Context-Aware QoE for Mobility-Driven Applications Through Dynamic Surveys

Abstract

The integration of outdoor smartphone applications with fitness trackers has introduced new opportunities and challenges for user interaction, particularly in mobility-driven activities. While these innovations offer significant benefits, they also pose challenges due to the many factors influencing the user’s quality of experience. Traditional methods of assessing user experiences, such as offline surveys and static questionnaires, often fail to capture the dynamic nature of outdoor activities. This research proposes a novel Quality of Experience (QoE) methodology for mobile applications to enhance the assessment of user experiences in cycling. Focusing on a use case in Blekinge, Sweden, where residents were encouraged to adopt cycling for daily transportation, we extracted land cover data and developed a server-side workflow for bicycle path segmentation. By incorporating dynamic surveys that adapt to users’ real-time experiences, we aim to generate a more accurate and context-aware dataset. This study makes several key contributions: First, it presents a scalable method for bicycle path segmentation; second, it demonstrates the utility and benefits of land cover data extraction; and finally, it evaluates the effectiveness of QoE influence factors through user surveys based on real-world cyclist feedback. This approach is expected to enhance the planning and development of cycling infrastructure by providing urban planners and stakeholders with valuable user insights using adaptable surveys based on route segmentation.

1. Introduction

The integration of outdoor smartphone applications with fitness trackers, such as smartwatches and fitness rings, has introduced novel use cases for user interaction with mobile devices and reshaped usage patterns. Consequently, this evolution has fostered mobility-driven activities, presenting new opportunities as well as challenges for stakeholders, including urban planners, healthcare providers, service providers, and application developers. There are significant benefits of promoting outdoor mobility and utilizing environmentally sustainable modes of transportation like the reduction of both stress levels and carbon emissions [1,2,3,4]. From the perspective of urban planners, the user experience of outdoor mobility has traditionally been assessed through offline surveys or web and mobile applications [4,5,6,7]. However, the quality of the user experience is directly influenced by the functionality and usability of mobile devices, as well as the reliability of internet connectivity, factors that fall within the domains of user experience (UX) and quality of experience (QoE). In this paper, we examine the limitations of conventional UX and QoE frameworks and metrics in the context of mobility-driven applications, proposing the concept of adaptable QoE, which enables dynamic user surveys. QoE evaluation scenarios are often static in terms of questions and selectable predefined answers. This paper argues that in dynamic scenarios (e.g., biking activities), a more flexible assessment method is necessary, as the relationship between influencing factors (as defined or hypothesized) and the observed task is context-dependent. The use case applied in this research leverages land cover data and employs dynamic questioning, where users respond based on their actual experiences, rather than a mixture of experiences and perceptions, as is common in survey questionnaires.

This paper emerged out of an EU-funded research project titled Combined Mobility, which involved the development of a mobile application, VäxlaUpp in the initial stage. The objective of the project was to promote cycling culture in Blekinge and to identify strategies for enhancing the user experience of cyclists. The app was made freely available for both Android and iOS users. The aim was to motivate residents of Blekinge, Sweden, to adopt cycling for daily transportation, thereby promoting improved health and reducing carbon emissions. With the global push towards sustainable and green transportation, cities are investing in cycling infrastructure as a means to reduce carbon emissions, alleviate traffic congestion, and promote public health [8,9,10].

Land cover information, which describes the physical material on the surface of the Earth, has been traditionally utilized in various sectors such as agriculture, mining, and urban planning [11,12,13,14,15]. Urban planners use land cover data to manage land use, design sustainable cities, and monitor environmental changes. These applications highlight the versatility and importance of accurate land cover information in supporting diverse activities that shape our natural and built environments. While the use of land cover data has primarily focused on these sectors, its application in transportation infrastructure specifically in the development of bicycle paths has become increasingly significant [16,17,18]. Accurate land cover data are thus essential for the effective planning and segmentation of bicycle paths, allowing for better decision-making in the development of cycling infrastructure. Bicycle path segmentation, which involves identifying and mapping bicycle paths within urban landscapes, is a critical component of transportation infrastructure planning. Traditionally, this task has been approached through manual mapping and field surveys, both of which are labor-intensive and subject to human error. Recent advancements in remote sensing and geospatial technologies have provided new avenues for automating this process. In this paper, we have used Google Earth Engine (GEE) [19], which allows researchers to perform complex spatial analyses and extract relevant information at scale. This capability is particularly valuable for urban planners and transportation engineers who need to analyze large areas quickly and efficiently. A Python script was written for processing and analyzing the extracted land cover data. The combination of GEE’s data extraction capabilities and Python’s processing power enables a scalable and efficient approach to bicycle path segmentation.

The objective of this research is to propose a novel QoE methodology that leverages GEE and mobile applications dedicated to outdoor activities to accurately assess user experiences by incorporating human, system, and contextual influence factors. In this study, by integrating these tools, we aim to provide a reliable and automated solution for identifying bicycle paths in urban environments. Bicycle paths are composed of multiple segments that can be combined and interchanged in various ways to optimize the cycling experience based on weather, path conditions, scenery, and other factors. Our contributions are threefold: First, we demonstrate the limitations of current methods for obtaining user experience feedback related to outdoor activities. Second, we present a Python-based, server-side prototype workflow leveraging GEE to process these data and accurately segment bicycle paths. Finally, we conducted a user survey, gathering real-world feedback from cyclists and analyzing the results to evaluate the benefits of adaptable surveys over traditional methods. This approach is anticipated to generate datasets that more accurately capture users’ experiences, enabling a nuanced understanding of human behavior and satisfaction levels in real-world contexts. This research contributes to the expanding field of user-centric modeling, addressing the need to improve user experience and support infrastructure planning through a novel approach that enhances both the accuracy and efficiency of bicycle path identification. Our methodology enables the capture of authentic user experience feedback within mobility-driven applications, offering valuable insights for stakeholders seeking to promote healthy outdoor activities.

The structure of this paper is as follows: Section 2 provides an overview of the background and a brief exploration of relevant technologies. Section 3 details the experimental setup, including all parameters and methods applied to extract the results. Section 4 presents the assessment outcomes with necessary explanations. Finally, Section 5 outlines the conclusions drawn from our study.

2. Background and Methodological Overview

This section reviews related work and provides an overview of land cover classifications, the role of QoE in outdoor sports activities, and its applications in promoting physical activity, which contributes to improved health and a cleaner environment.

2.1. Quality of Experience and Outdoor Activities

As mentioned in Section 1, the objective of the research project was to provide users with a mobile application to track their outdoor bicycling activities, promoting better health and reducing the carbon emissions footprint in the Blekinge region of Sweden. The end-user experience can be influenced by numerous factors, including their health and stamina, weather conditions, bicycle path quality, the mobile application itself, internet availability, and more. From a technical perspective, our focus was on collecting accurate data to analyze usage patterns and later present the results to stakeholders through data visualization and other methods. However, this process can be significantly hindered by incomplete sessions due to technical issues such as application unavailability, user interaction problems, or internet disruptions.

Thus, to investigate these complex interdependencies of human- and system-related factors, we decided to use quality of experience which is defined by ITU-T as ‘The degree of delight or annoyance of the user of an application or service’ [20], with reference to full definition that continues with ‘It results from the fulfillment of his or her expectations with respect to the utility and / or enjoyment of the application or service in the light of the user’s personality and current state’ [21]. This definition primarily addresses the fulfillment of user expectations, which can be shaped by various factors in different forms. To classify these factors, the term influence factor (IF), is defined as ‘Any characteristic of a user, system, service, application, or context whose actual state or setting may have influence on the quality of experience for the user’ [21,22].

Based on the definition provided above, numerous influence factors (IFs) can affect user satisfaction with a service. According to the Qualinet White Paper [21], these factors are typically categorized into three groups: human, system, and context. Figure 1 illustrates various factors within these categories that may influence user expectations during outdoor cycling. A typical scenario of summer and winter cycling, as experienced by users of our application, is depicted. In addition to the human-related IFs mentioned in Figure 1, the user experience can be significantly affected by system-related IFs, such as internet disruptions in remote areas, concerns over high battery consumption, or rare instances of application crashes. Furthermore, the user’s mood and their surroundings play a crucial role in motivating cyclists to engage in biking more frequently. QoE primarily focuses on user satisfaction with digital content and is typically quantified using both objective and subjective measures [23,24]. Objective metrics assess the quality of content received such as images, audio, and video by comparing it with the original (full-reference), using metadata from the original (reduced-reference), or based on predetermined quality levels in the absence of the original stimulus (no-reference). Subjective assessments, on the other hand, rely on direct feedback from users, typically quantified using the Mean Opinion Score (MOS) as defined by the International Telecommunication Union [25]. This feedback is often obtained through a five-point Absolute Category Rating (ACR) scale [26], where 5 indicates “Excellent” and 1 indicates “Bad”.

One challenge with subjective QoE assessments lies in the fact that MOS was originally developed to evaluate user satisfaction with audio quality in telecommunications and was later extended to multimedia content, with its effectiveness being questioned in many studies [23,27,28,29,30,31,32,33,34]. Furthermore, in the context of outdoor sports activities, it is difficult to design QoE assessments that align with ITU-T P.910 [26] and ITU-R BT.500 [35] recommendations, as these standards are primarily geared toward multimedia quality assessment. Finally, another challenge lies in the fact that QoE influence factors are typically predefined and remain constant to ensure replicability and comparability.

2.2. QoE or User Experience

In light of the issues discussed in the previous section regarding the application of QoE to outdoor sporting activities involving mobile services, some studies have explored the use of user experience (UX) as an alternative framework for evaluating outdoor sports [36,37]. UX focuses on users’ perceptions of digital services, such as mobile applications, and is widely used by developers to design websites and applications that enhance ease of use for end users [38]. The importance of context in UX has been emphasized in numerous studies. These studies include surveys of practitioners in software development, focusing on contextual factors, emotional responses, and social aspects [39]. Additionally, a dedicated scale tailored to capturing contextual nuances has been evaluated within these investigations [40]. Although QoE and UX share some commonalities, there are key differences between the two. A detailed discussion on the relationship between QoE and UX can be found in the book chapter [41]. However, in their current forms, neither fully addresses the challenge of quantifying user experience of outdoor activities, particularly considering emerging use cases driven by mobile applications and fitness tracking devices. While QoE objective and subjective metrics are primarily focused on multimedia, traditional quality of service (QoS) metrics, such as bandwidth, packet loss, jitter, etc., may offer some insights to service providers in remote areas. However, they are insufficient for capturing user perception, as they fail to account for various influence factors unrelated to the system.

2.3. Landcover Classifications

Landcover refers to the physical composition and types of surface materials present in a given area, such as forests, deserts, vegetation, anthropogenic structures, snow, and more. In agriculture, land cover data are crucial for monitoring crop health, assessing soil conditions, and managing water resources [11,42,43]. Mining operations rely on this information to evaluate the environmental impact of extraction activities and to rehabilitate land post-mining [44,45,46]. Landcover is dynamic, subject to temporal changes driven by factors such as weather conditions, natural disasters, and human-induced activities like conflict [47,48]. As a result, continuous monitoring is essential. Thus, the classification and analysis of landcover have significant implications for a variety of applications, including environmental and disaster management, urban planning, and agricultural resource optimization.

Yang et al. highlighted that numerous land cover classification systems have been developed at national, regional, and global levels and the lack of interoperability between these systems further complicates the analysis of multi-source, heterogeneous land cover data for diverse applications [49]. Thus, landcover can be classified into various categories depending on the specific objectives of the study. In this paper, we utilized the NASA MODIS dataset [50,51] in our prototype implementation, which categorizes landcover into the following classes:

• Water       

  Water bodies include inland water reservoirs, rivers, lakes, and oceans.

• Forest      

  The forested class comprises areas dominated by trees, covering tropical forests and mountainous regions with dense trees.

• Shrub, Grass   

  Grasslands and shrubs consist of areas covered by small plants, shrubs, and natural grasslands.

• Wetlands      

  Wetlands are typically characterized by the presence of both water and soil, where water saturation affects the vegetation, as seen in swamps.

• Croplands    

  The agricultural class primarily encompasses vast areas used for cultivation, including the production of vegetables, grains, fruits, and dairy farming.

• Urban         

  The urban class includes areas dominated by human settlements and infrastructure such as cities, transportation networks, and suburbs.

• Crop mosaic   

  The cropland mosaic class is different from croplands due to its heterogeneous nature, characterized by small agricultural fields interspersed with forests, urban areas, or other landcover types.

• Snow and ice 

  The snow and ice class includes areas covered by permanent or seasonal snow and ice, such as glaciers, where temperatures remain low throughout the year.

• Barren       

  Barren areas consist of regions with minimal biological activity, such as deserts, salt flats, and rocky landscapes.

• Tundra      

  Tundra refers to cold regions with sparse vegetation and limited tree cover, with temperatures ranging from −40 °C to 18 °C [52]. Unlike snow and ice-covered areas, tundra allows for a short growing season due to slightly warmer conditions during certain parts of the year.

The MODIS International Geosphere-Biosphere Programme (IGBP) color palette assigns 18 distinct colors for landcover classification [19,51]. Due to the diversity within the water and forest categories, these classes are represented by five different colors. All other landcover classes are assigned a single color each.

Huang et al. [16] demonstrated the integration of spatial data with various data sources by defining ontologies and applying them across a diverse dataset. Their use case involved visualizing urban bicycle paths by combining spatial data with field data, such as signage and speed limits. However, the study primarily focuses on integrating heterogeneous data and does not incorporate land cover information for mobile applications and user experience.

Bachechi et al. [18] utilized the open-source QGIS tool [53] to visualize cycling paths in the province of Modena, Italy. They categorized these paths into five different groups, ranging from dedicated bike lanes to areas lacking separate infrastructure for cyclists. The study primarily focuses on the visualization of bicycle routes and does not incorporate land cover data or investigate user experience.

To promote bicycle usage in small cities and rural areas, Cordero et al. [54] developed a route visualization tool based on data from a travel survey conducted in 2017. The study primarily focuses on urban planning strategies aimed at encouraging healthy activities such as cycling. The tool leverages the ArcGIS [55] toolbox for spatial data; however, end-user application and feedback fall outside the study’s scope. Due to privacy constraints, providers of GPS-based outdoor activity data, aggregate this information before it is commercially distributed. Huber et al. [56] developed a multi-step mechanism to extract individual cycling routes from aggregated datasets. Their study further estimates user behavior related to route choice in a specific case study conducted in Germany. However, the study does not address route segmentation based on land cover or incorporate user feedback regarding their experience.

Bhowmick et al. [57] collected a dataset comprising 673 bicyclists across 19,782 trips using GPS data from Melbourne, Australia. A pre-data collection survey questionnaire was designed for appropriate sampling to gather information from adult bikers by recruiting them based on certain parameters. The raw GPS data include information on location, time, and speed. Data collection was conducted through a mobile application and a Bluetooth beacon attached to each participant’s bike. The study does not incorporate land cover information or dynamic feedback for different trip segments. Although this dataset enables the extraction of route segments with land cover application, it remains limited by the absence of context-aware feedback on user experience.

3. Experimental Setup and Methods

The mobile application VäxlaUpp, utilized by over 300 project participants for over a couple of years was available in Swedish. The project included various activities and competitions among active application users, with prizes awarded during social gatherings. The application was updated to provide path segmentation based on land cover data. A demonstration video showcasing path segments and the dynamic questionnaire can be seen via this link [58]. As shown in the video, users can view their bicycling routes and, upon selecting a specific trip, observe the entire path in varied colors corresponding to segment types based on land cover information. Users can select individual segments to access questions tailored to that segment’s characteristics, such as safety, lighting conditions, and suitability for winter cycling. Due to screen size constraints, each segment is limited to two context-specific questions. Users also have the option to provide more detailed feedback by accessing the web page.

3.1. Conceptualization of Perception and Experience

Traditional user surveys conducted by various stakeholders such as urban planners, contractors, and health officials regarding outdoor activities are typically based on a predefined set of questions, administered either through on-site surveys or digital platforms. The limitations of these conventional methods are illustrated in Figure 2. Users are often asked to provide feedback on their perceptions, and data are collected from a broad range of respondents, regardless of their actual experiences. For instance, in a survey about the safety of bicycle paths in remote areas at night, many participants may not have directly encountered these conditions and instead responded solely based on their assumptions. Thus, feedback grounded in assumptions represents preconceived ideas that users form to fill gaps when information is incomplete or unclear. These assumptions are often influenced by prior knowledge, beliefs, or expectations, and while they can provide insight into user expectations or mental models, they also introduce the risk of biases. Such biases may distort the accuracy of the feedback, leading to conclusions that do not fully align with the actual user experience or system performance [59]. Recognizing the role of assumptions is critical when analyzing user feedback, as it helps differentiate between genuine insights and speculative conclusions. The experience of a user who has endured the challenges of outdoor activities in remote locations such as internet connectivity disruptions, and anxiety over battery consumption without access to charging facilities would differ significantly from that of a user responding based purely on assumptions, as depicted in Figure 2. Nonetheless, the acquired data are often utilized for various visualizations, and subsequent measures are implemented to enhance user experience, even though true user experiences were never adequately quantified.

The use of land cover data and bicycle path segmentation will enable more adaptable user surveys, allowing for the collection of feedback from only those users who have experienced specific routes and conditions. For instance, feedback regarding safety will be solicited solely from users with experience in remote cycling or late evening rides. Additionally, perceptions of safety in outdoor activities may vary based on factors such as gender. Similarly, users who have cycled in winter conditions, with snow and ice as reported by landcover data, will be asked about issues such as debris and road clearance. The dataset obtained from these targeted context-aware surveys will more accurately reflect user experiences, allowing all stakeholders to implement relevant measures that enhance overall user satisfaction.

3.2. User Survey

After implementing the proposed methodology within the application, we only received 16 inputs from users who had access to the beta version of the application with this functionality. Moreover, no feedback was possible on winter cycling using segments as the project concluded and the application was taken offline before the season. Thus, to obtain user feedback on their experiences, we conducted an online survey by inviting a pool of application users to provide feedback via email, following the prescribed guidelines [60]. The decision to conduct an online survey was not merely driven by the application going offline but rather by the need to benchmark user perceptions specifically within the context of Denmark, a country with a strong cycling culture compared to Sweden. In Copenhagen, the feedback was collected from employees of two companies that incentivize cycling through monthly awards based on categories such as usage frequency and total distance traveled. The idea was to obtain user assessments by two different groups and identify different sets of correlations, which would later serve as a benchmark for the results obtained from the new methodology in the future. Thus, the online survey was conducted concurrently in Copenhagen, Denmark, and Blekinge, Sweden, with a total of 49 participants responding to the questionnaire. However, three responses were discarded due to incomplete data. The questionnaire comprised 16 questions focusing on user demographics, type of bicycle used, cycling habits, factors affecting their rides, safety and condition of bicycle paths, factors that could motivate or hinder cycling, winter cycling, and debris clearance. The survey was conducted by following the guidelines of the Swedish Research Council [60,61]. The English version of the survey questions is available in Appendix A. The survey questions were organized into three categories: Questions 1 to 7 gather general demographic information, reasons for biking, and the type of bicycle used. The remaining questions primarily address various segments categorized by land cover data. Questions 8 through 11 evaluate users’ perceptions of safety and their overall biking experience. Question 12 focuses specifically on taking user feedback on whether they have the experience of winter cycling and the later questions regarding segment 2 in Appendix A.3 are only displayed to users who cycle during winter months. Similarly, questions regarding snow and ice will be presented exclusively to users identified, based on land cover data, as likely to encounter snowy conditions in winter. Consequently, questions 13 to 16 are tailored to users in snow- and ice-affected areas, exploring their expectations, experiences, and challenges faced while cycling in winter.

3.3. Route Segmentation

To acquire land cover information and subsequently segment the entire route based on variations in land cover classifications, a prototype workflow was developed using a Python script on the server-side. The script utilizes Python version 3.10.11, the Google Earth Engine (GEE) Python API, and the Open Source Computer Vision Library (OpenCV) version 4.7.0 [62,63]. The methodology applied for this prototype can be summarized as follows:

• The GEE add_ee_layer function was employed to overlay land cover information on a Folium map.

• The coordinates of the user’s route were obtained from a CSV file extracted from project data. Since these coordinates were not equidistant, a custom function was implemented to adjust the zoom level accordingly.

• A snapshot of the Folium map was converted into a Python Imaging Library (PIL) object, and the binary image was converted into PNG format using the Pillow library. OpenCV was then used to convert the image to grayscale, detect contours, and crop the image around the largest detected contour.

• Finally, to extract the relevant color information according to the IGBP palette, the hexadecimal color strings were converted into BGR tuples compatible with OpenCV.

Figure 3 presents the results of path segmentation applied to two different routes. The segments were generated based on the geographic coordinates extracted from the project dataset, where consecutive latitude–longitude coordinate pairs were transformed into rectangular segments. Route 1 represents a smaller collection of segments primarily situated in a semi-urban area, bordered by tree cover. In contrast, Route 2 was selected to demonstrate a more extensive variation in land cover, as it encompasses a 40 km path surrounding the city of Karlskrona, Sweden. It is important to note that the size of the rectangular segments is not uniform—a result of the non-equidistant nature of the input coordinate data. Subsequently, the IGBP color palette was applied to both routes to depict land cover classifications, as illustrated in Figure 4. For Route 1, only two colors corresponding to semi-urban and forest areas were extracted. In contrast, for Route 2, six distinct colors from the IGBP palette were identified: 0xaec3d4 (water), 0xcc0013 (urban), 0x111149 (wetlands), 0x33280d (crop mosaic), 0x387242 (semi-urban/forest), and 0x91af40 (shrub/grass) [19]. These results accurately segment the entire route based on the land cover classifications. Figure 5 illustrates the actual map displayed to the user, where different segments of the route are distinguished by distinct colors based on land cover classifications. The user should be able to select specific segments, prompting the system to present an adaptive questionnaire tailored to the characteristics of that land cover type.  

Although the dataset used in this research comprises bicycle paths, the proposed methodology is versatile and could be applied to various other use cases, such as car journeys or trip planning, to select routes that are more scenic or secure. Additionally, this approach enables the collection of user experience data by focusing on relevant aspects of the route. For instance, questions about scenery or forested areas would not be directed to users who have not encountered those types of land cover segments. As a result, datasets obtained from segment-based application surveys using this methodology would provide a more accurate representation of user experience, contextualized by factors such as time of day and location, rather than relying solely on subjective perceptions or a blend of experience and perception.

4. Results and Discussion

The demographic outcomes of the online survey are presented in Table 1. The survey was conducted concurrently in Blekinge, Sweden, and Copenhagen, Denmark as there is a popular trend of cycling in Copenhagen. This deliberate choice aimed to benchmark the cycling habits and analyze the factors influencing these behaviors. Participants were given the option to respond in either English or Swedish. Participants who had not experienced the challenges of winter cycling were automatically excluded from sections regarding winter conditions, such as ice and snow. However, we were unable to verify the claims of users who indicated experience with winter cycling and harsh conditions, as there was no contextual information available regarding the date, time, location, or the number of users present on that particular segment. This limitation underscores the inherent weaknesses of traditional surveys, regardless of how well the questionnaire is designed or structured. The outcomes of scalable questions can be seen in Table 2. The majority of respondents rated the quality of bicycle paths and their overall experience as either good or neutral. However, perceptions change during the winter season due to issues related to snow and debris clearance. A detailed analysis considering demographic factors will be provided later in this section.

One of the key insights from user feedback concerned factors that could motivate increased cycling, aligning with the primary aim of the project. Notably, the questionnaire was conducted at the end of the project lifecycle, with participants in Blekinge already utilizing the custom application, unlike those in Copenhagen. This question allowed multiple responses, enabling users to select from any of the provided options. The results are presented in Table 3. The data should be interpreted row-wise, with the combined percentage representing the proportion of users who selected this factor, irrespective of their country. It is evident that the majority of users prioritized safety and natural surroundings, supporting the hypothesis that cycling motivation increases when routes feature scenic and natural elements. Additionally, options such as adequate lighting and secure parking facilities are closely tied to both personal and equipment safety, further underscoring these as motivating factors. Notably, in environments where safety and a pleasant atmosphere are assured, most users do not feel a significant need for a companion or group cycling, suggesting that a well-designed cycling infrastructure can foster confidence and independence among cyclists.

The perception of safety while cycling in suburban areas is a critical factor, with notable variations based on gender. Figure 6 presents the results, with gender and location (Blekinge and Copenhagen) considered as factors. The most encouraging aspect is that none of the respondents selected the fourth or fifth options, Somewhat unsafe or Very unsafe, respectively, indicating a high level of confidence among Scandinavian users regarding their safety. As anticipated, female cyclists expressed slightly greater safety concerns than their male counterparts. Furthermore, due to Copenhagen’s urban environment and established cycling culture, Danish users reported feeling safer overall compared to those in Blekinge.

Another key aspect concerned user perceptions of cycling path maintenance and their overall cycling experience, inclusive of various contributing factors. Figure 7 presents user responses segmented by country. The variables are scaled from 1 to 5, where 1 represents Excellent and 5 represents Bad, as shown in Appendix A. Consequently, a lower mean value indicates a higher level of user satisfaction. In terms of bicycling path quality, the mean rating for Danish users is higher than that of Swedish users. However, a key observation is that responses from Swedish users are more varied, resulting in higher standard deviation values. Given the expansive land area of the Blekinge region, this suggests that users from different localities may have experienced varying levels of cycling path quality. While there is minimal difference in overall biking experience between the two groups, Swedish respondents again exhibit greater variability in their ratings, as shown in Figure 7.

Questions 13 to 16 focused on winter cycling, and only users who selected ’Yes’ or ’Maybe’ in response to having a winter cycling experience were allowed to proceed as can be viewed in Appendix A. This criterion excluded eight respondents who had not cycled in snowy or icy conditions. The results are illustrated in Figure 8. Responses were scaled from 1 to 5, with 1 representing Strongly Satisfied and 5 indicating Strongly Unsatisfied. Consequently, a lower mean value reflects a higher level of satisfaction. Although the sample size remains consistent for both groups, the responses from Swedish participants show greater variability, resulting in higher standard deviation values.

Analysis

In the context of biking, various environmental factors may impact user experience. To better understand these influences, we conducted two surveys, a segment-based application survey and an online survey to gather users’ perceptions of environmental conditions. The results presented above discussed the environmental factors in terms of frequencies of singular variables, expressed as counts or percentages. To analyze the interdependencies between multiple variables and assess their associations, we computed the correlation coefficients for different questions [64,65]. The surveys included five scalable questions, each addressing a specific environmental factor (see the identifiers in Appendix A: Q11 pertains to the overall biking experience, Q10 to the quality of the path, Q8 to the safety of the semi-urban route, Q16 to the safety conditions of a path, and Q15 to snowy weather conditions). Given that responses were on an ordinal scale (1 to 5), Spearman’s correlation coefficients [66] were applied to measure the impact of these environmental factors.

The results (see Table 4) show that, for all respondents, all factors are positively correlated to the overall biking experience, and the factor of path quality has the most correlation as compared to the others. Meanwhile, a p-value greater than 0.05 (with 95% confidence) indicates that the safety of the semi-urban route was not significantly correlated to the overall biking experience.

Additionally, given that these factors were subjectively perceived, we performed correlation analyses across different respondent groups to assess the consistency of associations between overall biking experience and its influencing factors. The resulting correlation coefficients for each group are presented in the following tables, with consistency indicated by the p-value. For a given factor (represented by each row in the table), if the p-value is significant in one group but not in another, this indicates an inconsistent association between the factor and overall biking experience. Conversely, if the p-values in different groups are all significant or insignificant, it means the association between the corresponding factor and overall biking experience is consistent. For gender (see Table 5), an inconsistency is observed for the factor of road safety conditions, as the p-value in the female group exceeds 0.05, while in the male group, the positive correlation with overall biking experience is significant. Regarding nationality (see Table 6), inconsistencies emerge for both safety conditions and biking during snowy or icy conditions. Table 7 presents the correlation coefficients between biking influence factors and the purpose of biking. Notably, this question was one of three that allowed participants to provide multiple responses. The results indicate that only path quality consistently aligns with users’ responses across different biking purposes.

Although the feedback was collected through an online survey, the analysis highlights which environmental factors may influence the overall biking experience and how this impact varies based on user perceptions. This underscores the challenge of assessing general user experience based on perceived environmental factors, especially in highly dynamic conditions. Consequently, we emphasize the need for adaptive surveys to capture more accurate and context-sensitive feedback on user experience.

Meanwhile, the relationship between overall biking experience and environmental influence factors, as indicated by an online survey and initial results from the segment-based application survey, shows correlation coefficients of $69\%$ and $85\%$, respectively, for the path factor. In contrast, this relationship yields coefficients of $19\%$ and $30\%$, respectively, for the safety factor. These preliminary results indicate different degrees of correlations, while their directions and strengths are close to each other. This is a promising observation, as it (a) indicates the potential of additional insights from the proposed method, while (b) still following the same expected trends. We conclude that the application method provides a valuable perspective by quantifying the experience based on real physical interactions, a detailed study of which is left for future work.

5. Conclusions

In this paper, we have proposed a novel approach to the user experience for mobility-driven applications by enabling route selection based on individualized factors such as safety, environmental quality, and scenery. With the successful implementation of our prototype, we can now provide an adaptable, context-aware questionnaire that captures users’ experiences in real-time. The resulting datasets gathered from user feedback through this path segmentation and customized questioning approach will empower stakeholders to make informed, contextually relevant decisions. This study highlights the need to expand existing QoE methodologies, as new use cases are likely to emerge with mobile applications supporting outdoor activities, spanning areas such as outdoor sports, healthcare, transportation, and urban planning, where user feedback is critical. Future work will focus on creating new datasets through this approach and benchmarking these with established feedback from this study for comprehensive evaluation.

Author Contributions

Conceptualization, O.N. and S.K.; methodology, O.N., S.K. and M.F.; software, O.N.; application survey, O.N., Y.Z. and S.K.; online survey, O.N. and Y.Z.; results compilation, O.N. and Y.Z.; validation, S.K. and M.F.; writing—original draft, O.N. and Y.Z.; writing—review and editing, S.K. and M.F.; supervision, S.K. and M.F. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Subjective assessments were carried out following the research ethics principles outlined by the Swedish Research Council.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:

ACR	Absolute Category Rating
GEE	Google Earth Engine
IFs	Influence Factors
IGBP	International Geosphere-Biosphere Programme
MOS	Mean Opinion Score
QoE	Quality of Experience
QoS	Quality of Service
UX	User Experience

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Factors influencing quality of experience of outdoor bicycling activities.

Figure 2. General vs. adaptable survey questions.

Figure 3. Path fragmentation.

Figure 4. IGBP color palette application to the path segments.

Figure 5. Displayed map to the users.

Figure 6. Safety perceptions of users.

Figure 7. User ratings on hedonic scale for bicycle paths and overall experience.

Figure 8. User assessments of snow/debris clearance and pathway safety.

Table 1

Frequency table of user info and online survey language.

Variable	Options to Respondents	Count
Language	English	26
	Swedish	20
Country	Denmark	23
	Sweden	23
Gender	Male	25
	Female	20
	Prefer not to say	1
Age	Less than 25 years	4
	25–50 years	33
	More than 50 years	9

Table 2

User responses to questions based on the hedonic scale.

Questions	Very Safe	Somewhat Safe	Neutral	Somewhat Unsafe	Very Unsafe
Did you feel safe while traveling along the semi-urban route?	11	23	12	0	0
Questions	Excellent	Good	Neutral	Poor	Bad
How would you rate the overall quality of the bicycle path during your trip?	6	15	21	0	4
How would you describe your overall biking experience?	6	26	13	1	0
Questions	Strongly satisfied	Slightly satisfied	Neutral	Slightly unsatisfied	Strongly unsatisfied
How do you rate snow/debris clearance from the cycling paths?	3	3	16	15	1
How would you assess the safety conditions of roads, including intersections and crosswalks, during winter?	0	6	11	15	6

Table 3

User response to multiple options.

Question	Response Options		Biking Country		Total
			Sweden	Denmark
What factors would motivate you to cycle more?	Feeling safer on the roads	Count	10	11	21
		% within Combine	47.6%	52.4%
		% within Country	43.5%	47.8%
		% of Total	21.7%	23.9%	45.7%
	Natural surroundings and favorable weather	Count	13	10	23
		% within Combine	56.5%	43.5%
		% within Country	56.5%	43.5%
		% of Total	28.3%	21.7%	50.0%
	More companions cycling with you	Count	1	5	6
		% within Combine	16.7%	83.3%
		% within Country	4.3%	21.7%
		% of Total	2.2%	10.9%	13.0%
	Improved bicycle parking facilities	Count	9	11	20
		% within Combine	45.0%	55.0%
		% within Country	39.1%	47.8%
		% of Total	19.6%	23.9%	43.5%
	Adequate lighting in dark areas	Count	7	10	17
		% within Combine	41.2%	58.8%
		% within Country	30.4%	43.5%
		% of Total	15.2%	21.7%	37.0%
Total		Count	23	23	46
		% of Total	50.0%	50.0%	100.0%

Table 4

Relation between overall biking experience and IFs based on the entire group of subjects.

Factor	Correlation Coefficient	p-Value
Path	0.69	$1.4\times {10}^{-6}$
Safety	0.19	0.25
Safety and path	0.45	$5.0\times {10}^{-3}$
Snow weather	0.32	0.05
Total number	46 interviewees

Table 5

Relation between overall biking experience and IFs based on different genders.

Factor	Female		Male
	Correlation Coefficient	p-Value	Correlation Coefficient	p-Value
Path	0.72	$2.5\times {10}^{-3}$	0.56	$5.9\times {10}^{-3}$
Safety	0.27	0.32	0.10	0.66
Safety and path	0.33	0.23	0.47	0.02
Snow weather	0.23	0.42	0.33	0.13
Total number	20 interviewees		25 interviewees

Table 6

Relation between overall biking experience and IFs based on different nationalities.

Factor	Sweden		Denmark
	Correlation Coefficient	p-Value	Correlation Coefficient	p-Value
Path	0.74	$3.1\times {10}^{-4}$	0.66	$2.2\times {10}^{-3}$
Safety	0.18	0.46	0.20	0.41
Safety and path	0.58	0.01	0.07	0.78
Snow weather	0.49	0.03	0.21	0.39
Total number	23 interviewees		23 interviewees

Table 7

Relationship between overall biking experience and IFs across different biking purposes.

Factor	Commute		Leisure		Exercise
	Correlation Coefficient	p-Value	Correlation Coefficient	p-Value	Correlation Coefficient	p-Value
Path	0.77	$2.3\times {10}^{-3}$	0.48	0.04	0.67	0.02
Safety	0.22	0.23	0.22	0.35	0.63	0.03
Safety and path	0.40	0.02	$3.2\times {10}^{-3}$	0.99	0.61	0.03
Snow weather	0.34	0.06	0.29	0.22	0.35	0.26
Total number	34 interviewees		23 interviewees		14 interviewees

Appendix A. Survey Questionnaire in English

Appendix A.1. General Info

• 1.

  Please select the language:

  • English

  • Svenska

• 2.

  Name (Optional):

• 3.

  Gender:

  • Male

  • Female

  • Prefer not to say

• 4.

  Age:

  • Less than 25 years

  • 25–50 years

  • More than 50 years

  • Prefer not to disclose

• 5.

  How do you normally use your bicycle for rides? (Check all that apply)

  • Commute

  • Leisure/Fun

  • Exercise

• 6.

  What type of bike(s) do you normally use for trips?

  • Road Bike

  • Mountain Bike

  • Hybrid (electric motor + pedal)

Appendix A.2. Segment 1

• 7.

  What factors would motivate you to cycle more? (Check all that apply)

  • Feeling safer on the roads

  • Natural surroundings and favorable weather

  • More companions cycling with you

  • Improved bicycle parking facilities

  • Adequate lighting in dark areas

• 8.

  Did you feel safe while traveling along the semi-urban route?

  • Very safe

  • Somewhat safe

  • Neutral (neither safe nor unsafe)

  • Somewhat unsafe

  • Very unsafe

• 9.

  What would you say is the main reason that stops you from biking more often?

  • Concern for personal safety

  • I live too far away

  • Bad weather

  • I don’t want to get all sweaty before class/work

• 10.

  How would you rate the overall quality of the bicycle path during your trip?

  • Excellent

  • Good

  • Neutral (not good or bad)

  • Poor

  • Bad

• 11.

  How would you describe your overall biking experience?

  • Excellent

  • Good

  • Neutral/Fair

  • Poor

  • Bad

• 12.

  Have you experienced snow or harsh conditions during your bicycle trips?

  • Yes

  • Maybe

  • No

Appendix A.3. Segment 2

• 13.

  Have you experienced a slip-and-fall incident that may prevent you from cycling in harsh conditions?

  • Never

  • Once

  • Twice

  • Thrice or more

• 14.

  What is the main problem with winter cycling that disturbs you? (Check all that apply)

  • Reduced visibility

  • Snow

  • Ice

  • Cold weather

• 15.

  How do you rate snow/debris clearance from the cycling paths?

  • Strongly satisfied

  • Slightly satisfied

  • Neutral (neither satisfied nor unsatisfied)

  • Slightly unsatisfied

  • Strongly unsatisfied

• 16.

  How would you assess the safety conditions of roads, including intersections and crosswalks, during winter?

  • Strongly satisfied

  • Slightly satisfied

  • Neutral (neither satisfied nor unsatisfied)

  • Slightly unsatisfied

  • Strongly unsatisfied

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