Do cycling facilities matter during the COVID-19 outbreak? A stated preference survey of willingness to adopt bicycles in an Indonesian context

Abstract

This study aims to evaluate to what level bicycles can replace motorized vehicles during the outbreak. The survey respondents were asked to choose between a bicycle and existing motorized vehicles for seven choice scenarios based on traffic congestion and bicycle infrastructure. This study integrated a multiple-indicator, multiple-cause (MIMIC) and a mixed logit model to examine the attitudes change caused by the outbreak and the social influences on the preference for bicycles. The results indicated that developing bicycle infrastructure encourages the adoption of bicycles, although most users tend to come from low-income and less-educated people. Based on the MIMIC model results, this study found significant differences in attitudes change and social influences across respondents’ socioeconomic characteristics, as male respondents were more likely to be easily influenced by their friends with respect to cycling than females. Attitudes change related to global warming and environmental consciousness also affected the preference for bicycles.

1. Introduction

The COVID-19 outbreak strongly affected the transportation sector, as mobility was discouraged in order to reduce the disease spread. The number of trips decreased dramatically (Aloi et al., 2020; Hotle and Mumbower, 2021; Irawan et al., 2020a; Zhang and Fricker, 2021), and the use of public transport was reduced in favor of private vehicles (Abdullah et al., 2020; Bhaduri et al., 2020; Das et al., 2021). During the outbreak, cycling has emerged as a desirable mode of transport to support public health and reduce the risk of COVID-19 transmission (Fuller et al., 2021; Musselwhite et al., 2020). Studies have shown that cycling increased significantly during the outbreak (Jobe and Griffin, 2021; Li et al., 2021; Padmanabhan et al., 2021). Some European cities have implemented dedicated bicycle lanes to encourage bicycle use during COVID-19 lockdowns (Kraus and Koch, 2021). However, de Vos (2020) stated that people used bicycles during the outbreak for their well-being and health rather than for transportation. One study also found that the increase in cycling during the outbreak did not result in mode switching from private motorized vehicles to bicycle use (Fuller et al., 2021).

As in other countries, cycling has become a popular activity during the outbreak in many cities in Indonesia (Febrianto et al., 2022). Despite the lack of data on the growth of the number of cyclists in Indonesia during the outbreak, Ramdani (2020) stated that sales of Polygon Bike, one of the popular bicycle brands in Indonesia, increased up to 1036% since early March 2020. As many cities worldwide have experienced a boom in cycling during the COVID-19 outbreak (Bagdatli and Ipek, 2022; Hong et al., 2020; Nguyen and Pojani, 2022), an agenda promoting people to shift from motorized forms of transport to cycling should be addressed. However, it is necessary to understand people's preferences in using a bicycle to produce suitable policy formulations during the outbreak.

This study aims to understand the willingness of bicycle adoption as a transport mode during the outbreak by considering the function of bicycle lanes, bicycle parking facilities, bicycle boxes, and traffic congestion. This study also attempted to reveal how social influences and attitudes change caused by the emergence of COVID-19 affected the preference for bicycles. The empirical investigation used data from a stated preference (SP) survey in Yogyakarta. Multiple-indicator, multiple-cause (MIMIC) was applied to determine the effect of socioeconomics on attitude change in response to the presence of COVID-19. The Mixed Logit (MXL) model was then applied to find the influence factor of the willingness to adopt bicycles during the outbreak, including cycling infrastructure, socioeconomics, motorized vehicle ownership, attitudes, and social influence.

The rest of this article is divided into several sections. The following section is a literature review outlining determinants of cycling and practical experiences in the field. Section 3 describes the study area. Section 4 addresses the study's research methods. Section 5 reveals the data and empirical findings, as well as discusses the findings. Last, Section 6 presents some concluding remarks, policy recommendations, and research directions for future studies.

2. Literature review

Previous extensive studies have investigated the relationship of various factors or variables influencing bicycle choice, in particular the role of the built environment and road infrastructure. The findings of cross-sectional research reveal a significant correlation between cycling rates and the availability of bike lanes and paths (Buehler and Pucher, 2012; Clark et al., 2021; Karpinski, 2021; Parkin et al., 2008; Pearson et al., 2022). Based on a sample of 37 Local Government Areas (LGAs) in the state of Victoria, Australia, Pearson et al. (2022) found that the majority of Victoria inhabitants are motivated by cycling, but only if the protected infrastructure is available. Karpinski (2021) also found that the bike-share ridership of Boston's bicycle sharing system tripled on routes using the dedicated bike lane after its installation. Using a differences-in-differences analysis, which assumes that the bike lane had no effect on neighboring routes, reveals that the additional bike lane caused an 80% increase in bike-share ridership on relevant routes. Findings indicate that the provision of segregated bicycle infrastructure is essential for the safety promotion of low-risk bicycling environments (Harris et al., 2013). The additional finding suggested that the influence of the bike lane is most significant when trip origins and destinations are less than 1.6 km from the bike lane, which may be important information when designing bicycle routes.

In terms of addressing safety in cycling, Dill et al. (2012) investigated the impact of bike boxes at ten signalized junctions in Portland, Oregon. They found that the frequency of observed conflicts at the bike box locations dropped significantly after its installment. Even when the sample was restricted to responders who were not also cyclists, a greater proportion of motorists believed that bike boxes made driving safer rather than more dangerous. Three-quarters or more of the cyclists surveyed thought that the boxes made the junction safer.

Controlling for other determinants of cycling, before-and-after studies found increased levels of cycling after the installation of bicycle parking facilities. The availability and quality of bicycle parking tend to be a predictor of cycling among current and potential cyclists (Heinen and Buehler, 2019). Buehler (2012) examined the role of bicycle parking as a determinant of bicycle commuting. The study found that bicycle parking is associated with higher levels of cycling to work. Employees with access to both bike parking and cyclist showers at work are more likely to cycle to work than those with access to bike parking but no showers. Further, bike parking is often analyzed as a component of the integration of bicycles and public transportation. The effective coordination of public transit and bicycle parking can expand the public transportation coverage area, minimize the need to operate feeder (bus) services, and boost the demand for bicycling and public transportation (Krizek and Stonebraker, 2011). Commuters in California are more willing to cycle to stations with the availability of bike parking (Appleyard and Ferrell, 2017). Additionally, the presence of bicycle parking in Denmark raised the likelihood of cycling to a station by a factor of 2.5 (Halldórsdóttir et al., 2017).

Previous studies also reveal that traffic condition is an essential variable in estimating the potential demand for cycling. Among the top motivators of cycling, routes away from traffic noise and pollution are associated with the regular, frequent, occasional, and potential cyclists in Metro Vancouver, British Columbia (Winters et al., 2011). Foster et al. (2011) also found that bicyclists tended to prefer lower-traffic-volume roads. In an emerging cycling city such as Brisbane, Australia, the cyclists see mixed traffic infrastructure layouts as less safe, experience greater traffic anxiety, and are more likely to abandon cycling as a means of coping, while the cyclist in an established cycling city such as Copenhagen, Denmark, are less likely to wear helmets and are more likely to cycle while preoccupied (Chataway et al., 2014).

From the above studies, it can be concluded that the presence of bicycle-friendly physical infrastructure and cycling networks has considerably impacted bicycle-related decision-making. Furthermore, studies have also found that the provision of bicycle infrastructure during the outbreak stimulated bicycle use, although this facility did not increase the riding safety for cyclists because roadways were safer as a result of the lockdown's unprecedented decrease in traffic (Budd and Ison, 2020; Jobe and Griffin, 2021; Kraus and Koch, 2021; Nikitas et al., 2021). Previous studies also conclude that sociodemographic factors such as age, gender, income, and education significantly influence the willingness to cycle. In car-oriented countries, studies found that female and elderly people appear to cycle much less than male and young/middle-aged populations (Heesch et al., 2012; Heinen et al., 2010). In addition, personal perception regarding attitude and social influence also plays a role in the propensity to cycle (Ortiz-Sánchez et al., 2022; Piras et al., 2021).

However, it can also be seen that few studies integrate cycling infrastructure and perception variables. Excluding a particular set of variables might possibly skew the findings. Therefore, this study aims to address this gap in the literature by integrating the MIMIC and MXL models to capture the impact of epidemiological and social influences on preference for bicycles within the framework of hybrid models. Additionally, this study aims to complement substantially different case locations in the literature since only a few studies examine the outbreak's effect on bicycle preference in Indonesia. At a practical level, our findings are valuable for authorities to understand the potential demand for bicycles during an outbreak when the bicycle infrastructure is developed, because transport sustainability must be improved in developing countries.

3. Study area

This study chooses Yogyakarta city as a case study context. Yogyakarta is located in Indonesia's south-central region and covers an area of 32.5 square kilometers. In 2020, the population in the study area was about 373 thousand (Yogyakarta Statistics Agency, 2021). Yogyakarta has a persistent issue with traffic congestion since private motorized vehicles, especially motorbikes, dominate the traffic (Ramlan et al., 2021). Additionally, people living in this city highly depend on motorbike-based ride-hailing (Irawan et al., 2021a). Although the government has provided a bus transit system and continuously improved the services, the bus demand was low (Irawan et al., 2021b). Similarly, the demand for bicycles was also low in this city. However, Yogyakarta is one city in Indonesia that continuously campaigned for and promoted bicycle use before the emergence of COVID-19. As shown in Fig. 1 , some roads in Yogyakarta were recently equipped with bicycle-painted lanes and bicycle boxes (i.e., bicycle priority boxes while waiting at signalized junctions). Some bus stops also installed bicycle parking facilities to support the bus-bicycle intermodal. During the outbreak, the local authority of Yogyakarta used this outbreak as an opportunity to invest in bicycle infrastructure. For example, the city government in Yogyakarta used the outbreak resurgence of bicycle usage to revitalize bicycle lanes (City Government of Yogyakarta, 2021) and expand tourism-based bicycle routes (Pratama, 2021).

Fig. 1.

Cycling Infrastructures on Yogyakarta's Main Roads

Basemap source: Google Maps (2022). Photographs were taken by authors on July 12, 2022.

4. Research method

4.1. Questionnaire design and survey description

The questionnaire contained three sections. In the first section, the respondents had to decide whether they would use (or not use) bicycles, where this decision was based on traffic conditions and bicycle facilities, including bicycle lanes, parking, and bicycle boxes. Referring to previous studies discussed in the literature review section, traffic conditions were classified into two levels: uncongested and congested. Meanwhile, the bicycle lane was divided into three levels: none, painted lanes, and separated or dedicated lanes. The next context variable was bicycle parking facilities, categorized into three levels: without parking facilities and on-street and off-street bicycle parking. The availability of parking facilities allows for secure parking of bicycles. Without parking facilities, the loss of a bicycle is the responsibility of the owner. In such cases, cyclists can take their bicycles into their offices. Off-street parking facilities protect bicycles from the sun and rain. A similar level of the bicycle lane, traffic conditions, and parking facilities was also shown by a study predicting the demand for cycling in London (Maldonado-Hinarejos et al., 2014). Last, two levels were defined for the bicycle priority box for waiting at a signalized junction: with and without boxes. Although Dill et al. (2012) still divided the bicycle priority box into colored and uncolored boxes, the uncolored box was excluded because there were no bicycle-unpainted boxes within the study area.

A stated choice experiment was developed from four variables with two to three levels. This method is used to elicit respondents' preferences based on hypothetical conditions. This study anticipated that the choice decision would be made by trading the attributes of the various alternatives and selecting the alternative that provides the highest utility. Therefore, this study could capture individuals’ preferences under varying conditions. In determining the scenarios of a stated choice experiment, a fractional factorial with an orthogonal design was utilized, as there is no knowledge about the prior parameters. Although only a portion of all potential choices was considered in the experiment, orthogonal estimation was still possible. If an experiment fulfills attribute level balance and all parameters are independently estimable, it is said to be orthogonal (Louviere et al., 2000). Orthogonal design is considered to be efficient only in cases when the prior parameters are unknown, as discussed by Bliemer et al. (2009). Previous studies also used this method to design the experiments (Han and Timmermans, 2022; Irawan et al., 2020b; Qin et al., 2017). However, two generated scenarios from orthogonal design are improper for the field condition: the availability of separated bicycle lanes exists in uncongested traffic. Due to this, these two scenarios were dropped and resulted in seven stated choice experiments. Table 1 shows the scenarios of stated choice experiment. An example of the first scenario asked to the respondent is as follows: “If the traffic condition is uncongested, the road is facilitated by painted lanes for bicycles, the signalized junction has no priority boxes for cyclists, and your destination place is facilitated by on-street parking making your bicycles will not be lost but unprotected from the sun and rain, Would you adopt a bicycle in that situation during the outbreak?” The respondents then answer that question with “yes” or “no” option.

Table 1.

Scenarios of stated choice experiment.

Scen.	Traffic condition	Cycle lane	Priority box	Parking facilities
1	Uncongested	Painted lanes	Without box	On-street parking
2	Uncongested	No lanes	Without box	Off-street parking
3	Congested	No lanes	With box	On-street parking
4	Congested	Separated	Without box	Without parking
5	Uncongested	No lanes	Without box	Without parking
6	Congested	Painted lanes	Without box	Off-street parking
7	Uncongested	Painted lanes	With box	Without parking

In the second section, respondents had to describe their attitudinal statements, which were measured on a 5-point Likert scale (1: strongly disagree; 5: completely agree). The purpose was to identify whether the respondents' perspective has changed after the emergence of COVID-19 toward cycling as a transport mode, the environmental consciousness, and global warming concerns. Therefore, the respondents were given three statements to address these perspectives which the statements include: (1) COVID-19 changed my perception of cycling; (2) COVID-19 made me think more about the environment; and (3) COVID-19 increased my concern about global warming. Some studies concluded that environmental consciousness and global warming concerns affect the decision to cycle. For instance, Piras et al. (2021) revealed a significant and positive correlation between the perceived benefits of cycling (e.g., pollution reduction) and residents' cycling frequency in Cagliari and Sassari, Italy. In this section, respondents were also asked about the social influence of their friends and other people on their bicycle use preferences on a 5-Likert scale (1: strongly disagree; 5: completely agree) with the statements are: “I cycle because my friends also use bicycles,” and “I cycle because other people cycle.” In this case, other people represent people who do not closely interact with the respondents but significantly affect respondents’ travel behavior. It is clear that individuals prefer to adopt behaviors that are prevalent among other people. The literature shows that social influence affects bicycle use behavior. For commuting trips by bicycle, a study in Spain found that family members, friends, and co-workers/classmates significantly influenced workers to cycle (Lois et al., 2015). Meanwhile, Han et al. (2016) revealed that social influences directly impacted bicycle use in the context of bicycle tourism. A study in a Colombian city also found that social influences significantly impacted bicycle use among both new and experienced cyclists (Rodriguez-Valencia et al., 2021).

Finally, the survey included questions related to the respondents’ socioeconomic characteristics (gender, age, income, and education) and automobiles and motorbikes ownership in a household. This is inseparable from the fact that socioeconomic factors and private vehicle ownership influenced bicycle utilization. For example, a study in England and Wales found that the number of automobiles significantly influenced the proportion of overall bicycle use in work commutes (Parkin et al., 2008). Related to socioeconomics, men were found more likely to bicycle in six cities in Canada and North America: Montreal, Vancouver, Toronto, Chicago, Boston, and New York (Branion-Calles et al., 2019). Meanwhile, a study in Madrid stated that gender and education significantly influenced the intention to use bicycles (Fernandez-Heredia et al., 2016). Other studies also reported that higher-income groups showed a lower tendency to choose bicycles to travel to work (Habib et al., 2014; Parkin et al., 2008).

Web-based questionnaires were distributed online using various forums such as Facebook, Line, Instagram, WhatsApp, and Twitter. This data collection method is considered as a snowball and convenience sampling technique. The convenience sampling method was chosen due to its simplicity, ease of use, and prevalence among respondents (Taherdoost, 2016). Nevertheless, this study ensures the collected data's accuracy by cleaning data to eliminate incorrect or irrational responses. Furthermore, this study used Slovin's method to compute the sample size considering this study deals with a heterogeneous population mix. In Slovin's formula, the population of Yogyakarta was determined to be 373,589, with a 95 percent margin of error, producing the e value at 0.05. The sample size was found to be 399.57 or 400 individuals. Given that the surveys were conducted using online platforms, this study assumed a response probability of 50 percent for illogical responses and unanswered questions. As a result, this study targeted 600 respondents. The online survey attracted responses from 622 people living in Yogyakarta between December 2020 and February 2021. The respondents involved in this study were the same as those studied in Irawan et al. (2022). Unfortunately, for this study, only 362 responded to all the seven choice scenarios and answered other socioeconomic and preference questions related to the effects of the outbreak.

4.2. Model specifications

The survey collected seven observations regarding a respondent's cycling decision, and this study applied an MXL model to examine the inter-option choice sequence and intrinsic correlation. There are two main benefits of using the MXL model than the conventional logit model (i.e., multinomial logit model). The MXL model accounts for preference heterogeneity or random taste variations (Hensher and Greene, 2003). Thus, people sharing identical socioeconomic features may answer the survey in a different manner because of their unique unobserved characteristics. The MXL model also de-emphasizes the independence requirement of the standard logit model's irrelevant alternatives (IIA) assumption, because the mixed logit probabilities ratio is influenced by all data, including choice attributes (Train, 2009).

In the MXL model, the individual-n utility in using bicycle-i is as follows:

$$U_{ni}=\beta X_{ni}+\mu Z_{ni}+{\xi }_{ni}$$ (1)

Where $X_{ni}$ is the vector of observed variables related to bicycle-i, such as bicycle facilities, socioeconomic variables, and vehicle ownership; furthermore, the error component $Z_{ni}$ defines the utility's stochastic portion, along with ${\xi }_{ni}$ (distributed iid extreme value). Therefore, the unobserved utility portion is ${\omega }_{ni}=\mu Z_{ni}+{\xi }_{ni}$. It is also considered the model's estimated alternative specific error component. This creates a correlation among individuals and is calculated using the model.

The mixed logit probability of the bicycle-i choice of individual-n is the integral of standard logit probabilities over a density parameter value—the product across all ω values, with $\theta$ as the fixed parameters’ vector (Train, 2009).

$$P_n=\int \left(\frac{e^{U_{ni}}}{{\sum }_{j=1}^J{e}^{U_{nj}}}\right)f\left({\omega }_{ni}|\theta \right)d{\omega }_{ni}$$ (2)

Here, $\left(\frac{e^{U_{ni}}}{{\sum }_{j=1}^J{e}^{U_{nj}}}\right)$ is the logit probability, $f\left({\omega }_{ni}|\theta \right)d{\omega }_{ni}$ is the normal density function.

The goodness-of-fit was determined by comparing the final log-likelihood of the restricted and unrestricted models using log-likelihood ratio (LLR) tests. The LLR test statistic is computed as:

$$LLR=–2\left({\mathrm{L}(\beta }_{\mathrm{R}}\right)–\mathrm{L}\left({\beta }_{\mathrm{U}}\right))$$ (3)

where ${\beta }_{\mathrm{R}}$ represents the restricted model's estimated coefficients, and ${\beta }_{\mathrm{U}}$ represents the unrestricted model's estimated coefficients. The restricted log-likelihood (${L(\beta }_R)$) reflects the model estimation from the initial explanatory variables.

Furthermore, the unrestricted log-likelihood ($L({\beta }_U$)) is the log-likelihood derived by augmenting the basic (restricted) model with parameters. The following formula was used to determine the log-likelihood (L):

$$\mathrm{L}\left(\beta \right)={\sum }_{n=1}^N\sum _i{y}_{ni}\mathit{ln}\left(P_{ni}\right)$$ (4)

where $y_{ni}$ equals 1 if the response is to cycle and 0 otherwise, and $P_{ni}$ is the probability of cycling in each observation. The result of $LLR$ may then be compared to the chi-squared ($\chi$ ²) distribution with $K_U-K_R$ degrees of freedom (df); here, $K_U$ (restricted model) and $K_R$ (unrestricted model) are the total parameters. If $LLR$ > ${\chi }_{0.05}^2$, the null hypothesis is rejected, and it can be concluded that adding new parameters improves the previous model. The freeware APOLLO software (Hess and Palma, 2019) was used for model estimation to take advantage of its versatility in specifying the models constructed in this study.

Three models were applied in this study to produce a more behaviorally sound representation of the COVID-19 outbreak toward bicycle adoption. The first MXL model (Model 1) only included information about traffic conditions and bicycle facilities. The second MXL model (Model 2) provided more details about respondents' socioeconomic attributes (e.g., gender, age, education, and income) and the number of vehicle ownership in respondents' households (automobiles and motorbikes). The last MXL model (Model 3) included perception variables (e.g., whether COVID-19 changed respondents' attitudes toward bicycle perception, global warming and environmental consciousness; and whether social influences during the outbreak influenced respondents' willingness to adopt bicycles). Fig. 2 shows the modeling structure of the three proposed models. Unlike the first two models, a hybrid modeling framework was applied to Model 3. The hybrid model included two equations: a measurement equation (indicated by the dashed arrows) and a structural equation (indicated by solid arrows in Fig. 2). The modeling process in Model 3 is as follows: using structural equation modeling techniques, the first step examined the latent variables of attitudes change caused by the outbreak and their interconnection. After identification, the study used a MIMIC model to assess its value regarding observable variables (Jöreskog and Goldberger, 1975). This model can properly assess whether socioeconomic and psychometric indicators can predict attitudes change's latent variables and social influences on bicycle choice decisions. As shown in Fig. 2, the latent variable of “attitudes change” is a function of several psychometric indicators: the perception of cycling, pro-environment, and global warming concerns. In comparison, the latent variable of “social influences” is identified as a function of two psychometric indicators: the influence of the respondents' friends and other people. Respondents' socioeconomic characteristics tend to determine individuals' latent preferences; further, observed psychological responses of interviewed respondents also function as indicators for individuals' latent preferences. This study modeled bicycle use using an MXL model that incorporated these two latent variables in the last step.

Fig. 2.

Proposed modeling structure.

5. Results and discussions

5.1. Data

Table 2 displays the descriptive statistics of the 362 respondents who completed all the seven choice experiments and all the socioeconomic and perception questions. Since this study applied a snowball and convenience sampling method, the sample is not balanced with the age and gender distributions of the study area's population. The sample's gender distribution was slightly skewed toward males by 62.71%, while data shows that 48.72% of the population are males. For age distribution, the sample comprised 40.89% of young drivers less than 25 years old, 30.56% of drivers aged 25–40 years, and the rest (28.53%) are drivers more than 40 years old. Meanwhile, the population's age distribution reveals that 37.57% are people less than 25 years old, 37.5% are people aged 25–40, and 22.65% are people more than 40 years old. Therefore, there is a possible sampling bias in this study. Looking into the probability of bicycle adoption, the majority of respondents will adopt bicycles in Scenario 1, which accounts for 75.97%. In contrast, most respondents will not adopt bicycles in Scenario 3, accounting for 17.13%. Meanwhile, there is a slight difference in bicycle adoption probability between Scenario 2 and 7 (66.30% vs 69.89%).

Table 2.

Descriptive statistics.

Variable	Description	N	%	Mean	SD
Bicycle adoption	Scenario 1	275	75.97
	Scenario 2	240	66.30
	Scenario 3	62	17.13
	Scenario 4	196	54.14
	Scenario 5	174	48.07
	Scenario 6	101	27.90
	Scenario 7	253	69.89
Socio-economic characteristics
Gender	Male	227	62.71
Age	18–24 years	144	39.78
	25–40 years	136	37.57
	>40 years	82	22.65
Education	High school	72	19.89
	Graduate-level	207	51.18
	Masters-level or higher	83	22.93
Income	Less than 2 million IDR (139 USD)	144	39.78
	2–5 million IDR (139–349 USD)	139	38.40
	More than 5 million IDR (349 USD)	79	21.82
Number of automobiles in the household				0.77	0.79
Number of motorbikes in the household				2.17	1.07
Latent variable of attitudes change
COVID-19 changed my perception of cycling	Strongly disagree	21	5.80
	Disagree	132	36.46
	Neutral	21	5.80
	Agree	152	41.99
	Strongly agree	36	9.94
COVID-19 made me think more about the environment	Strongly disagree	8	2.21
	Disagree	36	9.94
	Neutral	8	2.21
	Agree	153	42.27
	Strongly agree	157	43.37
COVID-19 increased my concern about global warming	Strongly disagree	7	1.93
	Disagree	39	10.77
	Neutral	14	3.87
	Agree	145	40.06
	Strongly agree	157	43.37
Latent variable of social influence
I cycle because my friends also use bicycles	Strongly disagree	58	16.02
	Disagree	182	50.28
	Neutral	8	2.21
	Agree	99	27.35
	Strongly agree	15	4.14
I cycle because other people cycle	Strongly disagree	48	13.26
	Disagree	168	46.41
	Neutral	8	2.21
	Agree	116	32.04
	Strongly agree	22	6.08

The data also show that the average number of motorbikes in the household was 2.17, meaning that there are two motorbikes in the respondents’ households. Meanwhile, the standard deviation was 1.07, indicating that there was heterogeneity among all respondents related to the number of motorbikes in their household. Unlike motorbikes, the average number of automobiles in the household was 0.77, with the standard deviation being 0.79. It represents that not all respondents had an automobile, and there was homogeneity among all respondents concerned with the number of automobiles in their household. Related to the attitude change variable, more than 83% of respondents agreed and strongly agreed that COVID-19 outbreak altered their attitudes toward the environment and raised their concern about global warming. However, approximately the same number of respondents agreed (41.99%) and disagreed (36.46%) that COVID-19 changed their perception of cycling. To assure that global warming is not a part of environment, this study checked the pairwise comparison of those two variables. The result was significant, and a difference was revealed with p < 0.01, meaning that environmental awareness can be viewed as a separate behavior of global warming concern. Related to the social influence variable, the majority of responders denied (16.02% strongly disagreed and 50.28% disagreed) that they cycled as a result of peer pressure.

Furthermore, a reliability test was conducted to check the reliability of the collected responses related to the latent variable of attitude change and social influence. The results showed that alpha reliabilities for attitude change and social influence were 0.706 and 0.812, respectively, higher than the cut-off value of 0.7 (Hair et al., 2010). Therefore, this study could include these two latent variables for further analysis.

5.2. Model estimations

5.2.1. MIMIC model

The MIMIC model's goodness-of-fit measures indicated that this study's relevant model had a good fit. The results were as follows: GFI (goodness-of-fit index) = 0.992; AGFI (adjusted goodness-of-fit index) = 0.973. These values were above the recommended minimum value of 0.9. (Browne and Cudeck, 1992). The RMSEA (root mean square of approximation) was 0.042, which was slightly below the maximum acceptable value of 0.05 (Hu and Bentler, 1999).

The measurement and structural equation results are provided in Table 3 . For the measurement equations, the coefficient of the perception of cycling constructing the latent variable of attitudes change and the coefficient of other people building the latent variable of social influences were normalized to 1. The results showed that the latent variable-indicator relationship had the expected signs. The measurement equations also showed a strong latent variable-indicator relationship (except for the perception of cycling). For example, friends and others explained 76.2% and 61.5% of social influence, respectively. The indicators linked to attitudes change also explained that this latent variable, encompassing environmental and global warming concerns, had high percentages (80.2% and 98.3%, respectively). Meanwhile, regarding the influence of respondents’ socioeconomic characteristics on the latent variables, as displayed in the structural equations, the results suggested that gender, age, and education significantly affected latent variables of attitudes change and social influences. For income variable, it only significantly affected the latent variable of social influence.

Table 3.

MIMIC model parameter estimation.

Latent variable	R2	Attitude change		Social influences
		Coeff.	t-test	Coeff.	t-test
Measurement equation
The perception of cycling	0.072	0.268	–
Environment	0.802	0.896	13.125***
Global warming	0.983	0.992	13.779***
Friends	0.762			0.873	9.781***
Other people	0.615			0.784	–
Structural equation
Male		0.095	4.634***	0.060	2.741***
Aged 25–40 yearsa		−0.102	−4.835***	0.077	2.853***
Aged more than 40 yearsa		0.126	4.723***	0.028	1.023
Graduate-levelb		−0.106	−4.276***	−0.111	−3.855***
Masters-levelb		−0.121	−4.933***	−0.130	−4.250***
2–5 million IDR incomec		−0.005	−0.217	−0.090	−3.365***
More than 5 IDR million incomec		−0.043	−1.598	0.090	3.043***

***p < 0.01.

^aPeople aged 18–24 years.

^bHigh school educated people, and.

^cLess than 2 million IDR people as a reference category.

Discussing the results of MIMIC model, shown by a positive sign of the male variable, the model results reveal that male respondents tended to be more easily influenced by friends and other people in adopting bicycles compared to females. Men also tended to have a greater perception change caused by the outbreak, particularly related to bicycle mode, environment, and global warming than women. Meanwhile, attitude change caused by the emergence of COVID-19 outbreak was perceived more by people aged over 40 years than people aged less than 25 years since a positive correlation exists between those aged more than 40 years and attitude change due to COVID-19 outbreak. Conversely, as displayed by a negative sign, people aged between 25 and 40 years were less likely to perceive the attitude change than people aged less than 25 years. However, people aged between 25 and 40 years perceived the impact of social influences more than people aged less than 25 years. Furthermore, the outbreak and social influences negatively affected people with bachelor's and master's degrees more than people without a bachelor's degree. Regarding income level, the model results show a negative correlation between 2 and 5 million IDR income and social influence and a positive correlation between more than 5 IDR million income and social influence. It represents that the latent variable of social influence was perceived less for people with an income of 2–5 million IDR and more for people with an income of more than 5 million IDR. However, the latent variable of attitude change had no significant effect on individuals' incomes. It was also found that social influences had no significant impact on different individuals aged below 25 years and above 40 years.

5.2.2. MXL model

Table 4 presents each MXL model's estimation results. Regarding goodness of fit across models, in analyzing the values of the adjusted rho-square, this study observed that Model 3 had the best fit overall. In looking at the value of the likelihood ratio test of Model 2, $LLR$ > ${\chi }_{0.05}^2$ signified that the addition of socioeconomic variables improved Model 1, even though both models had the same values of the adjusted rho-square. In contrast, the addition of latent variables in Model 3 did not really enhance the explanatory power of Model 2, as observed by the values of the likelihood ratio test ($LLR$ < ${\chi }_{0.05}^2$), even though the adjusted rho-square was improved. Nevertheless, the parameters of the two latent variables (i.e., attitudes change and social influences) significantly affected the decision to cycle and are therefore worth discussing further.

Table 4.

Mixed logit model results.

Variables	Model 1	Model 2	Model 3
Traffic condition (uncongested)	4.953 (7.223)***	4.799 (7.515)***	4.904 (7.467)***
Bicycle facilities
Bicycle lane	1.359 (9.075) ***	1.475 (8.849)***	1.489 (9.016)***
Bicycle box	1.725 (6.561) ***	1.502 (6.642)***	1.541 (6.619)***
Bicycle parking	0.46 (1.787) *	0.52 (2.042)**	0.522 (1.945)*
Socioeconomic characteristics
Gender (male)		−0.262 (−0.69)	−0.27 (−1.02)
Aged 25–40 yearsa		1.187 (2.986)***	1.305 (3.263)***
Aged more than 40 yearsa		1.43 (3.23)***	1.338 (2.998)***
Graduate-levelb		−0.572 (−1.141)	−0.587 (−1.659)*
Masters-levelb		−0.307 (−0.379)	−0.313 (−0.841)
2–5 million IDR incomec		−0.681 (−1.676)*	−0.771 (−1.998)**
More than 5 IDR million incomec		−0.941 (−1.742)*	−0.859 (−1.661)*
Vehicle ownership
Number of automobile owners		−0.047 (−0.235)	−0.016 (−0.182)
Number of motorbike owners		0.099 (0.630)	0.086 (0.474)
Attitude change			1.054 (1.962)*
Social influences			−0.367 (−2.054)**
Alternative specific constants for cycling	−4.547 (−11.599)***	−4.464 (−6.775)***	−5.077 (−5.413)***
Random coefficients (normally distributed)
Uncongested	3.335 (7.486)***	3.159 (7.152)***	3.28 (7.148)***
Bicycle lane	−0.74 (−3.999)***	1.013 (6.464)***	1.022 (6.722)***
Bicycle parking	−1.567 (−7.341)***	−1.288 (−6.226)***	−1.336 (−6.787)***
Statistics
Observations	2534	2534	2534
Number of iterations	67	98	112
Adjusted rho-square	0.434	0.434	0.435
Null log-likelihood (0)	−1756.435	−1756.435	−1756.435
Final log-likelihood (L(β))	−985.312	−975.048	−1300.543
Number of independent variables (K)	4	17	19
$LLR$	–	20.53	−650.99
${\chi }_{0.05}^2$	–	19.02 (KU– KR = 9)	5.99 (KU– KR = 2)

*** represents p < 0.01.

** represents 0.01 ≤ p < 0.05.

* represents 0.05 ≤ p < 0.1.

^aPeople aged 18–24 years.

^bHigh school educated people, and.

^cLess than 2 million IDR people as a reference category.

The MXL models utilized 500 quasi-random Halton draws for assessing bicycling infrastructure and traffic variations (based on the estimated coefficients' standard deviations). The models were drawn from normal distributions. When the standard deviation is larger, individuals’ choice-related variations are greater. Since the standard deviation of the bicycle priority box was less stable, this variable was dropped from the model. The results showed that all standard deviations were significant. The significance of the standard deviations indicates that there was heterogeneity among all respondents with respect to bicycle facilities and traffic congestion.

Regarding the independent variables considered in the models, not all variables significantly influenced bicycle adoption during the outbreak. Looking into account the first MXL model result, this study confirmed that bicycle infrastructure, including bicycle lanes, parking facilities, and bicycle boxes, was sufficient for the adoption of bicycles during the outbreak in Yogyakarta, Indonesia. Shown by positive signs, the MXL model results revealed that the probability of cycling increased with the availability of bicycle lanes, parking facilities, and bicycle boxes. Since the bicycle facilities were set as 0 for those without bicycle facilities (no bicycle lanes, no bicycle boxes, and no bicycle parking), 1 for bicycle facilities with mixed motorized traffic (painted bicycle lane and priority boxes) and 2 for bicycle priority facilities separated from motorized traffic (separated bicycle lanes and off-street parking), the MXL model results revealed that providing painted lane could increase 1.489 of bicycle adoption utility in Model 3. Meanwhile, providing separated bicycle lanes could increase bicycle adoption utility for the same model (Model 3), accounting for 2.978. A similar situation can be explored for bicycle boxes and bicycle parking facilities. For Model 3, providing bicycle boxes could increase bicycle adoption utility, accounting for 1.541. Meanwhile, providing bicycle on-street and off-street parking only increased bicycle adoption utility, accounting for 0.522 and 1.044, respectively. From this, it can be concluded that significant and positive coefficients of bicycle facilities substantially reduce the high basic disutility received by respondents in adopting bicycles. As shown by a significant, negative, and high coefficient of ASC in the MXL model results, respondents receive high basic disutility when they decide to adopt bicycles during the outbreak. This intuitive finding matches extant findings regarding the correlation between bicycle facilities and bicycle use during the outbreak (Kraus and Koch, 2021; Nikitas et al., 2021) and outside of outbreak conditions (Abolhassani et al., 2019; Buehler and Dill, 2016, de Souza et al., 2014; Luis and Soto, 2021). Furthermore, this study also found that the probability of the decision to cycle increased with a decrease in motorized traffic since there was a positive and significant relationship between uncongested traffic congestion and the probability of cycling.

Including the socioeconomic characteristics and vehicle ownership in the second model, the MXL model results revealed that factors of age and income significantly affect the willingness for bicycle adoption in Yogyakarta. As shown by a positive sign of age, people aged 25 years or older had a higher willingness to cycle than those under 25 years old. Conversely, as displayed by a negative sign of income, people with less than 2 million IDR income were more likely to cycle than people with 2 million IDR income or more. Meanwhile, factor of gender, education level, and the number of automobile and motorbike owners had no significant effect on bicycle adoption. Related to no relationship was found between gender and bicycle preference, this finding was consistent with Abdullah et al. (2020), showing that there was no correlation between gender and non-motorized transportation choice during COVID-19.

Finally, considering the effect of latent variables of attitude change and social influence in the last model, both latent variables seemed relevant from a statistical viewpoint. Shown by a negative sign of social influence variable, the findings revealed that individuals who used bicycles because of societal pressure tended not to adopt bicycles as their means of transport. It also means that these people used bicycles for other purposes, such as health and well-being, during the outbreak. A different sign was shown by the effect of attitude change on probability of cycling during the outbreak. The MXL model results found that COVID-19 positively changed the perception of cycling and directed attention to the environment and global warming, which significantly increased the adoption of bicycles during the outbreak. Meanwhile, for the education level, although the relationship was not statistically significant in Model 2, Model 3 revealed that there was a negative and significant relationship between graduate-level and probability of cycling. It means that individuals with a bachelor's degree were less likely to use bicycles than individuals without a bachelor's degree.

6. Conclusions and policy recommendations

This study aimed to measure bicycles adoption during the outbreak in Yogyakarta, Indonesia. It identified and utilized the latent variables of the outbreak effects on cycling perception, environmental consciousness, global warming concerns, and social influences during the outbreak. These latent variables, along with the traditional variables of bicycle infrastructure, socioeconomic characteristics, and vehicle ownership, were used to explore the determinants of bicycle adoption. Three models based on the MIMIC and MXL models were proposed in this study. Referring to the assessment of goodness of fit and the interpretability of the results, the present study found that integrating the MIMIC and MXL models was the most appropriate solution to describe the preference for bicycles during the outbreak.

This study found several factors primarily affect the decision-making on cycling during the outbreak. Socioeconomic factors such as income level, educational background, and age significantly affected preferences for cycling. Meanwhile, no correlations were found in terms of cycling preferences with gender and vehicle ownership. The availability of cycling infrastructures, including bicycle lanes, parking facilities, and priority boxes at signalized junctions, also promoted the probability of shifting to cycling during the outbreak. Other than that, the model parameter in this study also discovered growing consciousness over environmental and global warming, reflected by parameters used in the model affecting preferences for bicycles. Moreover, social influence also became a determinant in adopting bicycles during the outbreak.

Finally, based on the study's findings, two main recommendations could be proposed to increase the willingness to adopt bicycles during the COVID-19 outbreak in Yogyakarta, Indonesia. First, as also suggested by Budd and Ison (2020), the government needs to provide infrastructures dedicated to bicycles aiming to facilitate bicycle travel without interference from motorized vehicles. The other findings supported this proposed policy by showing that the ownership of automobiles and motorbikes did not significantly influence the preference for bicycles. It is clear that Indonesian cities, including Yogyakarta, are dominated by private motorized vehicles. Therefore, the insignificant relationship between motorized vehicle ownership and bicycle adoption refutes the statement that bicycle infrastructure is insufficient to promote cycling in automobile-dominated cities (Fuller et al., 2021). However, it should be highlighted that this study also found that bicyclists tended to be people with lower income and education. This finding was strengthened by the other model results, showing that people with low income and less education were less likely to perceive social influence and more likely to perceive attitude change caused by the outbreak than their counterparts. In other words, well-educated and middle- or upper-income people used bicycles more because of the cycling trend during the outbreak and were, therefore, less likely to adopt bicycles for transport, even if bicycle infrastructure was provided. Given this finding, the stimulation of bicycle infrastructure to increase bicycle demand during the outbreak in Indonesia will not be as effective as in other countries, such as Australia and European countries (Kraus and Koch, 2021; Nikitas et al., 2021). Second, the promotion of bicycles for environmental benefits can be carried out by the government to raise the demand for bicycles, as this study found that COVID-19 positively changed the perception of cycling and directed attention to the environment and global warming, which significantly increased the adoption of bicycles during the outbreak.

Although the abovementioned findings are significant, the authors acknowledge that this study has some limitations, thus suggesting opportunities for future research. First, the small sample size and the lack of sample representation of the population need to be anticipated for the following studies. Having a larger collected sample and applying a quota sampling method are essential to ensure the sample representativeness based on sociodemographic characteristics. Second, using an efficient design rather than a conventional orthogonal design in generating the choice situation needs to be executed for future studies to improve the stated choice experiment due to the fact that the efficient design was more statistically efficient and had lower standard errors in predicting parameters than the orthogonal design (Bliemer and Rose, 2011; Rose et al., 2008). Third, including other latent variables primarily related to safety is essential for future studies to better understand the preference for bicycles during the outbreak, as previous studies have shown that bicycle infrastructure has a strong relationship with safety issues (Luis and Soto, 2021; Thompson et al., 2017). Fourth, although this study found that bicycle infrastructure provision potentially enhances the preference for bicycles, future studies should further explore the effectiveness of bicycle facilities in escalating bicycle demand, for example, the effectiveness of dedicated bicycle lanes and bicycle-painted lanes. Further insights could be obtained in future studies by replicating this study in a post-COVID-19 context; this will help to compare potential bicycle users during and after the outbreak.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.