transportation planning of many cities because deep-rooted policies favour automotive transport alongside existing infrastructure priorities [1,17]. Studies show that infrastructure alone cannot change travel behaviour, but financial incentives, awareness campaigns, and regulatory reforms are necessary to promote active transport adoption [8,11]. Urban areas that achieved higher rates of walking and cycling activity introduced all-encompassing policy strategies, which established protected cycle lanes and pedestrian-friendly zones alongside economic and social mobility determinants. The implementation of high-quality cycling infrastructure, together with traffic-calming measures and pedestrianised zones, has shown success in cities like Copenhagen, Amsterdam, and Berlin [12,16]. Financial incentives serve as essential tools for influencing transport behaviour alongside physical infrastructure development. Several municipalities now offer bicycle purchase subsidies and tax breaks for active commuters while implementing congestion charges to reduce car usage and support sustainable transportation methods [9,15]. Public awareness campaigns, which aim to transform social norms related to walking and cycling, have successfully improved participation rates in areas where cycling has faced stigma and lower-income associations [1,18]. Policy effectiveness varies, but tailored strategies that reflect each city’s resource level are essential for equitable outcomes [8,17].

Urban policymakers can develop areas that encourage active travel through walking and cycling by adopting a comprehensive approach that supports sustainability, public health objectives, and urban liveability goals. Even though developments look positive, substantial obstacles remain in place, including pushback from car-focused industries, political reluctance, and cultural beliefs that identify cycling and walking as secondary travel methods rather than primary mobility options [25,26]. Both high-level policy changes and bottom-up community advocacy efforts are needed to transform public attitudes while making active transportation available to every city dweller. While previous studies have examined case-specific interventions for walking and cycling, there remains a critical gap in comparative research that synthesises global best practices and behavioural insights [9,11,29,38]. The role of active mobility as a core solution for sustainable urban transportation has become evident as cities address climate change together with traffic jams and public health deterioration [3,4]. The underutilisation of walking and cycling opportunities persists because of entrenched obstacles related to the infrastructure, as well as socio-cultural and behavioural factors [1,13]. The research investigates global findings on active mobility adoption to support ongoing discussions about sustainable transportation [6,9]. The research applies behavioural models to inform transport strategies aimed at promoting sustainable active mobility [1,12].

This review identifies global barriers and enablers of cycling and walking adoption, focusing on behavioural and infrastructural dimensions. It emphasises the importance of a comprehensive, multi-dimensional understanding of how urban design, public attitudes, and policy measures collectively shape mobility choices. It also offers practical, evidence-based recommendations for policymakers to enhance active mobility infrastructure and support behaviour change initiatives [1,39]. This study makes three novel contributions. First, it offers a global synthesis of behavioural and infrastructure-related barriers and enablers of active mobility, drawing from multiple contexts. Second, it integrates behavioural science with transport planning by applying the COM-B framework to identify the most effective policy levers. Third, it proposes a structured set of policy and planning recommendations, supported by global case evidence, that can be adapted to diverse urban contexts to increase walking and cycling uptake. This study is guided by the following main research questions (RQs) to address these research gaps:

RQ1: What are the key infrastructural, socio-cultural, and behavioural barriers and facilitators influencing walking and cycling adoption worldwide?

RQ2: How have policy interventions and urban planning strategies shaped walking and cycling behaviour in different global contexts?

RQ3: What best practices from successful active mobility initiatives can be adapted and implemented across diverse urban regions?

2. Materials and Methods

The research utilised a systematic two-stage methodology to achieve rigour and reproducibility while maintaining transparency throughout the synthesis and analysis of literature on walking and cycling adoption trends in urban transport systems. The methodology consisted of the following:

(1) Systematic literature search and identification: the systematic literature search identified relevant studies using explicit search criteria across various academic databases.

(2) Analytical framework and thematic analysis: this research used a structured analytical framework that features the COM-B model to organise and interpret findings, enabling a comprehensive understanding of the barriers and facilitators of active mobility.

2.1. Search Strategy

This research utilised a systematic search method to gather and combine findings about walking and cycling adoption in cities by examining behavioural patterns, infrastructural aspects, and socio-economic, as well as policy-related, influences on active mobility. The systematic literature review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Supplementary Material) [40] to guarantee rigour and clear transparency while maintaining reproducibility throughout study selection and data synthesis. This research then utilised the COM-B model to systematically identify barriers and facilitators to active mobility adoption and combined this behavioural science perspective with transport policy and infrastructure analysis [39]. To retrieve high-quality, peer-reviewed studies, the search was conducted using two major academic databases, Scopus and Web of Science, chosen for their comprehensive coverage of transport, urban planning, sustainability, and behavioural research [41,42,43].

The research used a structured Boolean search strategy to optimise precision and recall through search strings like (“walking” OR “cycling” OR “active mobility”) AND (“urban transport” OR “sustainable transport” OR “city mobility”) AND (“barriers” OR “facilitators” OR “policy interventions”). It included additional variations focused on behavioural determinants and policy measures that influence walking and cycling adoption. The search terms targeted titles, abstracts, and keywords to capture all pertinent literature concerning active mobility adoption, safety perceptions, social influences, and policy initiatives [44,45]. A manual review of reference lists from core articles helped incorporate influential research by finding relevant studies missed during the initial database search [42,46]. The search strategy’s validation process included pilot searches and expert consultations from transport researchers, urban planners, and behavioural scientists. This process led to iterative refinements of Boolean strings, keyword selection, and search filters, enhancing the precision of the results and minimising irrelevant studies [47,48].

2.2. Inclusion and Exclusion Criteria

The study selection process followed a rigorous multi-stage screening approach to ensure that the systematic review on walking and cycling adoption in urban transport included only high-quality studies with strong thematic relevance and methodological soundness. The search results were refined for relevance by selecting peer-reviewed journal articles, high-quality conference papers, and reputable institutional reports while excluding non-academic sources, opinion pieces, and technical studies unrelated to transport behaviour or policy [47]. The strategy effectively captured well-cited articles and systematic reviews, which allowed for synthesising leading research findings on walking and cycling [49,50]. This systematic literature review on walking and cycling adoption in urban areas applied predefined inclusion and exclusion criteria to ensure the relevance, quality, and methodological rigour of the selected studies. These criteria were consistently applied throughout all stages of the review process, including the title screening, abstract evaluation, full-text review, and final quality assessment. This approach ensured the inclusion of only high-quality, peer-reviewed studies that specifically addressed barriers, facilitators, and policy interventions relevant to the objectives of active mobility research.

The inclusion criteria specifically targeted studies that delivered valuable insights into walking and cycling adoption by examining behavioural patterns, infrastructural elements, socio-economic factors, and policy influences related to active mobility. The research inclusion criteria specified an examination period from 2004 to 2024 to monitor changes in urban transport policies, infrastructure, and public perception shifts regarding walking and cycling across two decades. The selected timeframe coincides with the era in which international sustainability efforts like the United Nations Sustainable Development Goals (SDGs), Paris Climate Agreement and national urban transport plans deeply impacted sustainable mobility discussions [51,52]. The literature search included only English-language studies to achieve accurate interpretation and uniform research methods across all selected studies. Limiting the studies to English language materials introduces a potential language bias; however, it ensures that researchers can analyse the studies without the risk of translation errors that could distort key findings.

To ensure thematic alignment with the research objectives, studies were required to focus on at least one of the following core areas: (1) urban environments present several obstacles to walking and cycling adoption, such as infrastructural limitations, safety concerns, and socio-cultural and behavioural challenges. (2) Active mobility facilitation comes through pedestrian-friendly street designs plus dedicated cycling lanes and includes incentives for non-motorised transport with community-led active mobility initiatives. (3) Policy interventions to boost active mobility encompass urban planning techniques, financial incentives, and land use and transport regulations promoting walking and cycling. The relationship between walking and cycling behaviours includes an analysis of public perceptions, social norms, and psychological motivators that drive active transportation choices. Walking and cycling offer public health improvements, economic savings, and environmental advantages, which include lower carbon footprints and better urban air quality. The review analysed active mobility studies from every major region to ensure that they represented a variety of socio-economic and infrastructural contexts. The study includes global research from developed and developing regions, ensuring a diverse representation of urban mobility contexts.

The inclusion criteria aimed to maximise thematic coverage, but the exclusion criteria played an essential role in filtering the final dataset to keep only relevant and high-quality studies. Research that examined private vehicle transport, including electric vehicles (EVs), AVs, or ride-sharing services, did not qualify for inclusion unless they specifically addressed how these vehicles affected walking and cycling behaviour. Studies on private vehicle transport, bicycle technical advancements, recreational cycling, and e-mobility were excluded from consideration unless these studies investigated their influence on walking and cycling behaviour. The systematic review kept scientific integrity and rigour by removing non-peer-reviewed sources alongside grey literature with questionable reliability and commercial or opinion-based publications. The study included institutional reports from government agencies and urban planning organisations that met high methodological standards, while other sources like blog posts, editorial articles and non-systematic literature reviews were excluded. Applying inclusion and exclusion criteria throughout each phase of study selection ensured that the resulting dataset was methodologically robust and maintained thematic relevance and policy focus. The studies were selected based on strict quality criteria. These included a clear research design (quantitative, qualitative, or mixed methods), adequate sample size and representativeness (excluding studies with fewer than 10 participants or limited generalisability), and transparency in methods and limitations. Only peer-reviewed studies with citations in reputable journals were included to ensure credibility. Each study also had to directly address barriers, facilitators, or policies related to walking and cycling. The review applies PRISMA guidelines and a multi-stage screening to provide a rigorous synthesis of evidence on active mobility adoption.

The review criteria enabled researchers to identify crucial active mobility adoption challenges and opportunities while delivering an extensive synthesis of barriers and facilitators along with urban policy interventions that support walking and cycling in cities around the globe.

2.3. Studies Included in the Review

A systematic screening process was conducted following PRISMA guidelines, resulting in 56 studies selected for final analysis. The researchers obtained and critically evaluated the full texts of 364 studies to confirm their relevance to the research goals and their methodological quality. The research team excluded 308 studies because they did not concentrate enough on walking or cycling adoption and lacked methodological rigour through unclear research designs and inadequate sample sizes or data collection methods. Additionally, other discussed studies included research from developed transport behaviour broadly without active mobility insights and included only conceptual discussions without empirical evidence. A total of 56 studies passed the full-text screening stage and moved forward to the final quality evaluation. A quality assessment process was conducted to maintain both the reliability and robustness of the final dataset. An analysis of 56 selected studies using the COM-B framework will yield a detailed, evidence-based understanding of factors influencing walking and cycling in urban transport systems. To visually summarise the key themes and topics from the reviewed studies, a word cloud was generated using the keywords extracted from the 56 selected articles.

As shown in Figure 1, this word cloud highlights the most frequently occurring terms, offering a concise overview of the dominant research areas. The most prominent words, such as “Cycling”, “Walking”, “Mobility”, and “Sustainable”, underscore recurring themes related to active transportation, urban mobility, sustainability, public health, and policy interventions. This visualisation provides insight into the critical discussions shaping research on sustainable transport and urban development. The study selection process is made transparent through the creation of the PRISMA flow diagram shown in Figure 2. The diagram provides a visual breakdown of the studies that were retrieved and then went through screening to be either excluded or retained in the final dataset. The PRISMA framework strengthens reproducibility while promoting methodological transparency through detailed documentation of the screening process [40,49].

2.4. Geographical Coverage of Reviewed Articles

A geographical coverage map was created to illustrate the distribution of the 56 studies across the globe, as shown in Figure 3. The studies span 30 countries across five continents, reflecting a broad global perspective on urban mobility research. Europe is the most represented region, with 20 studies conducted in countries such as Italy, the Netherlands, Malta, Cyprus, Denmark, France, the Czech Republic, Spain, Switzerland, Norway, the UK, Germany, and Ireland. Asia follows with 14 studies, covering China, India, Iran, Turkey, Bangladesh, Singapore, and South Korea. Africa is represented by 9 studies conducted in Kenya, Ghana, and Nigeria, while the Americas account for 8 studies from the USA, Brazil, Canada, Mexico, and Jamaica. Lastly, Oceania contributes 5 studies, all conducted in Australia and New Zealand. This geographical distribution underscores the widespread research interest in urban mobility, with a strong emphasis on European and Asian contexts, while also capturing insights from Africa, the Americas, and Oceania.

2.5. Coding and Thematic Analysis Using NVivo

The research utilised NVivo 14 software to carry out a thematic analysis for systematic study assessment, which enabled structured coding alongside theme categorisation and visualisation. The analytical framework based on the COM-B model served as the primary tool for classifying barriers and facilitators that impact walking and cycling adoption. This approach resulted in a complete synthesis of results rooted in a behavioural understanding that provided detailed insight into factors influencing urban transport systems’ active mobility uptake. A two-stage thematic analysis process was applied. Data extraction and open coding constituted the primary stage of the process. Researchers imported all 56 selected studies into NVivo software to systematically code sections on findings, discussions, and conclusions, which helped identify patterns and themes concerning barriers, facilitators, and policy interventions for walking and cycling.

The initial coding structure was designed using both inductive and deductive approaches. The researchers implemented a dual-method coding structure, which used inductive analysis to discover emergent themes from the studies and a deductive analysis to maintain congruence with research goals and the COM-B framework [53]. Following open coding, the second stage of the thematic analysis involved categorising themes under the three core elements of the COM-B framework. This research focused on coding studies to identify themes that describe how people’s physical and mental capabilities affect their participation in walking and cycling activities. The Capability (C) element comprises fitness levels and cycling skills, road safety knowledge, and accessibility issues affecting disabled individuals. Researchers discovered psychological obstacles, including road accident fears, risk perceptions, and low cycling confidence on shared roads. The Opportunity (O) category encompassed the various external, environmental, and social factors influencing people’s ability to adopt active mobility behaviours. The section’s coding highlighted themes related to the quality of infrastructure, such as cycle lanes and pedestrian-friendly roads, traffic rules, policy interventions, financial incentives, and prevailing cultural norms. Research on urban planning policies, along with government-run cycling programs and city-wide active mobility strategies, was organised in this section. The Motivation (M) component included studies examining habit formation and social identity related to active commuting, as well as motivational incentives. The COM-B model helped identify research gaps, inconsistencies, and key trends, which guaranteed that the final literature synthesis maintained behavioural relevance and policy significance.

The study utilised NVivo’s data visualisation tools, including word clouds, coding matrices, and network maps, to identify common barriers, facilitators, and policy measures related to walking and cycling adoption. The researchers quantified the prevalence of each theme through a coding frequency analysis and ensured that our discussion highlighted the most frequently mentioned issues. The thematic outcomes collected from NVivo were organised into main thematic segments, which were then interpreted in the results and discussion portions of the paper. Through a systematic approach that integrated a thematic analysis with the COM-B behavioural framework, researchers achieved structured findings that were behaviourally anchored, thus allowing urban planners and policymakers to create evidence-based active mobility solutions for cities around the globe.

2.6. Data Collection and Analysis Methods in Walking and Cycling Studies

Various methodological approaches for studying walking and cycling behaviours and their associated barriers, facilitators, and policy implications emerged from the data collection and analysis methods of the 56 studies reviewed in this systematic investigation (Table 1). Researchers used quantitative, qualitative, and mixed-methods approaches to collect data for active travel studies through online surveys like those by [54,55,56], as well as through systematic literature reviews such as those conducted by [14,57,58]. Significant data sources, including trip data from over one million Beijing users [59] and spatial GIS-based urban morphology evaluations [60,61], show how research is moving towards computational and geospatial analysis methods. A structured analysis of policy documents combined with a thematic content analysis serves as a methodological approach for multiple studies assessing government interventions [62,63,64]. Transport research demonstrates the integration of advanced technological methodologies through machine learning applications [65] and simulation-based crash modelling [57]. The data analysis approaches reflect a range of methodologies, including thematic content analysis and comparative policy evaluations, both of which are fundamental to qualitative research [66,67,68]. Statistical modelling techniques like multinomial logistic regression [55,69], factor-cluster analysis [56], and binomial logistic regression [70] are commonly used in quantitative research to study user segmentation along with travel behaviour determinants and urban density impacts. Reinforcement learning models help optimise active travel interventions, according to [65], while the space syntax analysis by [71] evaluates how urban morphology influences cycling adoption, and [72] deploys virtual reality-based experiments to understand cycling behaviours.

Mobility challenges in different urban areas can be better understood through mixed-method approaches, which integrate archival records with direct observations and structured interviews, as demonstrated by [73]. The analysis reveals the increasing application of geospatial data alongside machine learning and synthetic population modelling within transport research, which indicates a transition to data-driven urban mobility planning [10,74,75]. Integrating traditional survey methods with big data analytics allows researchers to obtain a deep understanding of behavioural patterns and policy impacts along with environmental factors that drive active travel. The use of self-reported survey data in many studies creates potential biases due to social desirability effects and recall errors. Policy document reviews yield institutional insights but fail to capture real-world behavioural effects until they receive empirical validation. Holistic evaluations of walking and cycling interventions require interdisciplinary methods that connect policy assessments with live mobility data and behavioural simulations.

Table 1. Summary of data collection and analysis methods in walking and cycling studies (56 articles).

Author(s)	Year	Country	Method
			Data Collection	Data Analysis
D’Apuzzo et al. [57]	2024	Italy	Literature review, simulation-based crash modelling	Impact analysis using multi-body codes to assess injury risk
Salm et al. [56]	2023	Netherlands	Online survey with 514 participants (442 valid responses), distributed via newsletters, bicycle shops, and social media	Factor-cluster analysis to segment speed pedelec users based on sociodemographic characteristics, attitudes, and travel behaviour
Soliz et al. [62]	2023	Mexico	Policy analysis and document review	Comparative content analysis focusing on social equity in transportation
Kazmi et al. [65]	2024	Italy	Machine learning process using smart city data and healthcare devices	Model selection, training, evaluation, and reinforcement learning-based optimisation
Rosario et al. [74]	2024	Australia	Synthetic population modelling, trip planning API data	Mode shift analysis, quantification of latent walking, spatial analysis using GIS
Nuuyandja et al. [68]	2024	Ghana	Systematic review of urban transport studies, policy documents, and empirical research in African cities	Thematic content analysis, comparative evaluation of urban form, and mode choice relationships
Lowe et al. [66]	2024	Australia	Content analysis of city planning policy documents from the City of Melbourne and the Victorian State Government (2020–2022)	Thematic content analysis, comparative policy evaluation based on evolutionary resilience and healthy cities frameworks
Attard et al. [76]	2023	Malta	High-resolution satellite imagery, Geographic Information Systems (GIS) analysis	Comparative spatial analysis of urban space distribution, equity evaluation across different transport modes
Hafenrichter et al. [70]	2024	Australia	Queensland Household Travel Survey (QHTS) data (2018–2023), Australian Bureau of Statistics population density data	Binomial logistic regression analysis of demographic and urban density impacts on transport mode choice
Yin et al. [59]	2024	China	Big trip data from over 1 million users in Beijing, geospatial big data, and socioeconomic data	Explainable machine learning (SHAP), Gradient Boosting Decision Tree (GBDT), statistical modelling
Papageorgiou and Tsappi [63]	2024	Cyprus	Literature review, policy analysis, case study evaluation	Thematic content analysis, framework development
Høyer-Kruse et al. [77]	2024	Denmark	Systematic literature search of studies on social and built environments influencing physical activity	Thematic content analysis, categorisation of studies based on built environment factors
Maas and Attard [64]	2022	Malta	Policy document analysis, 22 semi-structured interviews with stakeholders, experts, and public/private operators	Thematic content analysis of policy measures and cycling promotion strategies
Zafri et al. [55]	2021	Bangladesh	Online questionnaire survey with 804 respondents across Bangladesh	Multinomial logistic regression models, descriptive analysis
Vineis et al. [78]	2021	Italy	Policy review commissioned by the Italian Ministry of Health, analysis of national health and environmental policies, urban planning, transport, and dietary interventions	Thematic policy analysis, evaluation of primary prevention strategies for non-communicable diseases and climate change mitigation
Chen et al. [69]	2022	China	Online survey with 606 respondents from Beijing, including data on travel behaviour, attitudes, and the built environment	Multinomial logistic regression models, descriptive statistics, and factor analysis
Canitez et al. [67]	2020	Turkey	Review and qualitative analysis of Istanbul’s urban transport policies and strategies	Thematic content analysis of policy documents and mobility trends
Vietinghoff [79]	2021	France	19 narrative and semi-structured interviews with policymakers, residents, and bike service providers in Grenoble	Thematic content analysis of qualitative interviews, intersectional framework
Chevalier and Charlemagne [80]	2020	China	Survey with over 400 responses from kindergartens in inner Shanghai, mapping perceived dangers and drawing routes to school	Thematic analysis of danger perception, Desire Lines Analysis, infrastructure assessment
Ramirez-Rubio et al. [81]	2019	Spain	Review of Health in All Policies (HiAP) implementation in multiple cities across Europe, Africa, and Latin America	Conceptual framework analysis linking SDGs, urban policies, environmental exposures, and health outcomes
Barbarossa [82]	2020	Italy	Analysis of urban mobility policies in 10 Italian metropolitan cities post-COVID-19	Comparative policy analysis on sustainable urban mobility transformations
Cirianni et al. [83]	2018	Italy	Review of Sustainable Urban Mobility Plans (SUMPs) from various European cities	Thematic content analysis, benchmarking of urban mobility policies
Gabrhel [84]	2019	Czech Republic	Survey of 1301 respondents in Litoměřice, part of the city’s Sustainable Urban Mobility Plan (SUMP)	Exploratory factor analysis, multinomial logistic regression, descriptive statistics
Mitullah et al. [85]	2017	Kenya	Case study on Nairobi’s walking and cycling infrastructure, travel behaviour, and policy frameworks	Qualitative thematic analysis, policy review, and assessment of non-motorised transport integration
Sietchiping et al. [86]	2012	Kenya	Review of urban mobility policies, observations, and case study comparisons across sub-Saharan African cities	Thematic content analysis, comparative urban transport policy evaluation
Pan [87]	2011	China	Review of urban travel policies, case studies of Chinese cities	Comparative policy analysis, transport planning evaluation
Ahmad and Oliveira [88]	2016	India	Urban mobility determinants	Sustainable and inclusive urban transportation
Soltani [89]	2017	Iran	Review of urban transport policies, land use patterns, and case studies in major Iranian cities	Thematic analysis of urban mobility trends, evaluation of transport sustainability challenges
Lawson et al. [90]	2013	Ireland	Analysis of Irish census data (2006) on non-motorised commuter journeys across five major cities	Logistic regression modelling, thematic analysis of urban mobility patterns
Lopez-Escolano et al. [60]	2017	Spain	Case study of cycling infrastructure in Zaragoza, spatial analysis using GIS, accessibility assessments	Thematic content analysis, mapping of cycling infrastructure, assessment of accessibility to bike lanes and shared bicycles
Stevenson et al. [91]	2016	Australia	Health and travel surveys, urban design modelling	Statistical modelling, scenario analysis using health impact assessment frameworks
Hensley et al. [92]	2014	Australia	Systematic review of 22 studies on path dependence in urban and transport planning	Thematic content analysis of literature on policy and planning frameworks
Useche et al. [93]	2024	Russia	Online survey of young cyclists in Russian cities, observational study of cycling behaviour	Statistical analysis, correlation analysis among rider characteristics, behaviour, and safety incidents
Adinarayana et al. [94]	2024	India	Official reports on road infrastructure conditions, historical accident data, demographic data from surveys and government databases	Official reports on road infrastructure conditions, historical accident data, demographic data from surveys and government databases
Distefano and Leonardi [95]	2023	Italy	Online survey with 562 participants, field observations, and urban walking strategies	Factorial Analysis of Variance (ANOVA) for understanding pedestrian satisfaction
Panahi et al. [96]	2022	Iran	Semi-structured interviews with 66 older adults (purposeful sampling)	Thematic analysis [53] (p. 6) using Atlas.ti
Basil and Nyachieo [97]	2023	Kenya	Descriptive survey with 137 respondents (convenience sampling)	Quantitative analysis
Razak [73]	2022	Nigeria	Mixed-methods: archival records, spatial analysis, direct observation, and structured interviews (150 respondents)	Triangulation of qualitative and quantitative data
Schneider et al. [98]	2022	USA	Open-ended responses from the 2020 Milwaukee Safe and Healthy Streets survey	Binary logistic models
Ullmann et al. [72]	2022	Germany	Virtual reality simulation with 93 participants cycling through a virtual urban environment	Statistical analysis using t-tests, Mann–Whitney U tests, and Wilcoxon signed-rank tests
Gouais et al. [99]	2023	Jamaica	Semi-structured interviews with 10 expert stakeholders (urban planners, public health professionals, and civil society representatives)	Thematic analysis using NVivo software
Castro et al. [71]	2022	Brazil	Space Syntax Modelling, cyclist flow counts, accident records, and infrastructure mapping	Spatial and statistical analysis using Space Syntax metrics (Normalised Choice, NACH)
Scorza and Fortunato [100]	2021	Italy	Geomorphological analysis, Space Syntax Analysis, Place Syntax Analysis	Morphological and syntactic analysis of urban space
Roberts [101]	2020	Canada	Historical research using archival sources, media articles, and semi-structured interviews with cycling advocates and stakeholders	Qualitative historical analysis and thematic interpretation
Fortunato et al. [61]	2019	Italy	Geospatial analysis using GIS-based territorial assessment and urban morphology evaluation	Spatial analysis and territorial impact assessment
Götschi et al. [102]	2017	Switzerland	Systematic literature review of conceptual frameworks in active travel research	Synthesis and development of a comprehensive conceptual framework
Nordfjærn et al. [103]	2019	Norway	Cross-sectional survey with 441 university students in Trondheim	Linear mixed model analysis
Aldred [104]	2014	UK	Mixed methods: survey research, ethnography, policy analysis	Thematic analysis and policy evaluation
Macmillan et al. [105]	2014	New Zealand	System dynamics modelling using stakeholder interviews, workshops, and secondary data sources	Simulation modelling of policy impacts on cycling trends, health, and environmental outcomes
Khayesi et al. [106]	2010	Kenya	Archival research and policy analysis on urban transport planning for pedestrians, cyclists, and street vendors	Critical geographical analysis of transport policy and urban planning
Nguyen et al. [75]	2015	Singapore	Before-and-after study using cyclist counts, field observations, and perception surveys	Statistical analysis using paired sample t-tests and GIS-based spatial analysis
Mueller et al. [107]	2015	Spain	Systematic review of health impact assessment studies on active transportation	Comparative risk assessment and cost–benefit analysis
Humberto et al. [108]	2021	Brazil	Longitudinal intervention study with surveys of caregivers (pre, post, follow-up) and sentiment analysis of children’s statements	Difference-in-differences and time-series analysis
Roaf et al. [58]	2024	UK	Systematic review of 78 studies on interventions increasing active travel	Narrative synthesis and methodological quality assessment using the Mixed Methods Appraisal Tool
Kim et al. [109]	2023	South Korea	Online survey with 659 respondents (cyclists and walkers) using quota sampling	Partial least squares structural equation modelling (PLS-SEM), multi-group analysis (MGA), fuzzy set Qualitative Comparative Analysis (fsQCA)
Biehl et al. [54]	2018	USA	Online survey with 914 respondents (Amazon MTurk panel)	Ordinal logistic regression, factor analysis

Table 1. Summary of data collection and analysis methods in walking and cycling studies (56 articles).

Author(s)	Year	Country	Method
			Data Collection	Data Analysis
D’Apuzzo et al. [57]	2024	Italy	Literature review, simulation-based crash modelling	Impact analysis using multi-body codes to assess injury risk
Salm et al. [56]	2023	Netherlands	Online survey with 514 participants (442 valid responses), distributed via newsletters, bicycle shops, and social media	Factor-cluster analysis to segment speed pedelec users based on sociodemographic characteristics, attitudes, and travel behaviour
Soliz et al. [62]	2023	Mexico	Policy analysis and document review	Comparative content analysis focusing on social equity in transportation
Kazmi et al. [65]	2024	Italy	Machine learning process using smart city data and healthcare devices	Model selection, training, evaluation, and reinforcement learning-based optimisation
Rosario et al. [74]	2024	Australia	Synthetic population modelling, trip planning API data	Mode shift analysis, quantification of latent walking, spatial analysis using GIS
Nuuyandja et al. [68]	2024	Ghana	Systematic review of urban transport studies, policy documents, and empirical research in African cities	Thematic content analysis, comparative evaluation of urban form, and mode choice relationships
Lowe et al. [66]	2024	Australia	Content analysis of city planning policy documents from the City of Melbourne and the Victorian State Government (2020–2022)	Thematic content analysis, comparative policy evaluation based on evolutionary resilience and healthy cities frameworks
Attard et al. [76]	2023	Malta	High-resolution satellite imagery, Geographic Information Systems (GIS) analysis	Comparative spatial analysis of urban space distribution, equity evaluation across different transport modes
Hafenrichter et al. [70]	2024	Australia	Queensland Household Travel Survey (QHTS) data (2018–2023), Australian Bureau of Statistics population density data	Binomial logistic regression analysis of demographic and urban density impacts on transport mode choice
Yin et al. [59]	2024	China	Big trip data from over 1 million users in Beijing, geospatial big data, and socioeconomic data	Explainable machine learning (SHAP), Gradient Boosting Decision Tree (GBDT), statistical modelling
Papageorgiou and Tsappi [63]	2024	Cyprus	Literature review, policy analysis, case study evaluation	Thematic content analysis, framework development
Høyer-Kruse et al. [77]	2024	Denmark	Systematic literature search of studies on social and built environments influencing physical activity	Thematic content analysis, categorisation of studies based on built environment factors
Maas and Attard [64]	2022	Malta	Policy document analysis, 22 semi-structured interviews with stakeholders, experts, and public/private operators	Thematic content analysis of policy measures and cycling promotion strategies
Zafri et al. [55]	2021	Bangladesh	Online questionnaire survey with 804 respondents across Bangladesh	Multinomial logistic regression models, descriptive analysis
Vineis et al. [78]	2021	Italy	Policy review commissioned by the Italian Ministry of Health, analysis of national health and environmental policies, urban planning, transport, and dietary interventions	Thematic policy analysis, evaluation of primary prevention strategies for non-communicable diseases and climate change mitigation
Chen et al. [69]	2022	China	Online survey with 606 respondents from Beijing, including data on travel behaviour, attitudes, and the built environment	Multinomial logistic regression models, descriptive statistics, and factor analysis
Canitez et al. [67]	2020	Turkey	Review and qualitative analysis of Istanbul’s urban transport policies and strategies	Thematic content analysis of policy documents and mobility trends
Vietinghoff [79]	2021	France	19 narrative and semi-structured interviews with policymakers, residents, and bike service providers in Grenoble	Thematic content analysis of qualitative interviews, intersectional framework
Chevalier and Charlemagne [80]	2020	China	Survey with over 400 responses from kindergartens in inner Shanghai, mapping perceived dangers and drawing routes to school	Thematic analysis of danger perception, Desire Lines Analysis, infrastructure assessment
Ramirez-Rubio et al. [81]	2019	Spain	Review of Health in All Policies (HiAP) implementation in multiple cities across Europe, Africa, and Latin America	Conceptual framework analysis linking SDGs, urban policies, environmental exposures, and health outcomes
Barbarossa [82]	2020	Italy	Analysis of urban mobility policies in 10 Italian metropolitan cities post-COVID-19	Comparative policy analysis on sustainable urban mobility transformations
Cirianni et al. [83]	2018	Italy	Review of Sustainable Urban Mobility Plans (SUMPs) from various European cities	Thematic content analysis, benchmarking of urban mobility policies
Gabrhel [84]	2019	Czech Republic	Survey of 1301 respondents in Litoměřice, part of the city’s Sustainable Urban Mobility Plan (SUMP)	Exploratory factor analysis, multinomial logistic regression, descriptive statistics
Mitullah et al. [85]	2017	Kenya	Case study on Nairobi’s walking and cycling infrastructure, travel behaviour, and policy frameworks	Qualitative thematic analysis, policy review, and assessment of non-motorised transport integration
Sietchiping et al. [86]	2012	Kenya	Review of urban mobility policies, observations, and case study comparisons across sub-Saharan African cities	Thematic content analysis, comparative urban transport policy evaluation
Pan [87]	2011	China	Review of urban travel policies, case studies of Chinese cities	Comparative policy analysis, transport planning evaluation
Ahmad and Oliveira [88]	2016	India	Urban mobility determinants	Sustainable and inclusive urban transportation
Soltani [89]	2017	Iran	Review of urban transport policies, land use patterns, and case studies in major Iranian cities	Thematic analysis of urban mobility trends, evaluation of transport sustainability challenges
Lawson et al. [90]	2013	Ireland	Analysis of Irish census data (2006) on non-motorised commuter journeys across five major cities	Logistic regression modelling, thematic analysis of urban mobility patterns
Lopez-Escolano et al. [60]	2017	Spain	Case study of cycling infrastructure in Zaragoza, spatial analysis using GIS, accessibility assessments	Thematic content analysis, mapping of cycling infrastructure, assessment of accessibility to bike lanes and shared bicycles
Stevenson et al. [91]	2016	Australia	Health and travel surveys, urban design modelling	Statistical modelling, scenario analysis using health impact assessment frameworks
Hensley et al. [92]	2014	Australia	Systematic review of 22 studies on path dependence in urban and transport planning	Thematic content analysis of literature on policy and planning frameworks
Useche et al. [93]	2024	Russia	Online survey of young cyclists in Russian cities, observational study of cycling behaviour	Statistical analysis, correlation analysis among rider characteristics, behaviour, and safety incidents
Adinarayana et al. [94]	2024	India	Official reports on road infrastructure conditions, historical accident data, demographic data from surveys and government databases	Official reports on road infrastructure conditions, historical accident data, demographic data from surveys and government databases
Distefano and Leonardi [95]	2023	Italy	Online survey with 562 participants, field observations, and urban walking strategies	Factorial Analysis of Variance (ANOVA) for understanding pedestrian satisfaction
Panahi et al. [96]	2022	Iran	Semi-structured interviews with 66 older adults (purposeful sampling)	Thematic analysis [53] (p. 6) using Atlas.ti
Basil and Nyachieo [97]	2023	Kenya	Descriptive survey with 137 respondents (convenience sampling)	Quantitative analysis
Razak [73]	2022	Nigeria	Mixed-methods: archival records, spatial analysis, direct observation, and structured interviews (150 respondents)	Triangulation of qualitative and quantitative data
Schneider et al. [98]	2022	USA	Open-ended responses from the 2020 Milwaukee Safe and Healthy Streets survey	Binary logistic models
Ullmann et al. [72]	2022	Germany	Virtual reality simulation with 93 participants cycling through a virtual urban environment	Statistical analysis using t-tests, Mann–Whitney U tests, and Wilcoxon signed-rank tests
Gouais et al. [99]	2023	Jamaica	Semi-structured interviews with 10 expert stakeholders (urban planners, public health professionals, and civil society representatives)	Thematic analysis using NVivo software
Castro et al. [71]	2022	Brazil	Space Syntax Modelling, cyclist flow counts, accident records, and infrastructure mapping	Spatial and statistical analysis using Space Syntax metrics (Normalised Choice, NACH)
Scorza and Fortunato [100]	2021	Italy	Geomorphological analysis, Space Syntax Analysis, Place Syntax Analysis	Morphological and syntactic analysis of urban space
Roberts [101]	2020	Canada	Historical research using archival sources, media articles, and semi-structured interviews with cycling advocates and stakeholders	Qualitative historical analysis and thematic interpretation
Fortunato et al. [61]	2019	Italy	Geospatial analysis using GIS-based territorial assessment and urban morphology evaluation	Spatial analysis and territorial impact assessment
Götschi et al. [102]	2017	Switzerland	Systematic literature review of conceptual frameworks in active travel research	Synthesis and development of a comprehensive conceptual framework
Nordfjærn et al. [103]	2019	Norway	Cross-sectional survey with 441 university students in Trondheim	Linear mixed model analysis
Aldred [104]	2014	UK	Mixed methods: survey research, ethnography, policy analysis	Thematic analysis and policy evaluation
Macmillan et al. [105]	2014	New Zealand	System dynamics modelling using stakeholder interviews, workshops, and secondary data sources	Simulation modelling of policy impacts on cycling trends, health, and environmental outcomes
Khayesi et al. [106]	2010	Kenya	Archival research and policy analysis on urban transport planning for pedestrians, cyclists, and street vendors	Critical geographical analysis of transport policy and urban planning
Nguyen et al. [75]	2015	Singapore	Before-and-after study using cyclist counts, field observations, and perception surveys	Statistical analysis using paired sample t-tests and GIS-based spatial analysis
Mueller et al. [107]	2015	Spain	Systematic review of health impact assessment studies on active transportation	Comparative risk assessment and cost–benefit analysis
Humberto et al. [108]	2021	Brazil	Longitudinal intervention study with surveys of caregivers (pre, post, follow-up) and sentiment analysis of children’s statements	Difference-in-differences and time-series analysis
Roaf et al. [58]	2024	UK	Systematic review of 78 studies on interventions increasing active travel	Narrative synthesis and methodological quality assessment using the Mixed Methods Appraisal Tool
Kim et al. [109]	2023	South Korea	Online survey with 659 respondents (cyclists and walkers) using quota sampling	Partial least squares structural equation modelling (PLS-SEM), multi-group analysis (MGA), fuzzy set Qualitative Comparative Analysis (fsQCA)
Biehl et al. [54]	2018	USA	Online survey with 914 respondents (Amazon MTurk panel)	Ordinal logistic regression, factor analysis

3. Results of the Systematic Review

Upon reviewing 56 peer-reviewed studies, the systematic review found that only those with clear methods, adequate sample sizes, transparency, scholarly impact, and direct relevance to walking and cycling adoption were included, ensuring a rigorous and focused synthesis of key barriers, facilitators, and policy measures. Researchers conducted studies across various geographic areas and applied different methods to explore themes ranging from urban planning to engineering while obtaining insights related to public health, transportation policy, and behavioural science. The systematic literature review incorporates various methodological approaches, including quantitative surveys, qualitative interviews, systematic reviews, policy analyses, and simulation-based modelling. Research contributions from Europe, North America, Asia, Africa, and Oceania enable comprehensive global insights into walking and cycling behaviours. This study adopts the COM-B framework, as shown in Figure 4, which illustrates the interrelationship among capability, opportunity, and motivation in shaping behaviour (adapted from Michie et al., 2011 [29]).

3.1. Barriers to User Acceptance of Walking and Cycling

Adopting walking and cycling as dominant transport modes is often hindered by multiple barriers that exist across geographic locations and social and structural dimensions. The study outlines three main barriers and 14 sub-barriers to walking and cycling according to the COM-B framework, which includes Capability, Opportunity, and Motivation. An assessment of 56 scholarly articles reveals that infrastructural deficiencies, safety concerns, personal limitations, psychological barriers, socio-cultural perceptions, and policy gaps collectively represent the primary obstacles to active travel adoption. Several barriers to walking and cycling interact and reinforce one another, ultimately leading individuals to avoid these activities. This section presents a detailed examination of barriers cross-referenced with pertinent studies, and

Table 2. Barriers to walking and cycling adoption.

Main Barrier	Sub-Barrier	COM-B Category	Studies Reporting	Key Results
Infrastructure and Environmental	Lack of dedicated cycling infrastructure	Opportunity	Nguyen et al. [75]; Lowe et al., 2024 [1]; D’Apuzzo et al. [2]; Nuuyandja et al. [3]; Mitullah et al. [4]; Macmillan et al. [5]; Basil & Nyachieo [6]; Lopez-Escolano et al. [7]; Gouais et al. [8].	Poor cycling infrastructure increases injury risk and discourages cycling.
	Lack of pedestrian-friendly environments	Opportunity	Rosario et al. [9]; Attard et al. [10]; Maas & Attard [11]; Zafri et al. [12]; Castro et al. [13]; Distefano & Leonardi [14]; Cirianni et al. [15]; Gotschi et al., [16].	Poorly maintained sidewalks and crossings deter walking.
	Traffic safety concerns	Motivation	Kim et al. [17]; Ramirez-Rubio et al. [18]; Roaf et al. [19]; Schneider et al. [20].	Fear of accidents due to speeding vehicles reduces willingness to cycle.
	Lack of bicycle parking facilities	Opportunity	Macmillan et al. [5]; Nguyen et al. [21]; Soliz et al. [22].	The lack of secure bicycle parking discourages cyclists.
	Harsh weather conditions	Opportunity	Vietinghoff [23]; Stevenson et al. [24]; Nordfjærn et al. [25].	Heat, cold, and rainfall affect the willingness to walk or cycle.
	Poor urban planning and land use policies	Opportunity	Soliz et al. [22]; Barbarossa [82]; Maas & Attard [11]; Hafenrichter et al. [27]; Biehl et al. [28]; Gabrhel [29]; v. d. Salm et al. [30]; Scorza & Fortunato [31].	Poor land use prioritisation reduces accessibility for pedestrians and cyclists.
Personal and Psychological	Perceived lack of physical ability	Capability	Humberto et al. [32]; Maas & Attard [11]; Aldred [33]; Panahi et al. [34]; Soltani [35]; Khayesi et al. [36]; Roberts [37].	People with low fitness levels or mobility impairments struggle to adopt active travel.
	Distance and travel time constraints	Capability	Attard et al. [10]; Zafri et al. [12]; Papageorgiou & Tsappi [38]; Nguyen et al. [21].	Long commuting distances make active travel infeasible.
Socio-Cultural and Policy-Related	Cultural perceptions and social stigma	Motivation	Zafri et al. [12]; Chevalier & Charlemagne [39]; Canitez et al. [40]; Vietinghoff [23].	Cycling and walking are perceived as ‘lower-status’ modes in some societies.
	Limited government support	Opportunity	Kazmi et al. [41]; Lowe et al. [1]; Soliz et al. [22]; Ullmann et al. [42]; Lawson et al. [43]; Hensley et al. [44]; Chen et al. [45]; Fortunato et al. [46].	Weak policy frameworks and a lack of urban mobility plans discourage active transport.
	Crime and personal security concerns	Motivation	Nuuyandja et al. [3]; Yin et al. [47]; Ahmad & Oliveira [48]; Adinarayana et al. [49]; Useche et al. [50]; Sietchiping et al. [51]; Pan [52].	Fear of theft, harassment, and attacks discourages cycling and walking.
	Limited integration with public transport	Opportunity	Papageorgiou & Tsappi [38]; Nguyen et al. [75]; Hoyer-Kruse et al. [53]; Vineis et al. [54]; Razak [55].	Limited connections between cycling/walking routes and public transport discourage adoption.
	Inadequate financial incentives	Opportunity	Maas & Attard [11]; Lowe et al. [1]; Papageorgiou & Tsappi [38].	The lack of subsidies or tax incentives fails to make cycling and walking financially attractive.
	Lack of awareness about the benefits of active travel	Motivation	Mueller et al. [56]; Ramirez-Rubio et al. [18]; Chevalier & Charlemagne [39].	Limited public awareness prevents behaviour change towards active travel.

summarises the barriers along with their COM-B classifications and corresponding studies.

3.1.1. Infrastructure and Environmental Barriers

Poorly developed infrastructure and environmental limitations significantly hinder the widespread adoption of walking and cycling. Such conditions make these active travel modes less safe and less convenient. Cyclists face one of the most common barriers in the absence of exclusive cycling infrastructure since they must navigate roads with fast-moving vehicles, which heightens collision hazards and reduces participation rates [57,60,66,75,97,99]. Research from China, Italy, and Singapore shows that urban areas with seamless cycling networks have much higher cycling usage rates than those with broken or absent cycling facilities [66,75]. Many low-income regions, such as Ghana and Kenya, lack cycling infrastructure, making this mode of transportation unsafe and impractical [68,85]. The absence of pedestrian-friendly environments discourages people from choosing walking as a practical transport option. Urban areas in Australia and Malta face challenges such as decaying pedestrian pathways, inadequate pedestrian crossings, and car-focused urban spaces that create obstacles to safe pedestrian travel [71,74,76,83,95,102]. A study in Southern Europe reveals that pedestrian experiences become unpleasant and dangerous because of narrow sidewalks, parked cars obstructing paths, and limited crossing opportunities [64].

The absence of sufficient speed controls and protective measures for pedestrians and cyclists intensifies traffic safety issues in numerous urban areas. Research conducted in South Korea, the USA, and the UK reveals that road safety fears represent the main reason people avoid cycling and walking, as aggressive driving behaviour and weak traffic law enforcement discourage these activities [58,98,109]. Research conducted in Spain indicates that the country continues to experience high fatality rates among pedestrians and cyclists, contributing to public concerns about the safety of non-motorised travel [81]. Many urban regions do not offer secure cycle storage solutions, which represents a significant infrastructural obstacle to expanding cycle usage [62,75,105]. Business districts exhibit a particular problem with bicycle theft, which acts as a barrier for commuters who wish to cycle to work [62]. Harsh weather conditions remain a major obstacle that significantly affects how often people use active transportation methods. Research conducted in France and Australia demonstrated that extreme weather events, including heatwaves, heavy rain, and extreme cold, lead to decreased feasibility of walking and cycling [10,79]. The practice of cycling in Scandinavian countries experiences seasonality because icy road conditions render it unsafe throughout the winter period [103]. The difficulties of active transportation are exacerbated because urban planning and land use policies focus on motorised travel, resulting in inadequate infrastructure support for pedestrians and cyclists. Research from North America and Italy demonstrates that the urban design of many cities prioritises car usage, while mixed-use development remains scarce, resulting in limited accessibility for walk-and-cycle [54,56,62,70,82,84,100]. The barriers persist in preventing active travel unless urban design priorities change.

3.1.2. Personal and Psychological Barriers

In addition to infrastructure challenges, personal and psychological obstacles significantly limit people’s adoption of walking and cycling as transportation modes. The perception of insufficient physical ability represents a significant barrier to the adoption of walking and cycling among older adults and individuals with mobility impairments [104,108]. People often see cycling as a demanding physical activity that requires endurance, which they believe they do not possess for regular commuting [89,101,106,108]. A study in Iran showed that older adults are less likely to choose active travel methods because they worry about physical strain and safety risks [96]. Within cultures that view cycling primarily as a sport, people who do not consider themselves athletic feel unable to use cycles for daily travel [104]. The lack of adapted pedestrian and cycling infrastructure makes participation difficult for people with disabilities who face additional barriers when sidewalks are poorly maintained or curb cuts and ramps are unavailable [108]. Residents of sprawling urban regions encounter a major barrier to active transportation through extended distances and lengthy travel times. Urban sprawl creates difficulties for people who wish to engage in active commuting, as the long distances involved make walking and cycling impractical options [55,76]. Research conducted in Malta and Bangladesh found that people who travel more than 10 kilometres daily tend to prefer motorised transportation options over walking or cycling [55,76]. Research from Singapore and Cyprus reveals that compact urban planning alongside mixed land-use strategies assists in overcoming travel barriers by shortening commute distances while supporting pedestrian-friendly environments [63,75]. City planning measures that focus on reducing travel distances and enhancing last-mile connections have the potential to greatly boost active travel participation rates.

3.1.3. Socio-Cultural and Policy-Related Barriers

Socio-cultural factors, policy-related hurdles, and physical and psychological barriers create additional discouragement for walking and cycling. Cultural perceptions and social stigma around active travel remain among the most challenging obstacles to overcome. The perception of cycling and walking as activities associated with a lower socioeconomic status creates negative connotations that discourage people from participating in these forms of active travel [55,67]. Studies from Turkey and Bangladesh indicate that middle-income people tend to prefer car use, viewing cycling primarily as a mode of transport for those who cannot afford more desirable alternatives [55,67]. French cycling cities with a long-standing cycle culture encountered resistance as some demographic groups found bike-sharing systems inconvenient and socially unacceptable [79]. Limited government support for the development of active transport facilities, along with the insufficient promotion of incentives and awareness campaigns, represents another significant constraint. Local governments allocate funds primarily towards motorised transportation projects while neglecting walking and cycling infrastructure development [61,62,65,66,69,72,90,92]. Initiatives designed to boost cycling and pedestrian accessibility fail without substantial policy support. Cities that do not provide cycling subsidies or pedestrianisation initiatives and lack active transport promotion programs demonstrate the most significant institutional support deficiencies [65,66].

The threat of crime and concerns about personal security deter many individuals from participating in active travel, particularly in low-income areas where the risks of harassment, theft, or violent crime are high [59,68,86,87,88,93,94]. Research conducted in Ghana and China reveals that women and older adults demonstrate a strong preference for motorised transportation, mainly in response to unsafe conditions that discourage walking or cycling to avoid crime [59,68]. Active travel adoption remains low because inadequate integration between public transportation and other transit modes creates last-mile connectivity problems, which prevent commuters from walking or cycling to transit stops [63,73,75,77,78]. Public transport users avoid active travel for the first and last segments of their journeys in cities where transit hubs lack connections to walking and biking networks [63]. The absence of sufficient financial incentives creates a major barrier because many governments do not offer tax breaks and subsidies or invest in infrastructure to promote walking and cycling [64,66]. Australian studies demonstrate that targeted financial incentives substantially increase cycling rates [66]. A limited understanding of active travel benefits continues to hinder its broad adoption. The lack of knowledge about walking and cycling benefits in health, finance, and environmental areas leads to reduced participation interest among many people [80,81,107]. Research conducted in Spain demonstrates that parental opposition to children cycling stems from misconceptions about safety despite the well-established benefits of active commuting [80]. Without awareness campaigns and educational initiatives, the public will unlikely change its views on active travel, resulting in continued reliance on cars and stagnant cycling numbers.

3.2. Facilitators to User Acceptance of Walking and Cycling

Active travel adoption through walking and cycling heavily depends on multiple facilitators that improve ease of use and safety while making active travel a more attractive option. These facilitators of walking and cycling as main transportation modes are grouped into three categories: Infrastructure and Environmental Factors, Personal and Psychological Factors, and Socio-Cultural and Policy-Related Factors. All three categories function as essential components that eliminate obstacles and establish motivations that make walking and cycling competitive options compared to motorised transport. The analysis of 56 research studies enables this section to critically investigate how cities globally have utilised these facilitators to advance active travel. This section presents a detailed examination of facilitators cross-referenced with pertinent studies, and

Table 3. Facilitators to walking and cycling adoption.

Main Facilitator	Sub-Facilitator	COM-B Category	Studies Reporting	Key Results
Infrastructure and Environmental	Dedicated cycling lanes	Opportunity	Nguyen et al. [75]; Yin et al. [59]; Ullmann et al. [72]; Lowe et al. [66]; Nordfjærn et al. [103]; Lopez-Escolano et al. [60]; Gouais et al. [99]; Nuuyandja et al. [68]; Soltani [89].	Infrastructure investment improves safety and cycling rates.
	Walkable urban environments	Opportunity	Hensley et al. [92]; Maas & Attard [64]; Barbarossa [82]; Soliz et al. [62].	Cities designed for pedestrians encourage more walking.
	Traffic-calming measures (e.g., speed bumps, lower speed limits)	Opportunity	Schneider et al. [98]; Kim et al. [109]; Vietinghoff [79]; Roberts [101]; Mitullah et al. [85]; Ahmad & Oliveira [88]; Adinarayana et al. [94]; Useche et al. [93]	Slower vehicle speeds increase perceived safety and encourage active travel.
	Access to bicycle parking and storage facilities	Opportunity	Macmillan et al. [105]; Soliz et al. [62]; Vietinghoff [79]; Barbarossa [82]; Sietchiping et al. [86]; Pan [87]; Lawson et al. [90]; Fortunato et al. [61].	Secure and convenient parking options increase cycling uptake.
	Urban greening and shade provision	Opportunity	Barbarossa [82]; Vietinghoff [79]; Høyer-Kruse et al. [77]; Basil & Nyachieo [97].	More comfortable walking and cycling environments encourage greater participation.
	Climate adaptation strategies for active travel (e.g., weather-proof infrastructure)	Opportunity	Vietinghoff [79]; Stevenson et al. [10]; Basil & Nyachieo [97]; Barbarossa [82]; Castro et al. [71]; Cirianni et al. [83]; Gotschi et al. [102]; Biehl et al. [54]; Gabrhel [84].	Investments in weather-resistant infrastructure promote year-round walking and cycling.
Personal and Psychological	Bike-sharing and e-bikes	Capability	Roaf et al. [58]; Lowe et al. [66]; Chen et al. [69]; v. d. Salm et al. [56]; Zafri et al. [55].	Shared bikes and e-bikes improve accessibility and affordability.
	Technological advancements (e.g., cycling apps, real-time route planning)	Capability	Kazmi et al. [65]; Lowe et al. [66]; D’Apuzzo et al. [57]; Macmillan et al. [105]; v. d. Salm et al. [56]; Scorza & Fortunato [100]; Canitez et al. [67]; Aldred [104].	Digital tools improve navigation, safety, and confidence in active travel.
	Health awareness campaigns	Motivation	Mueller et al. [107]; Ramirez-Rubio et al. [81]; Chevalier & Charlemagne [80]; Humberto et al. [108]; Panahi et al. [96].	People walk/cycle more when aware of the health benefits.
	Safe routes for children and elderly pedestrians	Opportunity	Ramirez-Rubio et al. [81]; Humberto et al. [108]; Panahi et al. [96]; Chevalier & Charlemagne [80].	Safe and accessible paths encourage walking and cycling among vulnerable populations.
Socio-Cultural and Policy-Related	Social norms and community influence	Motivation	Khayesi et al. [106]; Nguyen et al. [75]; Høyer-Kruse et al. [77]; Panahi et al. [96]; Basil & Nyachieo [97].	Walking and cycling are more likely in areas where they are socially accepted.
	Financial incentives (e.g., subsidies, tax deductions for cyclists)	Opportunity	Papageorgiou & Tsappi [63]; Maas & Attard [64]; Lowe et al. [66]; Attard et al. (2023); Vineis et al. [78]; Razak [73]; Rosario et al. [74].	Financial incentives encourage adoption.
	Employer-based cycling incentives	Opportunity	Maas & Attard [64]; Lowe et al. [66]; Schneider et al. [98]; Hafenrichter et al. [70].	Workplace support and cycle-to-work programs promote active commuting.
	Public transport integration	Opportunity	Macmillan et al. [105]; Nguyen et al. [75]; Distefano & Leonardi [95]; Ramirez-Rubio et al. [81].	Seamless connections improve usability of active transport modes.

summarises the facilitators along with their COM-B classifications and corresponding studies.

3.2.1. Infrastructure and Environmental Facilitators

The design of urban spaces and constructed surroundings plays a major role in determining people’s decisions to walk or cycle. Research has shown that dedicated cycling lanes serve as one of the primary drivers that boost cycling adoption among residents. Investing in a superior separated cycling infrastructure leads to substantial growth in cycling safety and increases participation rates [59,60,68,72,75,89,99,103]. The construction of exclusive cycling routes in Beijing and Melbourne led to increased cycling trips due to the decreased risk of vehicle collisions and enhanced cyclist safety [66,82]. Walkable urban environments attract pedestrian activity by properly maintaining sidewalks and establishing pedestrian-priority zones and accessible crossings. Studies conducted in Melbourne and North America demonstrate that pedestrian-friendly urban designs stimulate walking habits, reduce car dependency, and contribute to the creation of vibrant street environments [62,64,92]. Traffic-calming strategies, including speed bumps and pedestrian-priority crossings, serve as essential infrastructure components to slow down vehicles in residential and commercial zones. Research conducted in European and Asian cities demonstrates that reduced vehicle speeds enhance confidence among pedestrians and cyclists, thus making walking and cycling more attractive options [85,88,93,94,98,101,109]. Netherlands and France cities with 30 km/h zones experienced major transitions to cycling as their primary transportation option [76,79].

Bicycle parking and storage facilities prove essential for supporting the cycling infrastructure. Reliable bicycle parking facilities at transit hubs and workplaces minimise theft risks and offer secure storage options to users [61,62,86,87,90,105]. Urban centres in Europe that have implemented additional bicycle parking facilities near train stations and commercial districts have experienced a higher cycling frequency [14,54,71,79,83,84,96]. Urban greening and its accompanying shade offerings make active travel more appealing to people. The pedestrian paths with tree-lined surroundings, shaded cycle lanes, and urban green spaces offer protection against harsh weather, making walking and cycling more comfortable [79,82]. Research from Latin America and Africa demonstrates that urban designs with added green elements enhance pedestrian and cycling activity by improving the air quality and increasing comfort levels [77,81,97]. Climate adaptation solutions for active travel, including covered sidewalks and storm-resistant walking paths, have proven crucial for maintaining pedestrian and cycling mobility throughout the year [79,91]. Cities like Montreal and Copenhagen, which implemented climate adaptation measures, have succeeded in boosting active travel during bad weather [82,96,97].

3.2.2. Personal and Psychological Facilitators

While infrastructure matters, individual motivations and psychological drivers play a crucial role in influencing walking and cycling practices. Bike-sharing programs and electric bicycles serve as key facilitators by enabling expanded cycling access in areas with challenging terrains or extensive distances. The bike-sharing programs in Beijing, Amsterdam, and New York have eliminated expense obstacles while making cycling more convenient and enabling people to ride cycles without having to own them [58,66,71]. The introduction of e-bikes has widened access to cycling, which now serves as a practical transportation solution for older people and individuals with limited physical capabilities [55,56,70]. Electric mobility innovations have achieved significant success in Asian and European cities due to heavy subsidies for electric transportation solutions. The usability of active travel options has improved significantly due to technological advancements, including real-time cycling apps, route optimisation tools, and digital wayfinding systems [56,57,65,67,100,104]. Studies show that smart city technologies, including real-time traffic information, intelligent navigation systems, and cycle availability tracking, enhance route efficiency while reducing perceived cycling barriers [58,105]. Education about the benefits of physical activity through health awareness campaigns results in meaningful increases in the adoption of walking and cycling. Research shows that public health programs connecting active travel to cardiovascular health, obesity prevention, and mental well-being have significantly increased active travel participation [80,81,107]. The provision of protected walkways for young students and older pedestrians is a key factor in promoting active transportation among these vulnerable groups. The development of school walking pathways, combined with pedestrian infrastructure designed for different age groups and the provision of safe crossing areas, has enhanced mobility for children and older adults, resulting in increased walking frequencies [81,96,108]. Studies from Shanghai and Bangladesh demonstrate that providing safe walking paths for schoolchildren and older adults fosters better social interaction and mobility [55,80].

3.2.3. Socio-Cultural and Policy-Related Facilitators

Walking and cycling habits are significantly influenced by both policy decisions and cultural factors. Community norms and social influence strongly impact travel behaviours, as individuals are more likely to engage in active transportation when it is accepted and supported within their communities [75,77,106]. The studies conducted in African, Asian, and European environments demonstrate that active travel participation rates rise when strong cycling cultures and supportive social networks are present [96,97]. Studies demonstrate that financial motivators like bicycle purchase subsidies along with tax advantages, serve as powerful tools for increasing cycling participation [63,64,73,74,78]. Cities that offer direct financial support for cycle purchases or tax deductions for cyclists observe higher rates of cycling adoption because reduced costs make cycles more accessible [66,70,76]. Workplace cycling incentives, such as cycle-to-work programs and corporate cycle fleets, lead to higher commuter cycling rates [64,70,98]. Public transport integration enables active commuting by developing multimodal options [66,75,81,95,105]. Studies demonstrate that direct connections between the cycling infrastructure and transit hubs increase the commuter use of cycles for first and last-mile travel. Paris and London demonstrate that integrating bike-sharing with train and bus services results in a significant growth in cycling use [96,108].

3.3. Interventions to Facilitate Walking and Cycling Adoption

Multiple interventions have been designed and put into practice in diverse urban areas to eliminate obstacles to active travel while boosting supportive elements. The planned interventions focus on the building infrastructure while integrating behavioural change strategies, policy reforms, and financial incentives to establish walking and cycling as practical, safe, and appealing modes of transport. This section presents a thorough evaluation of successful global active travel interventions based on the findings from 56 studies and discusses their impact on sustainable mobility development.

3.3.1. Infrastructure-Based Interventions

The establishment of dedicated cycling and pedestrian pathways stands as an essential action to increase walking and cycling. Research demonstrates that creating dedicated cycle lanes and pedestrian-friendly spaces paired with better sidewalks leads to safer and more inviting active transportation options. The implementation of an expanded cycling infrastructure within Beijing’s urban mobility strategy caused cycling participation rates to increase [69]. Both Melbourne and London recorded higher walking rates after their city centres underwent pedestrianisation projects and redesigns that favoured non-motorised mobility [62,66]. According to recent research, traffic-calming strategies like speed reductions and pedestrian-first crossings make walking and cycling both safer and more attractive [64,98]. Amsterdam and Copenhagen have created 30 km/h speed zones and expanded areas designated for shared mobility, resulting in decreased car use and more people choosing active travel [76,79]. The implementation of these measures leads to better air quality and lower urban noise levels, which strengthens the advantages of walking and cycling transportation methods. Bike-sharing systems, alongside e-bike subsidies, have played an essential role in increasing cycling rates within urban areas. The integration of dockless bike-sharing systems in Paris, New York, and Beijing has allowed people to find bicycles with ease while maintaining low costs [58,66]. E-bikes have expanded quickly to overcome physical limitations and distance barriers, thereby enabling older people and those with limited fitness to cycle more easily [55,56,70]. Research demonstrates that the combination of public transport systems with cycling and walking leads to successful multi-modal commuting [75,81,105]. Cities including Berlin, Tokyo, and Montreal experienced improved cycling rates by introducing bicycle parking at transit stations, allowing bicycles on public transit, and providing last-mile connectivity solutions.

3.3.2. Behavioural and Social Interventions

Behavioural change interventions serve as essential tools to promote walking and cycling beyond the development of infrastructure. Several health awareness programs have been established to teach people about the health advantages of active travelling methods [80,81,107]. Latin American public health programs that educate about walking and cycling benefits for cardiovascular health have successfully driven active travel participation rates up significantly [77,97]. Community-led programs and social norm reinforcement serve as effective strategies to promote walking and cycling. Cities that build strong cycling and pedestrian cultures through events like Car-Free Days and Open Streets experience higher long-term rates of active transportation participation. Bogotá’s weekly Ciclovía event, which shuts down major roads to cars every Sunday, serves as an excellent model for encouraging active transportation while building community connections [81,96]. Employer-based cycling incentives represent an effective behavioural intervention. Companies that provide commuters with financial rewards, secure bicycle storage, and shower facilities achieve higher cycling participation rates among employees [64,70,98]. Germany and the Netherlands introduced tax incentives for cycle-to-work schemes, resulting in increased rates of bicycle commuting [62,95]. The protection of vulnerable road users represents an essential social strategy. The implementation of programs that create safe walking paths for children and older adults through supervised school walking initiatives and senior-friendly pedestrian crossings has effectively boosted pedestrian activity [81,96,108]. The implementation of urban planning strategies that meet the requirements of children and older adults in Stockholm and Tokyo has resulted in improved active mobility rates [55,80].

3.3.3. Policy and Financial Interventions

Long-term walking and cycling adoption requires both policy initiatives and financial support mechanisms. Countries like France, Denmark, and Singapore have widely implemented direct financial incentives like cycling subsidies and tax deductions to promote active travel [63,64]. The combination of subsidised bicycle purchasing programs and tax benefits for frequent cyclists has resulted in increased adoption rates among lower-income groups [66,70,76]. Cities need to implement comprehensive active travel legislation along with urban mobility plans to drive effective policy interventions. Urban centres that have incorporated non-motorised transport policies into their urban planning frameworks show sustained growth in walking and cycling participation [67,82]. Cities like Istanbul and various African urban centres have experienced gradual yet consistent enhancements in active travel rates by implementing transport plans that prioritise cycling and walking [86,87,92]. Technological advancements introduced into active travel policy have supported increased adoption rates for walking and cycling. User confidence in trip planning and reduced planning uncertainty resulted from the introduction of real-time cycling apps and digital wayfinding systems in London, Berlin, and Singapore [57,58,65]. The effectiveness of mobile applications supplying real-time traffic updates alongside air quality information and ideal cycling routes has resulted in higher urban cycling rates [66,105]. The importance of climate adaptation strategies for maintaining walking and cycling during extreme weather conditions is now widely acknowledged. Urban centres that build weather-resistant infrastructure, including covered pathways and heat-resistant surfaces, see higher active travel activity throughout the year [79,91]. Through winter maintenance policies, both Montreal and Copenhagen ensure that their cycling lanes operate properly during snowfalls, allowing cyclists to stay active throughout cold months [82,97].

The success of walking and cycling interventions requires an integrated approach that combines infrastructure investment with behavioural change strategies, financial incentives, and supportive policy frameworks. Municipalities that focus on building dedicated cycling paths alongside traffic-calming measures and integrating public transit systems foster environments that support active travel. Health campaigns alongside employer incentives and safe pedestrian routes motivate people to make the transition from motorised transport to active travel. When cities combine these multiple strategies, they establish urban transport systems that serve everyone while promoting sustainability and health.

4. Findings of the Systematic Review

Research findings demonstrate that active travel has gained recognition as an essential factor in promoting sustainable urban mobility, improving public health, and supporting climate change adaptation. Even though walking and cycling have gained popularity as feasible transport options, the numerous existing obstacles call for well-coordinated solutions supported by data analysis and governmental policies. The analysis in this section examines current trends in walking and cycling adoption and proposes future research and policy pathways to guide active travel development.

4.1. Emerging Trends and Future Directions

4.1.1. The Shift Toward Integrated and Smart Mobility Solutions

Public transport systems and smart mobility platforms are increasingly integrating walking and cycling as key components of sustainable urban mobility. Research shows that active transportation increases when walking and cycling options are integrated with buses, trains, and micro-mobility services [75,81,105]. The design of bike-sharing systems in cities such as Singapore, Amsterdam, and Berlin aims to support public transit networks by enabling short-distance cycling journeys and facilitating transit use for extended travel. Mobility-as-a-service (MaaS) platforms that bring together real-time data and payment options make route planning easier, thereby supporting the transition to more sustainable transport modes [65]. Advancements in artificial intelligence (AI) and big data analytics enable governments and urban planners to implement real-time monitoring systems that enhance infrastructure for active travel. Machine learning applications allow cities to evaluate the cycling demand together with pedestrian flow and safety hazards, thereby supporting the dynamic adjustment of urban mobility strategies [56,57]. Cyclists and pedestrians can now obtain customised route suggestions from AI-powered apps, which consider conditions like air quality levels, traffic congestion, and terrain challenges [59,65]. Smart infrastructure investments must grow, and digital innovation must play a greater role in future active mobility planning.

4.1.2. The Rise of Equity-Centred Active Travel Policies

Active travel planning increasingly recognises the importance of incorporating equity and social justice principles. Research demonstrates that poor transportation infrastructure, alongside safety and cost issues, presents significant obstacles to walking and cycling for low-income communities and marginalised groups, including individuals with disabilities [62,79]. Transportation planning has traditionally favoured vehicular infrastructure development, thereby deepening mobility disparities among different social groups [64,67]. According to recent studies, cities are implementing inclusive mobility strategies focusing on low-income areas and gender-sensitive transport while creating accessible infrastructure [77,97]. Bogotá has concentrated its cycling initiatives on growing bike-sharing opportunities in underserved areas, while Paris has launched women-focused cycling programs, which enhance safety and accessibility for female riders [96]. Research needs to examine the global scalability of equitable policies to ensure active travel advantages reach all socioeconomic groups fairly.

4.1.3. The Expansion of Climate-Resilient Walking and Cycling Infrastructure

Urban areas are increasingly prioritising investments in adaptative transport systems to maintain walking and cycling accessibility under the extreme weather conditions brought about by climate change [10,79]. The study identifies multiple cities that have adopted heat-resistant sidewalk materials, flood-resistant bike lanes, and shaded pedestrian walkways to address the challenges of increasing temperatures and extreme weather conditions [81,82]. Cities located in hot climate regions, such as Dubai and Singapore, have been testing covered walkways and cooling systems to maintain pedestrian accessibility throughout all seasons [63,64]. Winter maintenance policies in northern European cities such as Copenhagen and Montreal maintain cycling lane functionality during heavy snowfall periods [96,108]. Future urban mobility policies need to combine climate resilience strategies to maintain the adaptability of active travel infrastructure against future climate changes.

4.1.4. The Role of Economic Incentives and Fiscal Policies in Promoting Active Travel

Financial incentives stand out as an effective method to boost the use of walking and cycling as transportation modes. Direct subsidies and tax benefits, alongside employer-based incentives, demonstrate effectiveness in increasing walking and cycling rates, especially where car ownership competes with active travel options [62,63]. The Netherlands and France have experienced ongoing growth in commuter cycling as a result of their bicycle tax deduction programs [64]. Employer-based initiatives that provide cash reimbursements together with free bicycle maintenance and cycle-to-work incentives have effectively motivated people to choose active transportation methods over short car journeys [98]. Although financial incentives yield positive results, institutional funding systems that support active travel investments on a long-term basis must be established. National mobility strategies must incorporate walking and cycling incentives, which require government support for sustained subsidies, grants, and infrastructure budgets [67,95]. Ongoing policy discussions need to address scaling financial strategies to developing regions beyond high-income countries, where infrastructure investments are still inadequate.

4.1.5. The Future of Active Travel Post-COVID-19

The COVID-19 pandemic led to a worldwide increase in walking and cycling because lockdown measures and public transport restrictions forced people to adopt more active travel methods [55,66]. In response to the increased active travel demand, Milan, Paris, and London quickly expanded cycling lanes and pedestrianised streets while reallocating road space for non-motorised transport [64,82]. Preliminary mobility policies during the pandemic achieved impressive results, which led to debates about their permanent adoption [80,96]. Urban areas need to preserve pandemic mobility achievements by integrating active transportation into city planning rules and transport investment strategies [81,108]. Future research needs to examine persistent changes in behaviour due to the pandemic and evaluate the long-term viability of increased walking and cycling trends after the pandemic ends.

4.2. Thematic Insights and Study Characteristics

An analysis of 56 research articles uncovered primary obstacles and enabling factors for walking and cycling adoption, which were classified into three main themes: infrastructure and environmental conditions, personal and psychological factors, and socio-cultural and policy-related influences. Figure 5 (barriers) and Figure 6 (facilitators) illustrate the distribution of these themes and identify the main challenges and supports found in global active travel research.

The identified barriers show that 45% are related to infrastructure and environmental factors, demonstrating ongoing deficiencies in cycling infrastructure and pedestrian-oriented urban planning, especially concerning safety [64,75,76]. The widespread presence of this barrier category demonstrates that numerous cities prioritise automobiles, thus making walking and biking inconvenient and dangerous for users. The combination of socio-cultural elements and policy-related issues stands as the second-largest barrier category at 39%, encompassing insufficient government backing, poor financial incentives, and unfavourable societal views about cycling and walking [62,65,66]. The analysis demonstrates how entrenched policy stagnation and prevailing societal mindsets form major obstacles to active travel implementation. The personal and psychological category makes up 16% of barriers and deals with individual perceptions about physical capability limitations alongside concerns about crime risk and distance parameters [79,81]. Despite being the least common category, its power should be recognised because perceived risks and physical constraints heavily influence people’s decisions against active travel.

The analysis revealed several factors that encourage the adoption of walking and cycling. The research showed that infrastructure and environmental facilitators represent the most dominant category, accounting for 51% of the findings, and emphasise the importance of dedicated cycling lanes, enhanced walkability, and urban greening initiatives [59,75,92]. The research shows that building physical infrastructure leads to increased active travel, as it is essential for urban planning that supports non-motorised transportation methods. The second-largest facilitator category at 25% consists of socio-cultural and policy-related initiatives that include public-transport-integration strategies, financial incentives, and workplace cycling programs [63,64]. The implementation of these interventions decreases financial and organisational obstacles to active travel, thereby turning it into an accessible and appealing choice for a wide range of people. Personal and psychological facilitators represent 24% of identified influences, supported by health campaigns, social norms, and technological tools, such as cycling apps [81,107]. This high percentage demonstrates that behavioural interventions and digital tools serve as key motivators for people to choose active travel.

A comparison of Figure 5 and Figure 6 reveals that infrastructure and environmental factors control both barriers and facilitators to active travel, indicating that urban design and investment choices play a critical role in shaping active travel adoption. The research results demonstrate that substandard infrastructure forms the primary obstacle to active travel, while upgraded infrastructure is the top facilitator. This underlines the need for cities to focus on developing pedestrian and cycling-friendly environments [64,75]. The greater number of socio-cultural and policy-related barriers than facilitators (39% vs. 25%) demonstrates the underuse of policy reforms and financial incentives to encourage active travel. The comparable proportions of personal and psychological barriers (16%) and facilitators (24%) indicate that behavioural interventions, public awareness initiatives, and community programs could effectively shape mobility decisions. The insights suggest that future interventions require a holistic strategy that integrates infrastructure enhancements with policy incentives and behavioural nudges to build a walking and cycling-friendly culture. Policymakers and urban planners can develop transport systems that support inclusivity and sustainability while promoting public health by removing major obstacles and strengthening existing positive factors.

5. Discussion of the Approach and Limitations

This study systematically identifies key barriers and facilitators of walking and cycling through a global systematic review and thematic analysis using the COM-B model. The research employs a systematic selection method, which chooses rigorous peer-reviewed studies but eliminates grey literature and opinion pieces that do not meet empirical standards. This methodology guarantees that the study findings rest on strong scientific validation instead of speculative or anecdotal evidence. Researchers utilise Scopus and Web of Science academic databases to gather a broad spectrum of studies about transport systems, sustainability initiatives, behavioural science research, and urban planning methods. The Boolean search strategy enables researchers to accurately identify relevant studies, which are then narrowed down to focus exclusively on urban transport initiatives involving the adoption of walking and cycling. Studies with clear research objectives and high analytical rigour are considered for inclusion only after passing through the multi-stage screening and quality assessment process, which includes title and abstract screening, full-text review, and final quality evaluation. The ultimate selection of 56 studies presents a methodologically rigorous array of research on active mobility adoption, incorporating qualitative, quantitative, and computational approaches.

This review demonstrates a significant strength by combining behavioural science research with a transport policy and infrastructure evaluation using the COM-B model. Traditional transport studies focus on infrastructure and policy interventions, whereas this research adopts a behaviourally informed perspective that incorporates psychological, cognitive, and environmental influences on mobility choices. The COM-B model offers a systematic approach to examining the impact of physical and psychological capabilities on walking and cycling patterns, the role of external environmental opportunities, such as infrastructure and policies, in facilitating or restricting active travel, and the influence of personal motivation factors, including risk perceptions and social norms, on mobility choices. The approach facilitates a comprehensive comprehension of how personal choices interact with urban planning and transportation regulations. The approach of merging behavioural science and transport research reveals how comprehensive interventions that target cultural, psychological, and economic aspects alongside the infrastructure can better promote walking and cycling adoption.

The systematic review showcases its major strength through the geographic diversity of its reviewed studies. Research from Europe, North America, Asia, Africa, and Oceania enables the study to evaluate active mobility issues and solutions through the lenses of both developed and developing countries. The extensive geographic range of the study enables researchers to identify both global best practices and unique regional barriers to walking and cycling. Research initiatives must expand to include more regional perspectives that address urban mobility challenges in low- and middle-income countries. The study’s advantages need to be balanced against its several methodological weaknesses. The study faces selection bias because it only included English-language publications. Future research should incorporate multilingual studies and grey literature to capture diverse perspectives on walking and cycling adoption. This research methodology maintains high standards of academic rigour but risks omitting critical information from non-academic organisations such as NGOs, local government bodies, and community groups that are essential in advancing walking and cycling initiatives within cities. A major weakness of many studies is their dependence on data collected from self-reports. Transport research commonly employs surveys, interviews, and stated preference studies, but these methods face challenges from social desirability bias alongside recall errors, which undermine the precision of reported walking and cycling behaviours and attitudes. The cross-sectional survey data used in many studies records mobility patterns during a one-time frame yet prevents researchers from analysing how walking and cycling habits evolve. The assessment of policy interventions’ long-term impact becomes difficult because short-term behavioural adaptations do not guarantee permanent changes in active transportation practices. The next phase of research needs to focus on longitudinal research methods that monitor walking and cycling behaviour changes over extended periods while implementing objective mobility tracking tools like GPS devices and smartphone applications to improve data reliability compared to self-reported travel behaviours.

The evaluation of policy effectiveness poses another significant limitation to this research. The review incorporates studies on cycling policies, pedestrian infrastructure projects, and financial incentives; however, most research methodologies depend on policy document reviews instead of empirical outcome evaluations. The real-world effectiveness of interventions to boost walking and cycling participation remains uncertain because these studies lack direct outcome measurements. Most policy research offers theoretical evaluations of best practices without delivering quantitative evidence on how much active travel increases after policy implementation. Without thorough impact assessments, researchers cannot reliably determine which interventions produce the best outcomes. Upcoming research must focus on empirical evaluations through a before-and-after analysis, randomised controlled trials, and natural experiments to determine how effective active travel policies are. The COM-B model serves as an effective tool for analysing active travel behaviour determinants but fails to capture the complex interplay between urban design elements and policy initiatives that affect individual activity choices. The model successfully organises factors into Capability, Opportunity, and Motivation, but it lacks the ability to fully represent non-linear interactions between these elements and the dynamic feedback loops that affect walking and cycling patterns over time. Initial cycling participation can rise when cycling infrastructure provides opportunity; however, sustaining behavioural change depends on developing habits and cultural adaptations alongside reinforcement factors that extend beyond the description of the COM-B model. Subsequent research should incorporate behavioural theories like habit theory, social practice theory, and system dynamic modelling to achieve a deeper understanding of active mobility adoption. This systematic review reveals important information about the factors that influence walking and cycling patterns along with policy interventions, yet it underscores crucial research gaps that require attention for enhanced and equitable active mobility frameworks. The results highlight several key research needs, including longitudinal studies, accurate mobility tracking systems, thorough policy assessments, and broader global research participation. Researchers need to implement multi-method approaches in future studies that integrate behavioural insights with urban planning analysis and transport policy evaluations to develop evidence-based strategies for sustainable and equitable active transportation systems globally.

6. Conclusions, Recommendations, and Forward Look

This review highlights the complex interplay of infrastructural, policy, and behavioural factors influencing active travel decisions. Evidence-based, multi-dimensional strategies that incorporate infrastructure investment, behaviour change programs, financial incentives, and supportive policy frameworks must be implemented to advance active mobility across global cities. According to research, infrastructural and environmental elements present major obstacles to the adoption of walking and cycling. Major deterrents that discourage people from choosing active travel include poorly developed or non-existent cycling lanes, unsafe pedestrian environments, limited bicycle parking facilities, and traffic safety concerns. Research conducted across various urban settings shows that the provision of well-designed and uninterrupted protected pathways for cyclists and pedestrians leads to significant increases in the rates of active transportation. The study reveals how policy measures and financial rewards influence travel behaviour patterns. Urban areas offering tax benefits for active travel and subsidies for bike purchases alongside congestion pricing and public transport connections see greater participation in active transport modes. Policy support for active mobility demonstrates inconsistency between cities, while transportation planning continues to prioritise motorised vehicles because of established car-oriented planning methods. The decision to engage in active travel is heavily influenced by behavioural and psychological elements. Potential users stay away from walking and cycling as regular modes of transportation due to fears of road accidents, a lack of confidence in their cycling abilities, social stigma associated with cycling, and concerns about crime and personal safety. The research demonstrates how public awareness campaigns and community-based programs can create positive changes in active travel perceptions. Municipalities that have promoted cycling education alongside workplace cycling incentives and cultural changes for active commuting experience positive behavioural shifts throughout time.

The COVID-19 pandemic marked a decisive moment for the expansion of active mobility trends, prompting many cities to rapidly develop the cycling infrastructure, pedestrianise streets, and reallocate space for non-motorised traffic. These changes temporarily boosted active travel usage; however, their permanency remains uncertain as several cities begin reverting to car-oriented development strategies. To sustain and build upon these gains, policymakers must institutionalise active mobility measures as permanent urban features. Future sustainable urban mobility depends on making walking and cycling practical, appealing, and easily accessible options for all residents. This study delivers practical recommendations derived from systematic review findings to eliminate barriers and boost facilitators for walking and cycling adoption. The study’s novel contributions lie in three areas. It offers a cross-context analysis of the behavioural, policy, and infrastructural factors influencing active travel. It demonstrates the added value of integrating behavioural models, particularly the COM-B framework, into urban mobility policy design. It also provides a globally relevant, action-oriented set of recommendations to guide sustainable transport planning and behavioural change strategies. The recommendations target key stakeholders, such as governments, urban planners, transport policymakers, businesses, and civil society organisations, to create an environment that promotes active mobility.

Table 4. Tiered recommendations for enhancing walking and cycling adoption across different city contexts (low-resource vs. high-resource settings).

Barrier Identified	Recommendation	Action	Responsible Entities	Examples of Implementation	Application in Low- vs. High-Income Settings
Infrastructure gaps	Expand and improve active mobility infrastructure	Develop protected cycling lanes, pedestrian-friendly streets, and secure bicycle parking. Ensure accessibility for all.	City governments, urban planners, transport departments	Amsterdam and Copenhagen’s extensive cycling infrastructure networks	Low-income: tactical urbanism (e.g., paint, bollards); donor funding. High-income: permanent protected lanes, AI-powered monitoring.
Safety concerns	Implement traffic-calming measures	Introduce lower speed limits, vehicle-free zones, and pedestrian-priority crossings in high-footfall areas.	Municipal authorities, traffic management agencies	London’s 20 mph speed limit zones and pedestrianised areas in Barcelona	Low-income: signage, speed bumps, community enforcement. High-income: smart intersections, speed cameras.
Lack of integration with public transport	Integrate cycling with public transport	Provide bicycle parking at transit hubs, allow bicycles on public transport, and develop bike-sharing programs linked to metro and bus networks.	Public transport authorities, city planners	Paris’ Vélib’ bike-sharing system integrated with metro stations	Low-income: low-cost bike racks at transit stops. High-income: app-based multimodal platforms.
Financial barriers	Financial incentives for active travel	Introduce tax credits, subsidies for bicycles and e-bikes, congestion charges for cars, and employer-supported cycling incentives.	National and local governments, businesses, employers	France’s e-bike subsidy program and Germany’s company-sponsored cycling initiatives	Low-income: NGO or donor-sponsored vouchers. High-income: tax relief, employer incentives, congestion pricing.
Safety enforcement	Address safety concerns and enhance enforcement	Strengthen road safety laws, increase penalties for reckless driving, and enhance enforcement of pedestrian and cyclist rights.	Law enforcement agencies, transport regulators	The Netherlands’ strict liability laws for cyclist safety	Low-income: mobile policing, community-led patrolling. High-income: automated enforcement, legal reforms.
Social stigma and behaviour	Launch public awareness and behaviour change campaigns	Implement cycling education programs, health-based active travel campaigns, and community-based walking and cycling promotion.	Public health departments, NGOs, cycling advocacy groups	Bogotá’s Ciclovía program promoting weekly car-free streets	Low-income: school campaigns, local events, radio. High-income: media campaigns, social marketing.
Car dependency	Ensure urban planning prioritises active mobility	Revise land-use policies to prioritise mixed-use developments, ensure walkability, and reduce car dependency.	National and local governments, urban planning agencies	Freiburg’s car-free neighbourhoods and Barcelona’s Superblocks initiative	Low-income: pilot mixed-use zoning. High-income: redesign entire districts for active modes.
Climate resilience	Make walking and cycling resilient to climate change	Invest in weather-resistant infrastructure (covered walkways, shaded cycling lanes, flood-resistant bike paths).	City governments, environmental agencies, climate policy groups	Singapore’s sheltered walkways and Montreal’s winterised bike lanes	Low-income: tree planting, drainage systems. High-income: heat-resistant surfaces, climate-adaptive design.
Limited community engagement	Support community-led initiatives	Provide funding and policy support for grassroots organisations advocating for active travel and community cycling/walking initiatives.	Local governments, NGOs, community groups	Portland’s neighbourhood greenway program	Low-income: microgrants and training. High-income: foundation partnerships and grant schemes.
Lack of data-driven policies	Monitor and evaluate active mobility policies	Establish data collection mechanisms to track walking and cycling rates, assess policy effectiveness, and make evidence-based adjustments.	Research institutions, municipal transport agencies	New York City’s cycling data-driven planning using automated counters	Low-income: manual counts, open-source tools. High-income: sensor networks, AI dashboards.

provides an overview of the recommendations by detailing essential actions and identifying responsible organisations.

The recommendations in Table 4 outline key actions for urban policymakers and planners. Numerous walking and cycling promotion initiatives do not possess long-term empirical evaluations for evaluating their lasting effects. The next stage of research requires before-and-after studies coupled with natural experiments and a longitudinal travel behaviour analysis to determine how infrastructure changes and policy incentives influence active travel patterns over time. Active mobility planning requires significant attention toward the integration of smart technologies. The potential of AI, big data, and real-time navigation apps to drive walking and cycling usage still requires a comprehensive study. Smart mobility platforms that combine cycling and walking with public transport systems help users plan routes better while reducing travel barriers and building trust in active transportation. Active mobility policies designed to serve low-income and marginalised groups should receive top priority to achieve equitable transportation solutions. Active mobility programs in urban environments mainly serve higher-income groups, whereas lower-income areas experience substantial obstacles to active travel because of inadequate infrastructure combined with crime risks and financial barriers. Transport planning of the future should aim at creating inclusive systems that specifically focus on vulnerable populations while working to bridge mobility equity divisions.

Scaling up climate-resilient active mobility strategies stands as another essential area for development. Cities should build climate-adaptive infrastructure, such as heat-resistant cycling lanes and storm-proof pedestrian pathways, as climate change brings more frequent heatwaves, heavy rainfall, and other extreme weather events, thereby necessitating resilient, all-weather active transport systems. To ensure global applicability, the study recognises that cities vary widely in their capacity to implement policy recommendations. Each recommendation is designed to be flexible and can be tailored to both low-resource and high-resource settings. For instance, cities in low-income countries may rely on donor funding, community-based enforcement, or tactical urbanism for cycling infrastructure, while high-income cities can invest in smart mobility integration, tax incentives, and AI-powered enforcement tools. The last column of Table 4 outlines how each action can be tailored to match different urban capacities, helping to ensure inclusive and realistic implementation. The study identifies obstacles that prevent people from choosing active travel but also shows how different cities are working to make walking and cycling standard transportation options. By combining evidence-based policies, behavioural insights, and inclusive planning, cities can make walking and cycling central to sustainable urban mobility. This study supports a shift towards low-carbon, health-enhancing, and equitable transport systems through active mobility.