Risk-Perception Formation Considering Tangible and Non-Tangible Aspects of Cycling: A Flemish Case Study

Risk perception influences cycling attributes toward its adoption. Researchers are investigating attributes associated with risk formation. In this study, various attributes were selected which influence the user’s formation of risk perception. For this purpose, an online questionnaire survey was conducted in Flanders, among all segments of the population (N = 774). Participants were asked questions for attributes relating to risk formation. Results suggested that risk formation among users evolves around tangible to non-tangible attributes. The spectrum of risk perception was developed which visualizes risk evolution, considering various attributes. Surprisingly, elements such as “comfort”, surface evenness, and policies were rated as being neutral. Infrastructure and the presence of opposite road users tend to be foreseen as critical factors for risk formation. Risk perception varies depending upon psychometric paradigm shifts, such as dread and unknown risk. This strange notion is considered to lie in a space between dread and unknown risk. This explains the difference in risk perception, knowingly or not knowingly (subconsciously), yet expressing cognitively and evolving inside. This is an interesting finding, but reasons behind such a motive need to be explained. A possible explanation behind such behavior is that people tend to change their responses due to knowledge acquisition during the survey.


Introduction
Cycling is considered a global mobility solution to issues such as congestion, health, environment, and socioeconomic impact. However, factors affecting cycling-adoption levels need to be investigated in detail. Perception formation for a specific facility, vale, bicycling, is dependent upon multiple factors, i.e., previous experience, infrastructure, local measures, and environmental aspects.
Road-traffic risk perception is dependent on fear and sensation [1]. Separate cycling paths are considered more secure than mixed motorized bicycle lanes [2]. Interaction of cyclists with other modes raises the risk levels [3], while Jacobsen [4] reveals that a higher number of presence of road users increases safety perception. Experience increases the adaption level for risks. Variant risk perception is subjective and also ranges from person-to-person personal characteristics and attributes [5]. Choosing a particular strategy for avoiding traffic interactions may eventually lead to a reduction of objective risk levels. Perceived behavioral control refers to individual perception to execute a particular action [6]. Perceived behavioral control refers to self-perception regarding the ability and resulting quality toward doing an action. For cyclists, this is linked to how skilled and efficient they are at avoiding troublesome situations. Reduced risk levels are observed when perceived behavioral control is high [7]. Higher for traffic safety, culture, and development levels, respectively [34]. Risk-perception formation varies among countries due to their respective traffic-safety conditions, user attitudes, driving behavior, cultural differences, and socioeconomic conditions [35,36].
This research attempts to address the research gaps mentioned in the above studies. The above-stated parameters influence risk formation significantly. This includes investigating parameters in which users interact regularly.
Risk-perception formation is considered a broad term in the accident-analysis context. Although it is influenced by multiple aspects, its formation is not well-defined, especially for regions like Flanders. Therefore, this study aims to analyze, form, and visualize risk-perception formation according to tangible or not tangible aspects, i.e., infrastructure, peer pressure, etc.

Materials and Methods
An online questionnaire survey was conducted between 1 November and 24 December 2018, through online means in Flanders.
With new mobility regulations having been introduced in Flanders, VRUs or weak road users in Dutch such as non-motorized traffic users, i.e., bicycle and pedestrians, were encouraged to participate in traffic. Nevertheless, the higher risk associated with current conditions hinders them from doing so. The survey was evaluated critically through pilot means. Academicians, Professionals, cyclist clubs, and mentors who are engaged in cycling activities actively provided detailed insight. Later, the survey was improved by following their recommendations.
For reducing self-selection bias, a wider audience was attracted from a variety of socio-demographic groups from different locations. The survey was disseminated through cyclist associations, social media groups, bicycle-sharing points, and community forums, in order to attract a larger audience. For reaching a wider audience, a small link with flash messages was distributed. Later, the advertised link possessed a detailed description of the study, along with its aims.
As seen in Figure 1a, a total of 824 participants responded, and 774 participants were recruited for analysis. Figure 1b reveals that mixed responses with an inclination toward "risky" were recorded about the risk perception of current conditions. The excluded participants came from the age group that did not fall under the scope of this study (under 18 and over 65) and were also underrepresented. Underrepresented data categories yield into unrealistic inferences. Therefore, participants in the age groups under 18 and 65+ were removed. Risk-perception formation is considered a broad term in the accident-analysis context. Although it is influenced by multiple aspects, its formation is not well-defined, especially for regions like Flanders. Therefore, this study aims to analyze, form, and visualize risk-perception formation according to tangible or not tangible aspects, i.e., infrastructure, peer pressure, etc.

Materials and Methods
An online questionnaire survey was conducted between 1 November and 24 December 2018, through online means in Flanders.
With new mobility regulations having been introduced in Flanders, VRUs or weak road users in Dutch such as non-motorized traffic users, i.e., bicycle and pedestrians, were encouraged to participate in traffic. Nevertheless, the higher risk associated with current conditions hinders them from doing so. The survey was evaluated critically through pilot means. Academicians, Professionals, cyclist clubs, and mentors who are engaged in cycling activities actively provided detailed insight. Later, the survey was improved by following their recommendations.
For reducing self-selection bias, a wider audience was attracted from a variety of sociodemographic groups from different locations. The survey was disseminated through cyclist associations, social media groups, bicycle-sharing points, and community forums, in order to attract a larger audience. For reaching a wider audience, a small link with flash messages was distributed. Later, the advertised link possessed a detailed description of the study, along with its aims.
As seen in Figure 1a, a total of 824 participants responded, and 774 participants were recruited for analysis. Figure 1b reveals that mixed responses with an inclination toward "risky" were recorded about the risk perception of current conditions. The excluded participants came from the age group that did not fall under the scope of this study (under 18 and over 65) and were also underrepresented. Underrepresented data categories yield into unrealistic inferences. Therefore, participants in the age groups under 18 and 65+ were removed.

Results
The dataset was subjected to the test of normality to check the randomness. Later, the dataset is discussed with the association between variables, utilizing the Chi-square test of independence and Kruskal-Wallis H test for differences. The Chi-square test of independence determines whether there is an association between two nominal variables. Cramer's V value represents the strength of the relationship in numeric form. The Kruskal-Wallis H test is a nonparametric test which determines significant differences among a foreseen group of variables. The Kruskal-Wallis H test determines the stochastic dominance of one sample or not, but it does not determine the occurrence of stochastic dominance. Degree of freedom is an estimate of parameters equal to the number of independent scores that go into estimate minus number of parameters used as intermediate steps for its estimation.

Results
The dataset was subjected to the test of normality to check the randomness. Later, the dataset is discussed with the association between variables, utilizing the Chi-square test of independence and Kruskal-Wallis H test for differences. The Chi-square test of independence determines whether there is an association between two nominal variables. Cramer's V value represents the strength of the relationship in numeric form. The Kruskal-Wallis H test is a nonparametric test which determines Sustainability 2019, 11, 6474 5 of 19 significant differences among a foreseen group of variables. The Kruskal-Wallis H test determines the stochastic dominance of one sample or not, but it does not determine the occurrence of stochastic dominance. Degree of freedom is an estimate of parameters equal to the number of independent scores that go into estimate minus number of parameters used as intermediate steps for its estimation. Table A2 in Appendix A shows frequency and distribution for the dataset. Results are stated in Table 2 (Chi-square test of association) and Table 3 (Kruskal-Wallis H test); the results are discussed below w.r.t. current risk perception in Flanders.

Discussion
As seen in Figure 2a and Table 3, risk perception changes according to age qua experience with p < 0.05. The higher number of bicycled km leads to higher exposure to various conditions, i.e., infrastructure, environment, and cyclist behavior. It is interesting to see experienced cyclists (higher bicycled km) tend to rate conditions "Risky", whereas less-experienced cyclists (lower bicycled km) rate conditions as safe, hence confirming the conclusion of Heinen and Maat [37] that longer-distance bicycle commutes play a significant role, since attentiveness increases, and the user enjoys a better attitude and has better. Figure 2b confirms that longer distances ultimately result in better user experience, more exposure to conditions, and a better understanding of risk. Where this study notably differs from the conclusion by Heinen and Maat [37] is that a higher cycling distance is not a significant predictor for the safety perception. Older users tend to rate current conditions as "Risky" (N = 136), as noted in Figure 2a. In Figure  3a, parameters such as an improvement in infrastructure and attractiveness are considered important for increasing risk levels from "Risky" to "Safe". The previously stated parameters are more related to traffic safety. Another important observation noted in Figure 3a is that respondents who consider perception levels as "Safe" tend to pursue cycling as a recreational activity, i.e., interaction with nature, which shall be treated as a source of attractiveness. Meanwhile, in Figure 2b it is previously noted that cyclists who use cycling for shorted distance rate current system as "Safe", too. With current risk perception w.r.t varying distance in km's with χ 2 (15) = 24.747, p = 0.05, Cramer's V value = 0.103, and Kruskal-Wallis H test value (3) = 11.492 and p-value = 0.009. In Figure 3a, it is a cognitively undisguised that users who mainly bicycle for pleasure or recreational activities consider cycling to be safe due to the reason that they are exposed less to the general cycling experience. Thus, a green environment means that the "recreational green feeling" is induced, which influences cycling decisions about risk perception. Cognitively indulging cyclists to shift their cognitive priorities from safety to leisure or attractiveness is thus an important policy to consider. This is a very interesting observation since it only complies with two parameters, "Safety and Attractiveness", of the guidelines issued by the European Commission for cycling infrastructures [38,39]. Meanwhile, for risk formation, the parameters "Coherence, Comfort, and Directness" are considered to be not important here. Older users tend to rate current conditions as "Risky" (N = 136), as noted in Figure 2a. In Figure 3a, parameters such as an improvement in infrastructure and attractiveness are considered important for increasing risk levels from "Risky" to "Safe". The previously stated parameters are more related to traffic safety. Another important observation noted in Figure 3a is that respondents who consider perception levels as "Safe" tend to pursue cycling as a recreational activity, i.e., interaction with nature, which shall be treated as a source of attractiveness. Meanwhile, in Figure 2b it is previously noted that cyclists who use cycling for shorted distance rate current system as "Safe", too. With current risk perception w.r.t varying distance in km's with χ 2 (15) = 24.747, p = 0.05, Cramer's V value = 0.103, and Kruskal-Wallis H test value (3) = 11.492 and p-value = 0.009. In Figure 3a, it is a cognitively undisguised that users who mainly bicycle for pleasure or recreational activities consider cycling to be safe due to the reason that they are exposed less to the general cycling experience. Thus, a green environment means that the "recreational green feeling" is induced, which influences cycling decisions about risk perception. Cognitively indulging cyclists to shift their cognitive priorities from safety to leisure or attractiveness is thus an important policy to consider. This is a very interesting observation since it only complies with two parameters, "Safety and Attractiveness", of the guidelines issued by the European Commission for cycling infrastructures [38,39]. Meanwhile, for risk formation, the parameters "Coherence, Comfort, and Directness" are considered to be not important here. Figure 3a shows that respondents who rate the current perception as "Risky" tend to prefer the presence of "Separate, prioritized bicycle paths (N = 173)" or "Traffic measures (N = 54)", while respondents who opted to consider the current perception as "Safe" had more an inclination toward "Presence of green environment (N = 132)". It is interesting to note that respondents who rate the current systems as "Safe" opt for using bicycles as a tool for direct interaction with nature. However, in the end, the distributions were evenly distributed with no significant p-value to cycling for risk perception. This may be noted in χ 2 (9) = 15.305, p = 0.083, Cramer's V value = 0.081, and Kruskal-Wallis H test value (3) = 5.333 and p-value = 0.149. Figure 3b shows that road users who rely more on a sustainable option of alternate travel tend to rate it as "Safe", i.e., bus/tram (N = 194), while the reliance on peer support (i.e., car use) is seen as a "Riskier" option (N = 152). This behavior may be because users are already adopted to sustainable modes of travel and prefer other sustainable travel modes as a reliable option.  Figure 3a shows that respondents who rate the current perception as "Risky" tend to prefer the presence of "Separate, prioritized bicycle paths (N = 173)" or "Traffic measures (N = 54)", while respondents who opted to consider the current perception as "Safe" had more an inclination toward "Presence of green environment (N = 132)". It is interesting to note that respondents who rate the current systems as "Safe" opt for using bicycles as a tool for direct interaction with nature. However, in the end, the distributions were evenly distributed with no significant p-value to cycling for risk perception. This may be noted in χ 2 (9) = 15.305, p = 0.083, Cramer's V value = 0.081, and Kruskal-Wallis H test value (3) = 5.333 and p-value = 0.149. Figure 3b shows that road users who rely more on a sustainable option of alternate travel tend to rate it as "Safe", i.e., bus/tram (N = 194), while the reliance on peer support (i.e., car use) is seen as a "Riskier" option (N = 152). This behavior may be because users are already adopted to sustainable modes of travel and prefer other sustainable travel modes as a reliable option. Figure 4a shows satisfaction toward mobility regulations, considering different risk levels. It is interesting to see that cyclists who rate current systems as "Risky" tend to choose "Overall convenience with care on dangerous intersections (N = 276, 79.53%)", but people who rate the current systems as "Safe" (N = 251, 70.30%) also tend to choose "Overall convenience with care on dangerous intersections" with small decline "Safe and cycle every day (N = 89, 24.92%)" and a small increase, with results as χ 2 (12) = 119.174, p = 0.002, Cramer's V value = 0.227, Kruskal-Wallis H test value (3) = 72.73, and p-value < 0.05. This eventually means that respondents feel that current systems are "Risky (N = 347)", and yet are cognitively evaluating mobility regulations, still foreseeing dangerous intersections. A possible explanation is that, since those regulations only consist of traffic-sign measures with no infrastructural changes, i.e., bicycle streets (where cyclists have priority over motorized road users), one-way streets and banning car entry. In Flanders, banning car entry to specific streets or areas was introduced to give more freedom and access to non-motorized road users, with an exception for motorized road users who are residents. If those regulations had had any infrastructural improvement, i.e., segregation, elevation, and reconstruction, then they may have resulted as more effective into cognitive recognition.  Figure 4a shows satisfaction toward mobility regulations, considering different risk levels. It is interesting to see that cyclists who rate current systems as "Risky" tend to choose "Overall convenience with care on dangerous intersections (N = 276, 79.53%)", but people who rate the current systems as "Safe" (N = 251, 70.30%) also tend to choose "Overall convenience with care on dangerous intersections" with small decline "Safe and cycle every day (N = 89, 24.92%)" and a small increase, with results as χ 2 (12) = 119.174, p = 0.002, Cramer's V value = 0.227, Kruskal-Wallis H test value (3) = 72.73, and p-value < 0.05. This eventually means that respondents feel that current systems are "Risky (N = 347)", and yet are cognitively evaluating mobility regulations, still foreseeing dangerous intersections. A possible explanation is that, since those regulations only consist of traffic-sign measures with no infrastructural changes, i.e., bicycle streets (where cyclists have priority over motorized road users), one-way streets and banning car entry. In Flanders, banning car entry to specific streets or areas was introduced to give more freedom and access to non-motorized road users, with an exception for motorized road users who are residents. If those regulations had had any infrastructural improvement, i.e., segregation, elevation, and reconstruction, then they may have resulted as more effective into cognitive recognition.
It is interesting to note that, in Figure 4b, a gradual shift in risk levels was noted from "Safe" to "Risky", reducing the responses (N = 24) for "Separation colored/dashed bicycle lanes". This indicates that the segregation of bicycle paths is associated with safety, which confirms the finding of Thomas and DeRobertis [40]. The majority choose "Separate elevated bicycle path (N = 121, 72.35%) as "Risky" as the highest indicator, with nonsignificant association χ 2 (9) = 5.085, p-value = 0.827, and no variation in medians for different risk levels with Kruskal-Wallis H test value (3) = 0.603 and p-value = 0.896. Figure 3a reveals that the motivation to cycle does not change with different risk levels. Respondents who rate the current perception as "Risky" tend to prefer the presence of "Separate, prioritized bicycle paths (N = 173)" or "Traffic measures (N = 54)". Figure 4b reveals suggestions from road users to increase safety, while considering different parameters. It shows how suggestions very cognitively considering risk perception. Here, "Risk" is associated with the presence of motorized users and its possible interaction resulting in large exposure. At the same time, "Safety" is associated with infrastructural improvement, independence, and authority.  It is interesting to note that, in Figure 4b, a gradual shift in risk levels was noted from "Safe" to "Risky", reducing the responses (N = 24) for "Separation colored/dashed bicycle lanes". This indicates that the segregation of bicycle paths is associated with safety, which confirms the finding of Thomas and DeRobertis [40]. The majority choose "Separate elevated bicycle path (N = 121, 72.35%) as "Risky" as the highest indicator, with nonsignificant association χ 2 (9) = 5.085, p-value = 0.827, and no variation in medians for different risk levels with Kruskal-Wallis H test value (3) = 0.603 and p-value = 0.896. Figure 3a reveals that the motivation to cycle does not change with different risk levels. Respondents who rate the current perception as "Risky" tend to prefer the presence of "Separate, prioritized bicycle paths (N = 173)" or "Traffic measures (N = 54)". Figure 4b reveals suggestions from road users to increase safety, while considering different parameters. It shows how suggestions very cognitively considering risk perception. Here, "Risk" is associated with the presence of motorized users and its possible interaction resulting in large exposure. At the same time, "Safety" is associated with infrastructural improvement, independence, and authority. Figure 5a shows that the majority choose "Infrastructure (N = 169, 48.70%)" as "Risky" as the highest indicator with nonsignificant association χ 2 (9) = 17.235, p = 0.045, Cramer's V value = 0.086, and no variation in medians for different risk levels with Kruskal-Wallis test value (3) = 6.836 and pvalue = 0.077. For this study, infrastructural features deemed "Risky" are considered to be the most critical factors for a modal shift. The above conclusions from Figures 4b and 5a confirm the conclusion of Akar and Clifton [41] that separate bicycle trails encourage people to cycle, while intervention programs and policy stimulate cycling [10]. Disaster, i will not consider cycling Needs improvement before I will consider cycling Not familiar with the new mobility plan Overall convenient but will cycle carefully at some dangerous intersections 16 Figure 5a shows that the majority choose "Infrastructure (N = 169, 48.70%)" as "Risky" as the highest indicator with nonsignificant association χ 2 (9) = 17.235, p = 0.045, Cramer's V value = 0.086, and no variation in medians for different risk levels with Kruskal-Wallis test value (3) = 6.836 and p-value = 0.077. For this study, infrastructural features deemed "Risky" are considered to be the most critical factors for a modal shift. The above conclusions from Figures 4b and 5a confirm the conclusion of Akar and Clifton [41] that separate bicycle trails encourage people to cycle, while intervention programs and policy stimulate cycling [10].      [42], who state that segregation of trails is positively associated with comfort levels and cycling rates since the investigated people were pro-cyclists (the enthused and confident). This may be explained since active people in Flanders cognitively consider cycling as a better option for accessibility, comparing it relatively with other active mobility options. This may be due to time savings, availability, and economical options being compared with public transport. While new mobility regulations might also play a significant role in the formation of this behavior, since recent measures give more priority and freedom to cyclists, this conforms with "Directness", as indicated in EU regulations [39,43]. Figure 6a shows what the critical factors are, when looking at infrastructure, which influence risk perception. Interaction between different road users, i.e., active and passive play a critical role in the formation of the "Risky" risk perception. This might be noted as χ 2 (12) = 30.88, p = 0.002, Cramer's V value = 0.115, and Kruskal-Wallis test value (3) = 1.467 and p-value = 0.69. This conclusion aligns with the "safety in numbers" effect by Fishman and Schepers [44], which states that the presence of cyclists increases safety and awareness, whereas other infrastructural features that directly influence cyclists' risk perception, i.e., "sharp curves" and "road width", are surprisingly considered less important. This conclusion is not in accordance with the belief that geometric features such as curvature and width are important to prevent accidents/injuries [45]. Geometry cannot be overlooked when designing intersections [19], intersection size, and bicycle lanes are positively associated with safety [46]. Features such as curvature, radius, width, and surface are considered to be critical for designing a cycling network and for comfort levels [18,47].  Respondents who rated current infrastructure as "Risky" opted for "Dangerous intersections (N = 185, 53.31%)" and "Hilliness (N = 72, 20.74%)". This means infrastructure, i.e., safety over intersections, is considered to be important. This may be noted in χ 2 (12) = 26.818, p = 0.008, Cramer's V value = 0.107, and Kruskal-Wallis test value (3) = 8.285 and p-value = 0.04. While a gradual shift was noted among respondents who rated risk perception as "Safe". A gradual increase was noted in the category "Hilliness (N = 117, 32.73%)". It is interesting to see that users find "Hilliness" cognitively as "Safe". This conclusion is in accordance with Strauss and Miranda-Moreno [29], who found that proximity toward intersections and the involvement of multiple types of vehicles causes accidents. To be noted is that only dangerous intersections have a mixed traffic involvement, which leads to increased exposure among active (motorized) or passive road users (VRUs), hence leading   Figure 6b reveals the obstacles leading people not to cycle, considering different risk levels. Respondents who rated current infrastructure as "Risky" opted for "Dangerous intersections (N = 185, 53.31%)" and "Hilliness (N = 72, 20.74%)". This means infrastructure, i.e., safety over intersections, is considered to be important. This may be noted in χ 2 (12) = 26.818, p = 0.008, Cramer's V value = 0.107, and Kruskal-Wallis test value (3) = 8.285 and p-value = 0.04. While a gradual shift was noted among respondents who rated risk perception as "Safe". A gradual increase was noted in the category "Hilliness (N = 117, 32.73%)". It is interesting to see that users find "Hilliness" cognitively as "Safe". This conclusion is in accordance with Strauss and Miranda-Moreno [29], who found that proximity toward intersections and the involvement of multiple types of vehicles causes accidents. To be noted is that only dangerous intersections have a mixed traffic involvement, which leads to increased exposure among active (motorized) or passive road users (VRUs), hence leading to an increased chance of involvement in maneuvering. Figure 7a reflects Figure 4b in a more visually representative form. Figure 7b shows that "Shorter distances" are also associated with "Safe" risk levels, and a gradual shift was also observed from "Risky (N = 72, 20.74%)" to "Safe (N = 111, 31.09%)". Moreover, a majority of participants (N = 246) rated "Coherent, effective infrastructure" systems as "Risky", considering situations where cyclists find it the most important. The respondents tended to opt for "Coherent, effective infrastructure" among all conditions risky or safe. Thus, this is primarily a significant predictor that cyclists find important. In Figure 7b, the majority choose "Coherent, effective infrastructure (N = 482 for total), (N = 246 for Risky)" with a significant association and variation in medians for different risk levels. This means infrastructure, i.e., safety over intersections, is considered to be important. This may be noted in This conclusion aligns with the conclusion of safe cycling shall be addressed by reducing the risk inside intersections [20], and good infrastructure supports cycling especially among adults [48].
Here, for this research, respondents consider infrastructural aspects as a critical feature w.r.t. qua "Perceived behavioral factors", "Obstacles", "Modal shift", and "Suggestions for improvement". This clarifies and validates the effect among the importance of "Infrastructure" among active mobility users in Flanders, Belgium. Moreover, a majority of numbers (N = 246) were rated as "Coherent, effective infrastructure", rating the current system as "Risky", considering situations where cyclists find it the most important.
In Figure 8a, the majority of those who rated current conditions as "Safe" had a response with "No effect" (N = 169), which explains quite well that users who are already in the habit of cycling frequently consider current systems as safe enough to cycle. The majority choose "No effect (N = 336 considering both risky and safe stimuli), (N = 169 for Safe)" with the significant association and no   This conclusion aligns with the conclusion of safe cycling shall be addressed by reducing the risk inside intersections [20], and good infrastructure supports cycling especially among adults [48].
Here, for this research, respondents consider infrastructural aspects as a critical feature w.r.t. qua "Perceived behavioral factors", "Obstacles", "Modal shift", and "Suggestions for improvement". This clarifies and validates the effect among the importance of "Infrastructure" among active mobility users in Flanders, Belgium. Moreover, a majority of numbers (N = 246) were rated as "Coherent, effective infrastructure", rating the current system as "Risky", considering situations where cyclists find it the most important.
In Figure 8a, the majority of those who rated current conditions as "Safe" had a response with "No effect" (N = 169), which explains quite well that users who are already in the habit of cycling frequently consider current systems as safe enough to cycle. The majority choose "No effect (N = 336 considering both risky and safe stimuli), (N = 169 for Safe)" with the significant association and no variation in medians for different risk levels. This may be noted in χ 2 (9) = 21.811, p = 0.009, Cramer's V value = 0.168, Kruskal-Wallis H test value (3) = 6.303, and p-value = 0.098. Not considering an elevated bicycle path as important (No effect = 169) means that cyclists are more comfortable sharing their space with other road users. This is strange since, previously, it was noted that cyclists are more comfortable in their independent personal space than in sharing their space, which makes them more exposed to other road users. Moreover, this also reverses the effect of safety in numbers. Respondents who consider current systems as "Risky" are also tilted toward more "No effect (N = 144)". In Figure 8b a mix response was received for features that motivate to cycle. A very mixed distribution was noted among users who rate systems as "Risky" and "Safe", whereas the majority tilted toward "Presence of comfortable, separate bicycle path".
The majority choose "Presence of separate, comfortable and prioritized bicycle path (N = 375 for total), (N = 173 for Risky level)", with no significant association and no variation in medians for different risk levels. This may be noted in χ 2 (9) = 9.115, p = 0.427, Cramer's V value = 0.063, and Kruskal-Wallis H test value (3) = 6.225 and p-value = 0.101.
After infrastructure aspects, the element that motivates users most to take a day out cycling is interaction with nature, which refreshes them and is considered a sign of attractiveness according to the manual [39]. It must be noted that "Attractiveness" is considered to have sub-determinants: infrastructure and traffic measures. Nevertheless, this study shows that cognitive sub-determinants are identified as a separate entity. Here "Attractiveness" is seen by riders as an interaction with nature, giving a sense of freedom, individuality, and leisure. History reveals humanity's continuous interaction with nature. With the new age of urbanism, cities such as Tokyo, Beijing, and Mumbai have seen continuous infrastructural development where interaction with nature was reduced, which is quite evident in large cities i.e., Kuala lumpur [24], which face the challenges of car dominace, Moreover, this also reverses the effect of safety in numbers. Respondents who consider current systems as "Risky" are also tilted toward more "No effect (N = 144)". In Figure 8b a mix response was received for features that motivate to cycle. A very mixed distribution was noted among users who rate systems as "Risky" and "Safe", whereas the majority tilted toward "Presence of comfortable, separate bicycle path".
The majority choose "Presence of separate, comfortable and prioritized bicycle path (N = 375 for total), (N = 173 for Risky level)", with no significant association and no variation in medians for different risk levels. This may be noted in χ 2 (9) = 9.115, p = 0.427, Cramer's V value = 0.063, and Kruskal-Wallis H test value (3) = 6.225 and p-value = 0.101.
After infrastructure aspects, the element that motivates users most to take a day out cycling is interaction with nature, which refreshes them and is considered a sign of attractiveness according to the manual [39]. It must be noted that "Attractiveness" is considered to have sub-determinants: infrastructure and traffic measures. Nevertheless, this study shows that cognitive sub-determinants are identified as a separate entity. Here "Attractiveness" is seen by riders as an interaction with nature, giving a sense of freedom, individuality, and leisure. History reveals humanity's continuous interaction with nature. With the new age of urbanism, cities such as Tokyo, Beijing, and Mumbai have seen continuous infrastructural development where interaction with nature was reduced, which is quite evident in large cities i.e., Kuala lumpur [24], which face the challenges of car dominace, deteriance, and sociophysological issues. Locally, in Western Europe, people have the opportunity to decide for themselves due to monetary and legislative freedom. Climate debates within developed countries led to populistic knowledge development, stimulating users toward interaction with nature. In Flanders mainly, cycling provides ample opportunity for accessibility, more individual transport, and more interaction with nature. Figure 9a shows mixed responses which are distributed very evenly among different risk levels. More respondents consider current systems as safe but still think significant improvements are needed, especially when looking at intersections. A strange contradiction was noticed among respondents who rated the current system as "Disaster, and do not consider cycling" but are still considering subconsciously current systems as "Safe". The conclusion in Figure 9a agrees with Visschers and Siegrist [49] that risk perception varies depending upon a psychometric paradigm shift related to elements such as dread and unknown risk. Thus, this strange notion is considered lying in a space between dread and unknown risk, explaining the difference in risk perception not knowingly, subconsciously, yet expressing cognitively and evolving inside. This is an interesting finding, but the reasons behind such a motive need to be explained. A possible explanation behind such behavior is that people tend to change their responses due to knowledge acquisition during the survey. The overall majority of respondents tended to express their appreciation of cycling with care, which has been linked to infrastructure, presence of other vulnerable road users, and TDMs. Figure 9b reveals the possibility of risk formation, considering different cognitive parameters that influence behavior. Figure 9b further helps in forming a general-awareness spectrum (considering attributes related to risk formation) for cycling potentials inside cities having the possibility to change the attitude toward stimulating its further use. The risk-perception spectrum is considered adequate and reveals promising variability among cyclists' cognitive risk formation. The risk spectrum offers as a platform for potential attitudinal persuasion techniques, considering elements that contribute to risk formation. Those elements were categorized cognitively within a tangible and non-tangible manner, i.e., policy, safety, and infrastructure. The elements were considered for research after an in-depth analysis of the literature. It can be noted that user attitudinal formation is a complicated procedure involving multiple features from a distinct context. As noted in the spectrum of risk, a mixed risk-attitudinal perception is formed, evolving from "Most Safe to Most Risky". It is a functional validation since it is evident that humans react differently to situations depending on their subjective feeling. By the end of the research, users tended to be satisfied with current conditions but still expressed the notion of improvement with regard to the new mobility regulations in Flanders. As stated previously, user risk perception tends to be lower in relation to user experience. Thus, users tend to be satisfied with new mobility regulations since they are already proficient cyclists.
In this cluster adequately only, those elements were evaluated by users who were considered from "Most Safe" to "Most Risky". The users were presented with diverse questions from multiple aspects, later identifying latent variables. Under the "Safe" perimeter umbrella, users consider opting for public transport when problems arise, whereas users consider the use of a personal car as "Risky", due to the fact users were already inclined toward a more sustainable modal choice. A smaller cycled distance contributes to lesser exposure to the environment, whereas users considered it as a critical element and rate it as "Safe". An inclined preference for lower cycling distance was noted for lessexperienced cyclists in the age category 18-25. The prominent effect of weather conditions makes cycling a less-comfortable experience, yet this eventually reduces the speed of vehicles, which increases the pupil's perception or recognition periphery. Hilliness also reduces the speed of the vehicle, thus providing more opportunities for vision, recognition, and understanding; therefore, hilliness for cyclists contributes to "Safety", while road width contributes toward better flow, reducing the chance of interaction and exposure. For cyclist's road width attribute contributes to "Safety". The general idea of cycling through a green environment comes with lesser exposure to traffic variables and more attention toward individuality. Thus, for a cyclist who considers cycling through the green environment to be "Safe", estimation of safety cannot be measured by interest or affinity for nature. It shows an affinity for cycling through nature is considered "Safe" by pleasure cyclists.
stimulating its further use. The risk-perception spectrum is considered adequate and reveals promising variability among cyclists' cognitive risk formation. The risk spectrum offers as a platform for potential attitudinal persuasion techniques, considering elements that contribute to risk formation. Those elements were categorized cognitively within a tangible and non-tangible manner, i.e., policy, safety, and infrastructure. The elements were considered for research after an in-depth analysis of the literature. It can be noted that user attitudinal formation is a complicated procedure involving multiple features from a distinct context. As noted in the spectrum of risk, a mixed riskattitudinal perception is formed, evolving from "Most Safe to Most Risky". It is a functional validation since it is evident that humans react differently to situations depending on their subjective feeling. By the end of the research, users tended to be satisfied with current conditions but still expressed the notion of improvement with regard to the new mobility regulations in Flanders. As stated previously, user risk perception tends to be lower in relation to user experience. Thus, users tend to be satisfied with new mobility regulations since they are already proficient cyclists. The investigated users were already proficient in cycling in terms of experience and exposure. A possible explanation behind this is that cyclists having higher cycling skills tend to adapt their behavior and avoid risky situations. Their subjective feelings to deal with such conditions were considered to be handled comfortably. The users who rate this situation not risky nor extremely safe, i.e., neutral or no effect on perception formation are age, income, policy, streetlights, police presence, road surface, more vigilance during busy periods, the effectiveness of infrastructure, and coherency dependent upon new Flemish mobility regulations. Cyclists tend to be more careful during busy period due to the dominance of motorized road users, which makes non-motorized road users uncomfortable. Strangely enough, this is considered neutral by them, meaning that cyclists are used to such a practice and cognitively assume such routines to be a normal part of their life.
The visual spectrum helps visualize risky attributes considered by cyclists. Cyclists see opting for a "Personal car" only as a second option attributing to risk, since they already use a sustainable mode of transport and consider car-space occupancy to be attributing to risk increases. This may be connected to the effect of the presence of motorized road users, as well as non-motorized road users. As stated previously, "Safety in numbers" plays a significant role in the perception of safety. This may be negatively or positively associated: negative in terms of presence of opposite types of road users, and positive in terms of the presence of the same type of road users. As noted previously, the infrastructure itself is a tangible attribute that forms risk perception. Attributes such as dangerous intersections, multiple users in unsegregated lanes, presence of motorized road users, current bicycle paths, current mobility regulations, and perception during busy periods are associated with "Risk" formation.
Finally, it is essential to visualize a country's perspective over risk perception in order to get one step closer to behavioral formation based on tangible and non-tangible parameters. This would eventually help policymakers design better cycling policies and infrastructure that help stimulate the desire for cycling. Risk-perception visualization is critical, as it is an essential step before an attitude and behavior can change. Visualization, in particular with VRUs, is peculiar and novel in accident identification, but this appears to be of great importance since it yields a comprehensive, in-depth understanding of perception formation. Risk-formation periphery, when considering cyclists, is of great use for decision-makers, psychologists, and engineers, as it helps them construct and mold new mobility policies and decisions. Policymakers have a positive inclination toward cycling, especially in the Benelux context. The visual illustration for cycling advocates for its supports identifies and justifies it lacks for risk identification. This will eventually help to mitigate strategies toward cycling-friendly, safe infrastructure.

Conclusions
This study associates a cyclist's behavioral risk formation with experience, infrastructure, stimuli, and sociodemographic elements. It elaborates on cyclist perception regarding tangible and non-tangible attributes. The conclusion of this study is interesting since it has been highlighted how risk formation evolves around personal attributes, peer pressure, hinderance, policy, infrastructure, and geometry. At the same time, the investigated attributes are associated with experience, peer presence, accidents, aggressive road-user presence (motorized road user), policy, geometry, attractiveness, distance, and weather conditions.
The cyclists responded to questions regarding safety issues. Experienced cyclists rated conditions as "Risky" due to their exposure rate, whereas distance is considered as a safety indicator among them. Large distances lead to higher exposure, hence resulting in more "Risky" stimuli. The presence of a green environment shifts the cognitive priorities of risk engagement connected with infrastructure, thus gradually shifting from "safety" toward "attractiveness". Current mobility regulations were rated as "Safe", but with much room for improvement; a possible explanation is that those new regulations did not possess infrastructural improvements, since if those included infrastructural improvements, the cognitive satisfaction levels would have altered. The respondents considered infrastructural changes to be a critical predictor of safety, i.e., lane segregation resulting in lesser exposure and elevation resulting in more visibility.
Moreover, higher segregation of lanes results in higher comfort levels, providing lesser interaction with other road users. The safety-in-numbers effect is dominant among experienced cyclists, while, surprisingly, geometrical features, i.e., curvature, width, and hilliness, are not associated with "Risk". The comfort of using a bicycle path, which depends on surface conditions, is not rated as a critical predictor for safety. This can be viewed as the fact that the majority of bicycle paths are paved with "Asphalt", hence confirming with [47]. Thus, based on previous factors, a significant discrepancy is observed between perceived and actual risk formation.
In this manuscript, it is highlighted how perception attributes structurally evolve around personality, environment, stimuli, and experience for cyclists. When visualizing how cyclists' perceptions form, the cycling commute is considered to be a distinct practice. Various represented elements revolve around personal or nonpersonal attributes, i.e., policy and societal acceptance. As a result, in this study, policies, peer presence, attractiveness, environment, and infrastructure were identified to predict their involvement at the societal level, while their involvement with materials, competences, and meanings exists. The study highlights how cycling practice generates "Risky" and "Safety" perception influence on modal choice. Besides, it has been discussed in detail how policies, experiences, and infrastructure affect the transformation of the image of cycling in an "Active" and "Passive" manner. The analysis and conclusion of the study reflect of a particular region. For future studies, this study might be cross-validated with a dataset of another region/country that has similar conditions. Expanding responses or cross-validation from the same population sample will induce bias since the conditions have dramatically changed.
The approach taken in this paper leads to implications that can act as a basis for further cyclist modal policy development. The results have implications regarding the understanding of infrastructural influences, proximity, environmental conditions, and societal roles. This stimulates or reduces the obstructions for cycling, as well as minimizes circumstances reducing cyclists' risks.