The Micromobility Tendencies of People and Their Transport Behavior

Abstract

Addressing transport in cities requires a change in people’s behavior and a better distribution of different transport modes’ performances—a change in the modal split. This article focuses on detailed research on the transport behaviors of residents and their attitudes towards possible changes. We developed a questionnaire and distributed it online and physically. The data came from an anonymous survey, and basic statistical methods and a correlation analysis were applied to them. One of the research tasks was to find the correlations between individual characteristics. The analysis showed that the respondents’ education influenced their opinions about transport behavior. The results showed that the most common means of shared mobility was bicycles. The paper contains detailed results regarding the use of private cars and transport behavior in general. In addition, the study presents other significant findings regarding the average number of vehicles in households, the types of vehicles, and their usage patterns. The results of our study are useful for practical applications, because they describe traffic behavior patterns and can improve future decision making and transport planning.

1. Introduction

Cities around the world are struggling with the negative externalities of car travel and are therefore trying to move towards a more sustainable urban transport system. Our city centers are full of vehicles, parking or driving. The increasing number of cars is a threat to pedestrians. Most vehicles have internal combustion engines which cause greenhouse gas emissions [1]. This trend is unsustainable from the point of view of both the environment and the lives of urban dwellers. The concept of micromobility makes it possible to counteract the harmful effects of urban development. The introduction and popularity of new modes of personal transport, such as e-scooters and electric bicycles, could accelerate this transition, as they become more common and are included in regulatory frameworks. Along with other measures, these modes of transport reduce air pollution [2,3]. The integration of the new transport modes can improve the accessibility of public transport and lead to a reduction in private car use [4]. Studying commuting patterns and transfers to public transport is essential to uncovering the full potential of micromobility [5].

Micromobility is currently entering a period of significant change, as new transport technologies provide new possibilities for movement.

We cannot discuss micromobility without mentioning smart cities, which means that these cities will be the key to and hub for building the new synergistic infrastructure we need for the 21st-century economy. It will be about data processing, sustainable energy, and the shared economy. Thus, it is a concept that represents a complex approach to the functioning of the urban region, which affects various social areas, such as culture, infrastructure, environment, energy, social services, and others. In each of these areas, interconnected goals create a system based on the principles of sustainable development [6]. The fulfillment of these set goals relies on the active involvement of public administration, the private sector, and the civil society entities within the system. This is the reason why there is currently no international legally binding definition for the given concept or a legal framework that would precisely regulate the procedure to achieve the desired state. Individual states follow their own “smart” concepts and methodologies under global documents dealing with the mentioned issue [7].

The smart city concept aims to help the overall economic growth of a city and the quality of its environment, thanks to the efficient functioning of all areas. Overall, this concept does not only apply to the use of smart technologies [8]. Figure 1 shows the smart city concept according to the literature [8].

Intelligent mobility is a current topic closely related to traffic management. Intelligent traffic management improves the traffic flow in cities and their surroundings. It also helps to address the future mobility revolution. Road capacity management must be holistic, predictive, and focused on real demand. Transportation engineers must reconcile multiple aspects, such as cost effectiveness, convenience, safety, and environmental protection. The prerequisite is data obtained from intelligent systems. The information obtained from these sources helps roads and transport routes to be used optimally and incentives to be created for passengers that help to improve traffic flows and mobility behavior—including solutions for safer and more convenient cycling. The result is an attractive city with excellent mobility options that is green and livable [9,10].

The smart city concept should introduce a comprehensive view of urban mobility, where micromobility is the solution to handling the disproportionate traffic load on urban infrastructure. But how to manage it? Today, we already know that urban mobility improvement requires positive and repressive measures.

Repressive measures mean traffic regulation and restrictions, such as, e.g., the creation of emission-free or pedestrian zones, city tolls, and parking regulations, etc. Some of these measures can be ensured thanks to progress in electric mobility [11,12,13]. Positive measures mean attractive urban public transport, where micromobility is an important part, as are the intelligent management of individual traffic, minimization of the time spent in traffic jams, and intelligent parking systems, etc. One of the goals of intelligent traffic management is to reduce the number of traffic accidents [14,15]. Without these two antagonistic solutions, we have no chance of success.

2. Literature Review

Micromobility, an innovative urban transport solution, aims at providing short-distance travel options, including first- and last-mile journeys. It provides a flexible, sustainable, cost-effective, and on-demand transport alternative [16,17], and reduces the dependence on private vehicles for short-distance travel [18,19]. Micromobility solutions include lightweight devices or mini-vehicles that operate at speeds typically not exceeding 45 km/h. This means that micromobility does not include car-sharing systems [20,21]. Micromobility has the potential to help in solving many of the transport-related problems that cities around the world currently face, and can potentially support a significant transition away from private motor vehicles. However, what exactly is micromobility? In lit. [19], the definition proposed by the International Transport Forum (ITF) based on vehicle kinetic energy, is: “...use of micro-vehicles: vehicles with a mass of no more than 350 kg (771 lb) and a design speed of no more than like 45 km/h. This definition limits the kinetic energy to 27 kJ, which is a hundred times less than the kinetic energy achieved by a compact car at top speed”. This definition includes both human-powered and electrically assisted vehicles such as bicycles, e-bikes, scooters, e-scooters, skateboards, one-wheel balance boards, and four-wheel electric micro-vehicles. Therefore, to facilitate the regulation of micro-vehicles with different characteristics, the ITF proposes four categories of micro-vehicle types based on their top speed and weight. More details on this definition, the types of micro-vehicles, and their classifications are in lit. [22].

Interesting facts about shared e-scooters are in [23]. The research review revealed that shared e-scooters predominantly displace short car trips, suggesting their potential to reduce single-occupancy vehicle usage and mitigate traffic congestion in urban areas. Surprisingly, the study found that shared e-scooters also complement public transportation, with users often choosing them for the first and last mile of their daily commutes, enhancing overall multimodal connectivity. Additionally, the analysis indicated a switch from ride-hailing services toward shared e-scooters for quick and cost-effective point-to-point travel, indicating a growing preference for micro-mobility solutions among urban commuters. The article [24] introduced the intriguing concept of evaluating the level of service for electric bikes in urban environments. It explored how traditional bicycle level-of-service metrics can be adapted and expanded to accommodate the unique characteristics and needs of e-bike users, providing valuable insights into optimizing cycling infrastructure for this emerging mode of transportation.

Thus, the main potential of micromobility in the urban context lies in solving the problem of the first and last mile, by improving access to public transport and thus increasing access to services and opportunities, similarly contributing to changes in mobility models and behavior [25,26,27]. The swift implementation of micromobility in urban areas not only encourages active modes of travel, but also caters to users’ needs, enabling individual and family transportation for short distances and facilitating parcel delivery in urban environments [28,29,30].

Large cities around the world have introduced various shared micromobility means as alternatives to private vehicles for short trips. The most widespread is bike sharing [31], which, however, also requires a significantly developed cycling infrastructure [32].

The last decade has brought many on-demand mobility options, including shared bikes [33]. These new modes, often grouped under the term micromobility, have the potential to address key aspects of sustainability, i.e., they can improve environmental sustainability by reducing the dependence on private cars. They also promise to address the social and economic disparities in mobility by providing reliable, affordable, and equitable transportation that connects with transit and other modes of transportation [34,35,36,37]. An important aspect of all the mentioned systems is also their safety and security, which is solved by the authors in [38].

Even the best transport system will not work if people do not use it. It is very important to convince the inhabitants of cities to effectively solve the ecological situation and reduce their consumption [39]. The Czech biologist, sociologist, and environmentalist Hana Librová, in her book [40], says, we quote: “An important role in this process of efforts to reduce personal material consumption is represented by the transformation of values: The preference of certain values and needs represents an essential component of the motivation of human behavior. If we understand the way of life very simply, as a sum of human activities, then an ecologically favorable way of life is the result of a radical change in human values”.

The preferences of certain values do not necessarily manifest themselves in the practical aspects of life. Nevertheless, it is crucial to contemplate how individuals perceive suggestions for behavioral changes. Specifically designed lifestyle adjustments might be met with resistance and less willingness compared to general appeals to transform the values of the entire society. The possibility of saving humanity ecologically is in the sharing economy, which goes hand in hand with the introduction of micromobility in cities [28,29,30].

Source [41] describes three important principles that drive the shared economy:

• Unused things lose value (the unused potential of empty seats in the car and the possibility of obtaining a passenger).
• Access to things is better than owning them. Today’s generation of consumers is less interested in ownership and prefers to rent or lease things to suit their flexible lifestyles. Ease of access also reduces other barriers to using products (such as not having a driver’s license or not having enough money for an expensive train ticket) and it promotes economic flexibility.
• Trust: globalization has paved the way for a tightly connected business world. Over time, internet social networks have contributed to building a virtual reputation that makes it easier for us to work with people we do not know.

It follows from the above that we must change the urban environment, to ensure the freedom and safety of movement for pedestrians in city centers, to allow cyclists to move safely and smoothly, to provide adequate comfort for public transport passengers at a reasonable price, and to allow drivers to quickly find free parking spaces in city centers and pay a higher price. Simply explained: those who share a vehicle or make a physical effort will pay less for transportation or even not pay [29,30].

In Slovakia, the opinion that having a car is a status of social prestige still prevails. Even advertising for new vehicles subtly influences people’s opinions, such as the safety of the whole family, the silence and peace that occur while driving, the intoxicating feelings that occur when controlling a perfect machine, and the hope that we can achieve everything we want thanks to a car. On the other hand, we do not want to use public transport, which is full of people, discomfort, noise, and sometimes can also smell [30,42]. In this context, bicycles are even worse—strenuous pedaling, no roof, no air conditioning, exhaust gases, and no airbags or deformation zones; thus, there is a fear for one’s safety. From this point of view, suddenly waiting at light-controlled intersections and driving slowly in traffic jams stops bothering us. The combination of comfort, security, and the illusion of freedom wins. Nowadays, there is also talk about autonomous vehicles, which could improve comfort and safety even more [43,44,45].

Motivating people to switch from cars to alternative modes of transport is a big challenge [46,47,48,49,50,51,52]. However, the condition for this is modern infrastructure, which we do not have in Slovakia. Based on the above, we established the following hypotheses:

H1. 

Respondents are aware of the need to change their traffic behavior. The condition for confirming this hypothesis is an average rating higher than 3.00.

H2. 

Changes in traffic behavior are influenced by level of education. The condition for confirming this hypothesis is a correlation coefficient between the mentioned variables of at least 0.50 at the 0.01 significance level.

H3. 

There is a dependence between the will to change traffic behavior and the possibility of obtaining benefits from an employer. The condition for confirming this hypothesis is a correlation coefficient between the mentioned variables of at least 0.50 at the significance level of 0.01.

3. Methodology and Data

Mobility is intricately linked to the wider social and cultural configurations of everyday life, including power relations, equality, inequality, and the spatial relations between people, places, and opportunities.

It follows from the above that it is necessary to change the traffic behaviors of people throughout society. We decided to ask the residents of Žilina city on their opinions on traffic behavior. With population of 81,385 inhabitants (1 January 2022), Žilina is the fourth-largest city in Slovakia [53]. It is the administrative, economic, transport, and cultural center of northwestern Slovakia.

We compiled the questionnaire very simply. Long-term experience shows that, the longer the questionnaire, the lower the returns or willingness to answer. The questionnaire consisted of 12 questions focused on the respondents’ sociological data, daily mobility, their vehicles, and their opinions on changing traffic behavior.

The online questionnaire collected answers from January 2022 to March 2022 (a total of two months). Our interviewers collected answers physically from April to May 2022. We received 255 responses to the online questionnaires. We targeted residents on the streets of the city. Of the 300 people contacted, 171 answered, which was 57% of the respondents. In total, we evaluated 426 questionnaires.

The methodology consisted of six steps for fulfilling the research objectives, such as determining the sample size, establishing the research hypotheses, compiling a scientific questionnaire, adjusting the database by unifying the categories of the categorical variables so that they met the minimum frequency, analyzing the relationship between the quantitative variables, and interpreting the results [54].

The questionnaire included 29 questions divided into three sections: socio-demographic characteristics, the frequency of day-to-day activities a week, and shopping preferences. We only used a subset of the total subset for this article. The sample size was determined according to Formula (1) [55,56,57]. The result demonstrated the minimum number of respondents.

$$\mathrm{minimum}\mathrm{number}=\frac{\frac{{\mathrm{z}}^2\times \mathrm{p}\times \left(1-\mathrm{p}\right)}{{\mathrm{e}}^2}}{1+\frac{{\mathrm{z}}^2\times \mathrm{p}\times \left(1-\mathrm{p}\right)}{{\mathrm{e}}^2\times \mathrm{n}}}$$

(1)

where

• n: the sample size was 81,385 inhabitants in Žilina [22];
• z: the critical minimal value for 95% confidence level was equal to 1.96;
• p: the estimated proportion of the population that had the attribute in question (0.5 was recommended for unknown values);
• e: the margin of error was equal to 0.05.

We found that the minimum sample size consisted of 384 respondents according to Formula (2).

$$383=\frac{\frac{{1.96}^2\times 0.5\times \left(1-0.5\right)}{{0.05}^2}}{1+\frac{{1.96}^2\times 0.5\times \left(1-0.5\right)}{{0.05}^2\times 81385}}$$

(2)

Methodology of Traffic Survey

This sociological traffic survey was intended to be as reliable as possible, and therefore relied not only on online responses, but also on physical interactions. When in physical contact with the respondents, the interviewers asked the questions out loud, so it was basically a guided interview. The electronic questionnaire, prepared in Google Forms (Figure 2), was distributed to the respondents using email and other communication channels.

Students of the University of Žilina physically conducted a questionnaire survey on the streets of Žilina. In this case, they were instructed to have the respondents write out the questionnaire so that they could read it themselves. The interviewer’s tasks were only to clarify some ambiguities on the part of the respondents and to answer supplementary questions. Only in the event that the respondent was unable to read the text were they authorized to read the questions and answers to the respondent, and then, according to the respondent’s instructions, fill in the answer for them.

The analysis of the results used basic descriptive statistics, including the arithmetic mean, variance, standard deviation, and range. Additionally, we employed a correlation analysis to examine the relationships between the variables under investigation. These statistical tools provided valuable insights into the data patterns and interrelationships, contributing to the comprehensive analysis of our study’s findings. Software Microsoft Excel v16.0 (Microsoft 365) was used for the statistical evaluation in this study.

4. Results

The questionnaire survey started with questions about the respondents’ demographic data. In the first question, we ascertained the gender of the respondents. We secured relatively balanced answers, i.e., 210 women (49%) and 216 men (51%). The gender composition of the respondents is shown in Figure 3.

Regarding the age of the respondents, it had the following characteristics:

• a minimum value of 15 years and a maximum value of 80 years,
• a variation range of 65 years,
• an average age of 39.92 years,
• a standard deviation of 14.87 years.

While conducting the survey, the interviewers addressed respondents over 15 years old. It follows that the respondents’ portfolio included high school students (9.9%) as the youngest people. Among adults, there were disabled pensioners (1.9%), unemployed residents (5.6%), pensioners (7.5%), and university students (13.6%) in the survey. Almost 62% of the respondents were economically active (employed). Figure 4 shows detailed results in absolute numbers.

This sociological traffic survey aimed to find out information about the mobility of individuals. It did not deal with exact property relations. Therefore, as part of the next question, we focused on the number of members in the respondent’s household and the number of vehicles available to that household. All the respondents were part of households consisting of from 1 to 7 members. The average number of members in one home was 3.47. The examined homes were most equipped with bicycles without their own propulsion. On average, there were 0.65 bicycles and 0.36 vehicles per one household member. The typical means of transport were also electric bicycles (0.13), scooters (0.08), electric scooters (0.07), and motorcycles (0.05). N1 category trucks (vans) were rarely found in households. A graphic overview of the answers is in Figure 5.

Subsequently, in the questionnaire, the respondents had to identify how essential individual aspects of the means of shared mobility in the city were for them. We used a Likert scale from 1 (least important) to 5 (most important) to rate this question. The aspects included in the questionnaire were the following:

• Costs (mob_price): this is a fundamental economic factor, which includes not only the price of buying or renting a given means of transport, but also the fuel/energy costs necessary for the vehicle’s operation.
• Safety (mob_safety): perceptions of safety can impact transportation choices. People may prefer modes of transport that they perceive to be safer, such as well-maintained public transportation systems or driving their own vehicles.
• The speed of a vehicle or its relocation time (mob_speed): time is an essential factor in transportation decisions. Some individuals prioritize speed and prefer faster modes of transportation, such as cars or planes, to reach their destinations quickly. Others may be more willing to trade off speed for other factors, such as cost or environmental considerations.
• The convenience of transportation: even though riding a bicycle within a city can be similarly time-consuming, safe, and inexpensive, the choice of a suitable means of micromobility also depends on the comfort factor. Especially in cold winters, the need for comfort and thermal comfort affects the choice of means of transport.
• Place for putting down/parking: like the availability of the means of transport, the availability of a place for putting it down/parking affects the respondents’ decision making.
• Infrastructure: the availability and quality of infrastructure is a significant factor that influences the choice of the means of micromobility. While sidewalks for pedestrians and roads for cars are standard, infrastructure for cyclists is lacking in eastern European cities.
• The availability of means: if the respondent does not have a given means of transport available, they cannot even use it. The availability of vehicles is an crucial factor and is also related to costs, especially the costs of procuring a means of transport.

The results of this part of the questionnaire were not surprising. The evaluation showed that the most important factor was costs or price (4.19, SD = 0.94) and also relocation time (3.98, SD = 1.24). Approximately the same scores with similar variances were obtained by three factors, namely: a place to drop off/stop the means of transport (3.65, SD = 1.11), the convenience of transportation (3.64, SD = 0.95), and infrastructure (3.60, SD = 1.07). The respondents indicated the availability of the means as the second-least important factor (3.34, SD = 0.97). In the next part of the questionnaire, we identified the traffic behavior of the respondents with five simple statements. The rating was based on a Likert scale from 1 (absolutely disagree) to 5 (completely agree):

• I am aware of the importance of changing traffic behavior (3.46, SD = 1.28).
• I will never change my traffic behavior (2.41, SD = 1.17).
• When choosing a means of transport, the ecological point of view is also important to me (3.22, SD = 1.01).
• Commuting by car does not improve my physical condition (3.29, SD = 1.31).
• Benefits from the employer (school) would convince me to stop commuting by car (3.42, SD = 1.29).

In the initial part of the questionnaire, we dealt with the personal data on the respondents’ ages or educational attainment. We further used this information in the correlation analysis.

Correlation Analysis

Correlation analyses are necessary for understanding the connections between variables [59]. We can expect relatively low values of correlation coefficients in psychological traffic research. According to [60], their interpretation depends on context. In physics, a correlation coefficient of 0.8 is very low; on the contrary, in the social sciences, it is a very high value. In 1988, Cohen established exact definitions for the values of correlation coefficients in psychological research. These descriptions are in the literature source [61].

The correlation coefficient measures the two-tailed linear dependence of two variables and takes values from the interval 〈−1;1〉. The following implications apply to the correlation coefficient:

r_xy = 0 ⇔ variables X and Y are not linearly dependent,

r_xy > 0 ⇔ there is a direct linear relationship between the variables X and Y,

r_xy < 0 ⇔ there is an indirect linear relationship between the variables X and Y.

The sign of the correlation coefficient determines the direction of the dependence. The absolute value of the correlation coefficient reveals the strength of the linear association between two variables. The closer the absolute value is to 1, the stronger the dependence.

$$r_{xy}=\frac{n\sum xy-\sum x\sum y}{\sqrt{\left[n\sum x^2-{\left(\sum x\right)}^2\right]·\left[n\sum y^2-{\left(\sum y\right)}^2\right]}}$$

(3)

The correlation coefficients, calculated according to Formula (3), can be displayed in a simple matrix. It is symmetrical because the order of the factors in the correlation analysis does not matter.

Table 1. Absolute and relative accuracy of the OUT sensor (unit: vehicles). Source: authors.

Factor	Age	Education	Opinion Importance	Opinion Change	Opinion Ecology	Opinion Health	Opinion Benefits
Age	1	0.335 **	0.181 **	0.260	−0.072	−0.079	−0.107 *
Education		1	0.532 **	0.424 **	0.238 **	0.139 **	0.179 **
Opinion importance			1	0.454 **	0.312 **	0.230 **	0.128 **
Opinion change				1	0.119 *	0.128 **	0.115 *
Opinion ecology					1	0.478 **	0.286 **
Opinion health						1	0.395 **
Opinion benefits							1

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

shows not only the correlation coefficients, but also the significance level of the correlation coefficient.

As can be seen from Table 1, the correlation coefficients were not high. Almost zero dependence was calculated between age and two factors, considering the ecological and health points of view (physical condition). A small correlation (from 0.1 to 0.3) emerged between the whole range of the pairs of variables. A weak indirect dependence was revealed between the age of the respondent and their opinion about the possibility of benefits from an employer. Other dependencies were directly proportional. Medium dependencies (from 0.3 to 0.5) were found six times in the matrix. With an increasing level of education, people were more open to progressive changes in traffic behavior. A dependence of 0.424 at the significance level of 0.01 was found between education and willingness to change. If people were aware of the importance of changing their traffic behavior in the future, they were mostly also willing to change this behavior, which was shown by the correlation coefficient of 0.454 at the significance level of 0.01. The respondents’ views on ecology and health were also related, with a correlation coefficient of up to 0.478 at the 0.01 significance level. Only in one case did we calculate a correlation coefficient that we could call high. This was the case of the dependence on respondents’ education and their awareness of the importance of changing their traffic behavior.

Moreover, we examined the influence of households’ car ownership on the assessment of individual factors. It was assumed that if a household did not have a personal car, it was because of the high costs associated with owning a personal car, or there may have been other reasons. The costs of owning a car can vary depending on various factors, such as the type of vehicle, its age, location, insurance rates, maintenance needs, and individual driving habits. Here are some typical costs associated with owning a car:

• Purchase price: the initial cost of buying a car is the most significant expense. It can vary widely depending on make, model, age, condition, and additional features or options.
• Depreciation: cars generally lose value over time due to depreciation. The depreciation rate depends on the model, mileage, and overall condition of the vehicle.
• Financing: external financing costs depend on the purchase price, down payment, loan term, and interest rate.
• Insurance: car insurance is a legal requirement in most places. These insurance costs can vary based on the owner’s age, driving history, location, type of car, and coverage options. Generally, newer and more expensive cars tend to have a higher insurance.
• Fuel: the fuel costs depend on the vehicle, fuel efficiency, and local fuel prices.
• Maintenance and repairs: these costs encompass expenses incurred for regular upkeep, servicing, and vehicle repairs. They are necessary for a vehicle’s operation.
• Registration and licensing: this category includes the fees associated with registering a vehicle with the appropriate authorities and obtaining a license for legal car operation.
• Taxes: this refers to the various taxes levied on car owners, such as vehicle excise tax, sales tax, or road tax, which contribute to the public funds for maintaining transportation infrastructure.
• Parking and tolls: this encompasses the costs associated with parking a vehicle in designated areas or paying toll fees for using toll roads or bridges.
• Additional costs: this category covers various supplementary expenses, such as insurance premiums, fuel costs, depreciation, and other unforeseen expenditures related to the ownership and operation of a vehicle.

Figure 6 shows that, with a higher number of passenger cars, there were considerations of price or the respondents’ mobility costs. When it came to security, the results were roughly even. For respondents equipped with personal vehicles, the time aspect, i.e., the speed of relocation, was important. Logically, the possibility of parking was much more significant for people who owned one or more cars. In terms of questions related to the availability of infrastructure and the availability of a means of mobility, no significant dependence was observable here. There was a relatively high correlation between the number of personal motor vehicles in a household and the frequency of their use, up to 0.556 (correlation was significant at the 0.01 level, 2-tailed).

The questionnaire focused not only on the number of personal vehicles in a household, but also on the frequency of their use. The resulting average value was 2.54. In general, respondents use vehicles once to several times a week. The questionnaire also investigated the purpose or purposes for which people use a bicycle or scooter. Detailed results are in the graph in Figure 7.

People use bicycles for various purposes, but some of the most common reasons include:

• Commuting: many individuals use bicycles as a means of transportation to commute to work, school, or other destinations. Bicycles can be a cost-effective and eco-friendly alternative to cars or public transportation for shorter distances.
• Sport: bicycling is an excellent exercise that provides cardiovascular benefits, strengthens muscles, and improves overall fitness. People often ride bicycles for leisure, recreational activities, or as part of their fitness routine.

People use scooters for similar purposes, but usually those with electric drive. Another question was also related to the popularity of individual types of shared mobility means. The respondents indicated which means of shared mobility they had experience with:

• Up to 90 respondents, 41.3%, had no experience with any means of shared mobility.
• The most important was shared bicycles, which had already been used by 91 (41.7% of respondents).
• Electric scooters were also popular, with which 35.3% of respondents (77 respondents) had experience.
• A total of 37 respondents (17%) had encountered shared electric bicycles and 28 (12.8%) respondents had encountered car sharing.

In our survey, the respondents could state that they used bicycles for several purposes. Therefore, the percentage expressions in the following lines do not add up to 100%. Only 36 respondents (8.45%) used a bicycle or scooter to get to public transport, a bus, or a train stop. Shared scooters and bikes enhance public transport accessibility in many Slovak cities. For example, Žilina offers classic shared bicycles but also electric scooters. Slovakia has only one city that also provides shared electric bicycles.

When commuting by bicycle to a public transport station or stop, it is necessary to have a suitable place to store the bicycle. Not having one is problematic due to security (bicycles are often targets of theft). Therefore, shared bikes are more suitable for this purpose. They are tracked using GPS and can be stored at points usually located near bus and train stations. Their frequent concentration in one place is problematic, which causes, for example, there to be a lot of shared bikes near train stations in the afternoon, but no bikes in the morning.

As for other results, 9.86% of the respondents did not use bicycles. In total, 13.15% of the respondents used a bike or scooter to carry out personal matters (shopping and visiting, for example). A total of 19.95% of the respondents used a bicycle to commute to work or work. Various campaigns, “To work by bike,” for example, enhance bike usage. The introduction of shared bicycle and scooter systems has also helped in this area. The respondents in our survey most often used bicycles or scooters for entertainment and sports. We received up to 185 such answers—43.43%.

5. Discussion

Micromobility represents a shift in urban transportation, offering flexible, environmentally friendly alternatives to traditional modes of transportation. It encompasses a range of options such as electric scooters, bicycles, and shared mobility services. Of course, all means of micromobility also require infrastructure and good technical conditions [62], especially e-vehicles [63].

Micromobility has the potential to alleviate the traffic congestion in urban areas. Micromobility options are efficient means of transportation for covering short trips, which make up a substantial portion of urban travel. By integrating these modes into existing transportation systems, cities can provide last-mile connectivity and reduce the reliance on private vehicles. Interestingly, the efforts of European cities have a higher likelihood of substituting short trips traveled by public transport or cars. Residents can undertake these trips using electric scooters suitable for covering shorter distances (shorter in Europe than in America). These insights are from article [23], which compares various studies exploring electric scooters. However, careful planning and infrastructure considerations are necessary to ensuring their safe and seamless integration [64].

Micromobility holds promise for reducing greenhouse gas emissions and improving air quality. Electric scooters and bicycles produce minimal or no emissions during their operation, making them environmentally friendly alternatives [64]. Shared micromobility services can also lead to a reduction in the overall number of vehicles on the road. Electric bicycles, according to [24], have the potential to replace passenger cars. However, life cycle assessments considering the environmental impact of manufacturing and disposal are essential for a comprehensive evaluation of their sustainability benefits.

Advancements in technology play a vital role in the growth of micromobility. Electric-powered vehicles with an improved battery efficiency and range have made micromobility more accessible and convenient. The integration of intelligent technologies, such as GPS tracking and mobile applications, has enabled efficient fleet management, user-friendly interfaces, and enhanced safety features [65,66]. Ongoing research and development in materials, propulsion systems, and connectivity will further enhance the performance and reliability of micromobility options.

Safety is a critical concern in the widespread adoption of micromobility. The compact size and vulnerability of micromobility vehicles pose unique risks, necessitating appropriate safety measures. Helmets, dedicated infrastructure, and public awareness campaigns are essential components of ensuring the safety of riders and pedestrians. Additionally, regulatory frameworks are necessary to address the problems related to vehicle standards, parking, liability, and user behavior.

In our article, we found that the respondents were aware of the need to change their traffic behavior. Over half of the respondents agreed with the statement about the importance of changing traffic behavior. The average score on the Likert scale was 3.46. Therefore, we can confirm hypothesis H1.

Furthermore, we used a correlation analysis to find out whether level of education affected changes in traffic behavior. This hypothesis can also be accepted. The correlation coefficient between the mentioned variables was higher than 0.50, i.e., 0.532 at the 0.01 significance level.

The last hypothesis we tested was H3: there is a relationship between the will to change traffic behavior and the possibility of receiving benefits from the employer. The correlation between these variables was only 0.115 at the significance level of 0.01. It follows that, if the respondents expressed that they were willing to change their traffic behavior, it did not mean that they would condition this change on a benefit from their employer.

There are several limitations to this study. While the analysis revealed some fundamental principles of using electric mobility modes, it did not propose specific solutions for improving their current state. Future research should focus on the exact (for example, bike-sharing) system and analyze geolocation data to assess and predict user behavior patterns. However, some operators (private companies) may resist such analyses. Another limitation is the questionnaire, as false responses could undermine the researchers’ efforts. Analyzing micro-mobility modes presents challenges, since the precise numbers of bicycles, electric bikes, and scooters are not always known, unlike registered personal motor vehicles. Additionally, the questionnaire’s shortness limited the exploration of other aspects of the respondents’ mobility, such as trip distances, willingness to transfer, local issues, and more, which should be addressed in future research.

6. Conclusions

The future of micromobility holds significant promise. With ongoing research and innovation, advancements in battery technology, vehicle design, and connectivity will further enhance the viability and efficiency of micromobility options. Integrating them with other emerging technologies, such as autonomous vehicles and Mobility-as-a-Service (MaaS), can lead to integrated and multimodal transportation systems. However, the realization of these prospects requires collaborative efforts among policymakers, urban planners, industry stakeholders, and researchers.

In our research study, we gathered significant insights into the traffic behaviors of city residents. These findings hold valuable implications for traffic planning and the strategic development of individual and shared mobility systems in urban environments. In this study, we found that the most prevalent means of transportation among households was not the automobile, but the bicycle. We confirmed that the perceived safety of the mobility mode was scarcely noticeable, with respondents rating it as the least important among the evaluated factors. Conversely, decisive factors were the cost and speed of transportation. While people recognized the importance of changing their transportation behavior, their willingness to make concrete changes was problematic.