*Article* **Effect of** *hukou* **Accessibility on Migrants' Long Term Settlement Intention in Destination**

**Peilin Li 1, Yufeng Wu 2,\* and Hui Ouyang <sup>3</sup>**


**Abstract:** Migrants' long-term settlement intention in urban areas has been emphasized by both policy makers and researchers in promoting urbanization and coordinating regional economic development. This study advances the body of knowledge by investigating the effect of what E.S. Lee has proposed as 'intervening obstacles' in the 'push-and-pull' theory—the difficulty in obtaining *hukou* in migration destination, on their long-term settlement intention in urban areas. Logistic regressions were applied to examine the effect of urban registered residence system (the *hukou* system) accessibility on migrants' long-term settlement intention in urban areas, as well as the determinants of subjective evaluated difficulty in obtaining urban *hukou*, based on a nation-wide large-scale survey in 46 Chinese cities. Our results suggest that difficulty in obtaining urban *hukou* does play an important role in shaping country-wide population movement. However, the negative impact of hukou difficulty on migrant workers' residence intention is not linear, and only when the threshold in obtaining *hukou* is too high and difficult to achieve will migrant workers choose to return to their hometown in the long term. Moreover, the subjective evaluation of difficulty is further influenced by personal capability and living conditions in cities. This study provides pragmatic implications for administrations from either push side or pull side to improve habitant-related development strategies.

**Keywords:** *hukou*; intervening obstacles; long-term settlement intention; migrants; push-and-pull theory

#### **1. Introduction**

Since the reform and opening up in China, migrants from rural to urban areas have become an indispensable driving force of China's economic development [1–3]. The growth rate of the migrants slowed down recently but still exceeded 290 million, accounting for 20.8% of the total population in 2019 [4]. Most of them have realized the transition from agricultural to nonagricultural in occupations rather than in their lives because they have not yet obtained urban registered residency (called *hukou* in the following sections), which is an identification document where general household information such as names, marital status and one's place of residence were recorded. Therefore, high difficulty in obtaining an urban *hukou* has been considered as an important factor that prevents migrants from moving to urban areas but also affects their daily life and consumption level [5]. In addition, *hukou* not only has population registration functions but also is an important administrative tool to distribute key welfare such as access to primary and secondary schools, affordable housing and medical insurance reimbursement ratios. The long-standing *hukou* system also prevents migrant workers from integrating into local society and even suffer discrimination from the labour market [6]. The *hukou* system and its subsidiary social welfare distribution system in Chinese cities is accordingly an intervening obstacle in the 'push-and-pull' theory. The research of *hukou*'s impact on long-term settlement intention provides ideal evidence

**Citation:** Li, P.; Wu, Y.; Ouyang, H. Effect of *hukou* Accessibility on Migrants' Long Term Settlement Intention in Destination. *Sustainability* **2022**, *14*, 7209. https:// doi.org/10.3390/su14127209

Academic Editors: Mengqiu Cao, Claire Papaix, Benjamin Büttner and Tianren Yang

Received: 2 May 2022 Accepted: 10 June 2022 Published: 13 June 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

in the effects of institutional intervening obstacle in developing countries, where countries such as China are enduring fast urbanization.

In the past, scholars have constructed a corresponding evaluation index system to measure the difficulty of obtaining *hukou* according to the specific requirements of policies in cities [7–9]. However, this approach is based on the strong assumption that migrants in the same cities feel the same way about the difficulty of obtaining *hukou*, which ignores the heterogeneity of the individual. As an improvement, this paper measures the difficulty of obtaining *hukou* based on respondents' subjective judgments from the survey question: '*How do you think of the requirements for obtaining a local hukou in your current city?*'. In addition, this paper also referred to the experience of previous scholars by exploring the influencing factors and their differences from four dimensions: individual attributes, economic status, mobility characteristics and social integration status.

Specifically, based on a panoramically representative survey in 46 cities in China in 2020, this paper analyses the relationship between the difficulty of obtaining local *hukou* and the long-term settlement intention. Moreover, this paper attempts to quantitatively evaluate for the first time the impact of this obstacle on migrants' long-term settlement intention in urban areas. It contributes to the development of the 'push-and-pull' theory for other economics managing to design wise immigration policies that well balance inbound labor and talent supplies, permanent residency threshold, aging society, and social welfare fund management.

The outline of this paper is as follows: the Section 2 reviews the influencing factors on long-term settlement intention, particularly from the institutional perspective. The Section 3 introduces *hukou* system, its reform process, and explains what makes it an intervening obstacle in the 'push-and-pull' mechanism. The Section 4 presents research data and methodology. The Sections 5 and 6 illustrate the analyses of preliminary and empirical results, respectively, followed by conclusion and policy recommendations.

#### **2. Three Factors That Have Major Impact on Long-Term Settlement Intention**

The study on migration behaviour can be traced back to the end of the 19th century [10] and has become quite mature at the present stage. Among them, the factors affecting the long-term settlement of the labour force can be summarized into three aspects: economic, family and destination characteristics factors.

#### *2.1. Economic Perspectives*

'Push-and-pull' theory is recognized as one of the earliest theories of population mobility [11]. It suggests the purpose of migration is to improve their living standard [12]. This means that migrants will hesitate to stay in urban regions when their living conditions do not improve or when there are better investment opportunities in their hometown [13]. The other situation is that the expected income of agricultural production is constantly increasing, while the migrants have to bear a lot of potential risks in urban regions. Therefore, they may consider returning to the countryside so that they can also enjoy the happiness of their family [14]. Chinese scholars have long tried to explain the phenomenon of labour mobility in China by push-and-pull theory. For example, Liu established an urbanization population model based on the push–pull theory, which took the GDP, consumption and regional total population as functions, and used the model to predict and analyse the urbanization population of Shaanxi Province [15]. When comparing the influence factors of Chinese internal migration with those of international migration. Li [16] found that underemployment and poverty in rural areas, rapid development of capital-intensive technology in cities, government development policies leaning toward cities, and concentration of economic activities in urban areas are the common pushing and pulling factors.

Unlike the 'push-and-pull' theory, Lewis [17] only focused on the labour migration behaviours from rural agricultural sector to urban industry. His two-sector model emphasizes that the key drive of labour migration is the higher wage level. Compared with the agricultural sector, a higher level of labour productivity in the urban modern industrial

sector leads to higher wage. Meanwhile, the urban industrial sector has unlimited ability to absorb the migrant labour force under Lewis' model. Under this strong assumption, labour will reside in urban regions permanently until the end of their working life. In reality, the urban industrial sector has a certain limit to absorb labour. Moreover, with the continuous outflow of labour, agricultural marginal productivity will begin to increase. By then, wages were set by the market, and the agricultural and industrial sectors competed together for labour on the basis of their respective productivity [18]. In this case, the willingness of the labour force to stay in the urban areas depends only on the wage level in both places. In addition, Ranis and Fei [18] also argued that when real wages do not meet their expectations, they will also consider leaving. Many scholars have drawn on the insights of Lewis and Todaro to explain the large flows of rural-to-urban migration in China. They generally agree that the higher wages or expected incomes in urban areas are fundamental drivers of rural to urban migration [19,20].

Economic theories mainly analyse the reasons that hinder the labour force from settling down in the destination from the perspectives of wage level and human capital. These theories imply that migration behaviour is to maximize individual utility. Obviously, they ignore the role of households in the migration process.

#### *2.2. Family Perspectives*

Different from the Neoclassical theory, the New Economic of Labour Migration (NELM), which emerged in the 1980s, believes that the pursuit of labour migration is to maximize the benefits of the whole family rather than individual [21]. NELM theory regards labour mobility as family risk-sharing behaviour. As a whole, the family can distribute its labour force in different industries or regions and carry out risk diversification among all family members, minimizing the financial risk level of the whole family. The income of family members is highly complementary and negatively correlated. Therefore, the migrants have the obligation to send their income back or back to supplement the family's needs. On the other hand, migrants also can receive support from his/her family [22]. This theory partly explains the phenomenon of labour mobility even when there is no significant difference in wage income between regions. It suggests that the migration of workers is temporary, and that they will leave their destinations to return to their hometown once they reach the earning target their families expect.

NELM essentially begins to shed the light on the importance of blood relationship on migration behaviour. On this basis, a large number of scholars began to try to explore the issue of labour migration from the perspective of sociology. The life course approach holds that the study of an individual's life should be conducted in the context of a specific society, structure and culture [23], which is increasingly used to study migration behaviours [24]. Scholars have introduced the concept of family solidarity to explain why family ties contribute to migration behaviour [25,26]. This means that there is an obligation and responsibility among family members to take care of those in need. Although migrants provide financial support to the family by moving out to work, this also limits their possibilities for those intergenerational care exchanges [27]. The study found that the vulnerability of left-behind women is increased after husband's migration alone [28]. This vulnerability is reflected in the increase in labour burden and responsibility, emotional damage and other aspects [16]. For instance, studies in Nepal and Pakistan have found that in households where remittances earn less, the burden of labour is heavier for women left behind [29]. Scholars from China and India have studied the mental health of leftbehind women and found that their psychological problems, such as psychological pressure and loneliness, are more serious than those of non-left-behind women [30]. Besides that, left-behind children's school performance and unhealthy behaviours (smoking, internet addiction, etc.) are also associated with a lack of parental care [31–33].

#### *2.3. Destination Characteristics Factors*

The external factors that influence the long-term migration intention can be divided into two perspectives: the local amenities and institutional factors.

Local facilities are considered to be important factors in destination influencing migration behaviour. When choosing a destination for migration, people are more likely to move to an area with a higher quality of life, even if it is more densely populated and housing prices are higher [34]. The effect of natural amenities such as climate has been tested in the U.S. Based on the data after World War II, Rappaport [34] found that residential movement in U.S. relates to the warm winters. Unnatural amenities also affect migration behaviours. For instance, high-quality consumer goods and services are more conducive to a high human capital migrants' inflow [35]. Education quality at a university also enhances their willingness to stay at a destination as recent graduates have stronger competitive edges locally in earlier career stages [36].

On the other hand, the influence of institutional factors cannot be ignored. The research of western scholars in this aspect focuses on transnational migration. The research on migrants from Albania in Western Europe found that to be true. In these areas, more than 10 percent of the population has gone abroad to work. The income of these people is one of the most important sources of income for their families. However, about 55% of them do not have the legal permanent residence permit, or some have only obtained a short-term job permit. About 70% of them return for good [37]. A study of global asylum applications since 2000 by Hatton [38] found that a third of the decrease in asylum applications to Europe, North America and Australia was due to stricter policies.

Previous studies on China's internal migration have found that *hukou* is the main reason for the weak social status of migrants, which affects their residence intention. Although the *hukou* system has been loosened, allowing migrants to live and work in cities without having to migrate, they still suffer social exclusion because of their household registration status [39]. A study in Hubei province found that because of the *hukou* system, migrants' access to some basic public services are restricted, preventing them from truly integrating into urban areas [40]. When Liu [15] studied the attitudes of local residents towards migrants, he found that local residents generally agree with the contribution of migrants to the local area, but they also hold that migrants should not have the same rights as local people in some public services such as unemployment relief and low-rent housing. Therefore, the *hukou* system not only affects migrants' rights to public services, but also create identity discrimination among residence. The higher the perception of fairness, the stronger the willingness to stay in the city, which even has a moderating effect on the initial willingness to stay [41].

#### **3. The** *hukou* **System**

#### *3.1. The Fundamental Role of hukou System in China*

China's *hukou* system essentially performs three functions: population registration, mobility restriction, and competitive welfare restriction.

Population registration function: in 16 July 1951, the Ministry of public security promulgated the *Provisional Regulations on City Household Registration Management*, which established the function of population registration in a registered residence system. Because it defines regulation for social affair management such as birth, death, immigration, relocation, social change and social identity, this function has its counterpart in the *hukou* system in Japan and the social security system in the United States.

Mobility restriction function: Based on the distinction between "agricultural household registration" and "non-agricultural household registration" in the *Household Registration Regulations Of The People's Republic Of China* passed by the Standing Committee of the National People's Congress in January 1958, the provisions of the Ministry of public security on the *Handling of Household Registration Migration (Draft)* "in August 1964 established restrictions on moving from rural areas to cities and market towns; and restrictions on moving from market towns to cities. " Consequently, Chinese cities are regarded collectively as welfare highland, with walls defined. The *hukou* system has become an administrative tool in restricting inbound migrants for long-term settlement.

Competitive welfare restriction function: What makes China's *hukou* system different from other countries' population management systems is that it artificially divides urban welfare according to its competitive attributes in the time when social production is not as high as nowadays. Noncompetitive welfare refers to public goods that have positive externalities, such as the degree of cleanliness of one city, the accessibility of municipal infrastructure and convenience. The number or quality of these benefits does not decline sharply due to the increase in people. Residents, no matter original or newcomer, can enjoy the same level of benefits. Competitive welfare refers to public service that has relatively high incremental cost due to limited professional resources such as teachers and doctors, or dedicated facilities such as schools and hospital beds. These services cover the field of healthcare, compulsory education, affordable housing, etc. The investment on these public services tightly links to local fiscal expenditure that mainly come from land transaction fees and cooperation tax, rather than property tax (this might also explain why municipal administrations are generally keen on inviting investment but are less enthusiastic in inviting population under the current tax system). Therefore, the *hukou* system protects vested population (citizens with local *hukou*) by setting access threshold on competitive benefits, such as public-school qualifications, college entrance examination qualifications, house purchase qualifications, car purchase qualifications, and medical insurance reimbursement ratios.

Under the current system, the mobility restriction function of registered residence system makes it possible to maintain the basic functions of a city and maintain public order. Through the administrative control of settlement conditions, settlement procedures and annual *hukou* quotas, cities are able to handle corresponding demand according to their own public service carrying capacity.

#### *3.2. hukou Is an Intervening Obstacle in the 'Push-and-Pull' Theory*

During the development of the 'push-and-pull' theory, E.S. Lee [12] argued that the mobility of migrants is not only affected by the 'push' and 'pull' factors from their hometown and destination but is also affected by intervening obstacles, such as distance and transportation between hometown and destination, cultural and dietary differences and the immigration laws.

The competitive welfare restriction function theoretically makes *hukou* an intervening obstacle besides the 'push-and-pull' mechanism, because it does not restrict migrants from entering the urban labor market at the present stage but restricts their right to obtain equal public services (esp. competitive welfare that are fundamental in access equal local development opportunity) in the city. For instance, participation in the middle school entrance examination and college entrance examination outside migrants' children's *hukou* registration place have been challenging. First, they are required to provide evidence that their parents are legally domiciled and employed locally (e.g., most provinces stipulate that in order to take the exam in the destination, the children who have migrated with parents need to provide a certificate of residence of their parents, a proof of stable occupation and a number of years of social security payment from their parents). Second, most cities do not open all types of public secondary schools to the children of migrants. Megacities such as Beijing, Shanghai and Tianjin only allow children of migrants to take entrance exams of secondary vocational schools.

#### *3.3. The Reform of hukou System*

The establishment of *hukou* system can be traced back to 1958, when the Standing Committee of the National People's Congress passed the Household Registration Ordinance. It stipulated that "citizens migrating from rural areas to urban areas must hold an employment certificate, a certificate of enrolment from educational institutions, or a permission document from the urban household registration authority". From the 1960s

to the 1970s, the *hukou* system saw strengthened restrictions on the movement of people between urban and rural areas legally. For example, in 1964, the Ministry of Public Security issued regulations to restrict population movement from two aspects: (1) from rural areas to cities; (2) from towns to cities.

Due to China's market-oriented reform in the 1990s, the rapid development of urban industry led to an increasing demand for labour, which provided incentive for the reform of the *hukou* system to gradually expand from small towns to cities. The State Council approved pilot schemes for reforming the *hukou* administration system in small towns in 1997, allowing rural residents who already work and live in small towns and meet certain conditions to apply for permanent *hukou* locally. After 2000, some local governments began to explore the path of household registration reform in cities. Cities such as Shenyang and Anshan introduced policies in 2010 to encourage talented people to transfer their *hukou* [42].

In recent years, the state has accelerated the reform of the *hukou* system. In 2013, the promulgation of the CPC Central Committee on reform of the overall number of major issues signifies the beginning of the systematic reform of the *hukou* system. In 2014, the State Council issued a guideline on the reform of the *hukou* system, which stated that by 2020, about 100 million migrants and other permanent residents would be encouraged to register as urban residents [43]. In 2019, the National Development and Reform Commission issued the *Key Tasks for New Urbanization Construction*. Under the plan, cities with a population under 3 million should remove all limits on *hukou*—household registration—and cities with populations between 3 million and 5 million should relax restrictions on new migrants [43]. Table 1 shows the relevant documents and main contents of *hukou* reform in recent years.


**Table 1.** The timeline of *hukou* system reform by city scales.

#### **4. Data and Methods**

#### *4.1. Data*

A survey of migrants' long-term residence intention was conducted in April 2020. Questionnaires were handed out randomly in four types of location—the four first-tier global cities, cities in developed coastal regions, other provincial capitals/subprovincial cities, other prefecture-level cities (Table 2 and red dots in Figure 1). The selection of survey locations was based on popularity of the city in attracting cross-region migration. A total of 23,381 surveys were collected, 99.36% (23,232) of which were valid.

**Table 2.** Four types of cities surveyed.


**Figure 1.** Distribution of cities surveyed and where surveyed migrants come from.

In our survey, 6973 respondents said they were not sure of their long-term residence intentions and were therefore not considered in this study. In addition, 895 respondents that did not answer questions about the difficulty of the local household registration system were also discarded. Finally, 10 respondents did not provide valid information required for some independent variables and were then discarded. Thus, the final valid observations were 15,355 covering all 46 surveyed cities. These respondents came from 304 prefecturelevel cities (90.2% of the total 337 mainland prefecture-level cities in 2020). The distributions of regions and demographic characteristics of these 15,355 observations and the whole observations did not display significant statistical differences. It suggests that our sample could be legitimately used to reflect the nature of the whole sample.

#### *4.2. Methodology*

In order to better understand the long-term residence intention of migrants in China, this paper mainly adopts two quantitative research methods, descriptive statistical analysis and binary logistic regression modeling. In our case, we defined the dependent variable 'long-term settlement intention' based on the question, '*What are your long-term residence plans in the future?*'. The respondents who choose '1. Purchase commercial housing locally. 2. Rent a house locally. 6. Stand in line to apply for affordable housing locally' have settlement intention in destination in long-term. The difficulty of obtaining a local *hukou* is considered as the key dependent variable in our study, which is based on the question '*How do you think of the requirements for obtaining a local hukou in your current city?*'. Other control variables include personal characteristics (gender, education, marital status, age and land right in hometown), migration characteristics (employment, income level and migration duration of migration) and destination characteristics (Ln (GDP per Capita), education resources and medical resources) (see Table 3 for summaries).


**Table 3.** Socioeconomic characteristics and long-term settlement intention of the sample studied.

The binary logistic model works by adapting the standard random utility model to our specific problem of resident intention choice as follows:

$$
\delta \mathcal{U}\_{i\dot{\jmath}} = \mathcal{S}\_{\dot{\jmath}} \mathcal{X}\_{i\dot{\jmath}} + \varepsilon\_{i\dot{\jmath}} \tag{1}
$$

where *i* refers to the individual and *j* to the type of intention. *Xij* is a vector of independent variables such as gender, age, education, etc.) and *εij* is a stochastic error component. Then the probability of choosing a given alternative can be shown as

$$P\_{\bar{\jmath}} = P\_r (\mathcal{U}\_{i\bar{\jmath}} > \mathcal{U}\_{ik})\_\prime \,\forall k \neq \bar{\jmath} \tag{2}$$

Obviously, the sum of the four probabilities must equal 1,

$$\sum\_{j=1}^{J} P\_{ij} = 1\tag{3}$$

Then, followed Long and Freese (2005), the multinomial logit model is given as follows:

$$P\_{ij} = \frac{\exp(\beta\_j X\_{ij})}{\sum\_{j=1}^{J} \exp(\beta\_j X\_{ij})} \tag{4}$$

Finally, the estimation of parameter *βj* is solved by using the maximum likelihood estimation methods operationalized with Stata.

#### **5. Preliminary Analysis**

#### *5.1. Basic Characteristics and Long-Term Settlement Intention*

Overall, Chi square test showed that the difference in all variables across groups (hometown and local) was statistically significant (*p* < 0.05). In terms of personal characteristics, about 56.05% of migrants are male in the local group, more than 10 percent points lower than the hometown group. It suggests that male workers are more likely to return to their hometown (see Table 4). Moreover, compared with the hometown group, a higher proportion of unmarried and young workers choose to stay locally. Moreover, well-educated (College and above) migrants are more likely to stay rather than return. About 47.29% of migrant workers have college and above degrees in the local group, about 23 percentage points higher than the return group. Migrants who have land rights in their hometown seems more likely to return than stay. Nearly 49% of workers in the local group state that they own lands in their hometown, 13 percent points lower than the return group.

Considering migration characteristics, 11.2% of workers in local group are unemployed, 6 percentage points lower than the return group (see Table 4). In terms of income level, the low-income level (less than 3500 RMB per month) takes up a higher proportion in the local group than the hometown group. This indicates that migrants with lower income level might be more willing to return home in the long-term. Migrants who have been local for more than five years are more likely to stay. A total of 65.37% of migrants in the local group have been living locally more than 5 years, over 9 percentage points higher than the hometown group. Lastly, as the distance increased, migrants were more likely to return home. Table 4 indicates that among respondents in the returning group, 42.25% were long-distance interprovincial migrants to other provinces, a significantly higher proportion than the local group.

Destination characteristics also differ between the two groups. In terms of regions, eastern migrants accounted for 59.27% in local group, more than 6 percent lower than the hometown group (see Table 4), which suggests that migrants in eastern China have a stronger desire to return home than other regions. It is worth noting that economic status of two groups has not much difference according to GDP. Finally, Table 4 shows that the medical and educational resources in the cities of migrant workers who are willing to

local areas.


**Table 4.** Basic characteristics of migrants by different long-term settlement intention.

return home are slightly lower than those of migrant workers who are willing to stay in

#### *5.2. What Kind of People Think It Difficult to Get a Local hukou?*

This part of the analysis preliminarily presents *hukou difficulty* among different socioeconomic groups. As shown in Table 5, for migrants with different education levels, 48.60% of migrants with low educational background perceive that it is too difficult to obtain local *hukou* currently, and this figure gradually decreases with the level of education increase. Only 30.77% of migrants with college degree or above have the same feeling. On the other hand, 47.72% of well-educated migrants find it a bit difficult to obtain a local *hukou* but believe that they would meet the requirements in the future. It suggests that the current *hukou* condition still casts hurdle for migrants with lower education levels.

In terms of income, as the income level rises, it becomes less difficult for migrants to obtain a local *hukou*. A total of 41.08% of migrants in low-income group (less than 3500) believe that the current *hukou* condition is too difficult for them. This figure did not change much in the wage range between 3501 and 8000. However, as the income level reaches 8000, the proportion of migrants who perceive *hukou* difficulty as high drop to 34.45%, which is significantly lower than other income groups.


**Table 5.** Subjective evaluation of difficulty in obtaining local hukou by demographic characteristics.

According to the current *hukou* policy, purchasing a house locally is still one of the key approaches in obtaining *hukou* in some cities. Table 5 also shows the attitudes of migrants towards *hukou* with different living conditions. First, only 20.78% migrants who own houses in destinations think it is too difficult to obtain a *hukou*. This number goes over 50% in other two groups (renting and living in dorms). This result might be due to the existence of survivor bias. On the one hand, buying a house might help migrants to gain a local *hukou* easier; on the other hand, it is likely that migrants themselves find it is not difficult to gain a local *hukou,* so they are willing to buy a house locally.

In terms of migration duration, with the increase in migration duration, the proportion of migrants who perceive obtaining local *hukou* as very difficult showed a downside trend, decreasing from 54.35% to 38.24%. This might be because of the recent *hukou* reform's emphasis on duration-orient policy, based on living duration and working duration locally. Consequently, as duration in destination increases, the chance of satisfying local requirement in obtaining *hukou* goes higher.

#### **6. Empirical Analyses**

#### *6.1. Modelling the Long-Term Settlement Intention in China*

In order to better understand influencing factors of residence intention, this paper establishes four binomial logistic models with residence intention as the dependent variable. Model 1 includes only the subjectively evaluated *hukou* difficulty as an independent variable. Variables of personal characteristics, migration characteristics and destination characteristics were successively added in Model 2 to Model 4 on the basis of Model 1 (see Table 6). The explanatory power of Model 1 to Model 4 was gradually enhanced according to the Pseudo R2.

In Model 1, key independent variable *hukou* was introduced. The positive coefficient suggested that compared with migrants who feel it is not difficult to obtain a local *hukou*, those who find it difficult are more likely to stay in the destination for a long term, but this result is not statically significant. Moreover, the negative value of *very difficult* variable meaning that migrants who found that it is very difficult to obtain a *hukou* locally were more likely to return to their hometowns than to stay is a result that was statistically significant. However, endogenous issues might exist in this simple regression, which mainly comes from the missing variables at the city and individual levels. For instance, *hukou* threshold is closely related to city characteristics such as the economy, population and industrial development. The more developed a city's economy is, the more intensive its industries are, consequently the more attractive it would be to migrants, and the higher the threshold of household registration would be (due to the constraints of population carrying capacity and management capacity of the city). In Model 2–4, the characteristic variables at the individual, migration and destination levels are gradually controlled, which significantly alleviates endogenous problems caused by the omission of variables. The coefficients of *hukou* difficulty are becoming significant in both 'a bit difficult' and 'very difficult'. This indicates that when migrants find it a little difficult to obtain a local *hukou*, but they can

meet the requirements later, they are more willing to stay in the destination for a long time. However, when migrant workers find it is very difficult to meet the *hukou* requirements, they tend to return home in the future rather than stay locally.

**Table 6.** Binomial logistic regression on influential factors of the long-term settlement intention (Ref = Hometown).


*t* statistics in parentheses; \* *p* < 0.05 \*\* *p* < 0.01 \*\*\* *p* < 0.001.

According to the regression results of Model 4, individual characteristic variables have significant impacts on long-term residence intention in destination. Compared with female migrants, male migrants are less likely to stay in the destination. This result is inconsistent with Siu and Unger's findings; they argue that female immigrants do not have much advantage in the labour market, so they are more inclined to stay at home to take care of children and the elderly [44]. One of the possible explanations is that male migrants are more likely to migrate alone, while other family members, such as children and wives, are left behind in hometowns. Thus, male migrants are likely to have a stronger desire to return to their hometown in the long-term. Compared with low-educated migrants, the stay intention of migrants with higher education is stronger and statistically significant. This may be because well-educated migrants are more competitive in the labour market and can better adapt to local life so that they are more willing to stay in the long-term. The results of Model 4 also show that married and aged migrants show less inclination to stay. The odds ratio of land variable is 0.786, indicating that migrants with land rights in their hometown are more inclined to return in the long-term.

Four migration characteristic variables in the model also showed significant correlations with residence intention. Compared with migrant workers who are unemployed, migrant workers with stable employment have a stronger desire to stay in local areas for a long time. It is also worth noting that the higher the position of migrant workers, the stronger the intention of residence. In terms of income level, compared with the reference group whose income level was less than 3500, migrants who earn from 3500 to 5000 are less likely to stay, but the medium and high-income (over 5000) group were not statistically significant. With the increase in migration duration, migrants are more inclined to stay in the destination. This match anecdotal experience that the longer the migrants stays in the local area, the more stable the local social network and living state will be, the stronger the social adaptability to the local will be, and the stronger the residence intention will be. Moreover, the increasing magnitude of migration distance suggests that there is a linear negative relationship between migration distance and migrant workers' stay intention in the destination, but it is not statically significant for medium-distance cross-city migration.

Migrant's stay intention in the destination is also related to destination characteristics. The coefficients of central and western regions variables indicate that migrants who migrate to these two regions are more willing to stay in the local area than those in the eastern region, but it is not significant for the central region. This may be due to the low level of living costs and housing prices in the western region, which encourage migrants to stay in the long term. A stronger level of economic development (GDP per capita) will also significantly enhance migrants' willingness to stay. The main reason is that economic growth will lead to an increase in job opportunities, which will attract migrants to stay in their destinations. Finally, consistent with the preliminary result, there is a negative correlation between education and medical resources and migrants' willingness to stay, that is, the higher the level of these two resources, the more reluctant migrants are to stay. One possible explanation for this result is that at present, the allocation of several key public resources is mainly based on *hukou* in most cities. Migrants without a local *hukou* therefore have to pay a higher price to access many public resources, such as medical fee. Therefore, the unequal distribution of public resources caused by the *hukou* system restrains migrants' willingness to stay.

#### *6.2. Robustness Check*

Based on the above analysis of the current *hukou* system reform, the objective difficulty of obtaining local *hukou* is related to the city scale (see Table 1). Therefore, the robustness test of this part will follow that of previous scholars [8,9] and take objective difficulty (i.e., city scales) as the core dependent variable to further examinate the relationship between *hukou* accessibility and long-term settlement intention. Specifically, we divided the sample cities into three levels according to their population size. More specifically, we divided the sample cities into three levels according to their scale:

Level 1 (most difficult): Beijing, Shanghai, Guangzhou, Shenzhen.

Level 2 (a bit difficult): Shenyang, Chengdu, Hangzhou, Jinan, Ningbo, Qingdao, Suzhou, Wuhan, Xian, Changsha, Chongqing.

Level 3 (not difficult): The rest of the cities.

Interestingly, the results of Model 5 in Table 7 are similar to those of our models above. To be specific, taking migrants in cities without *hukou* threshold (Not difficult) as a control group, those in cities with certain *hukou* difficulty tend to stay local, but this result is not significant. However, for migrants in Beijing, Shanghai, Guangzhou and Shenzhen, their willingness to stay in their destinations for a long time is weakest, and they are more inclined to return to their hometown. The results of the rest of the control variables are the same as those above and will not be repeated here.

**Table 7.** Binomial logistic regression on influential factors of the long-term settlement intention (Ref = Hometown).


*t* statistics in parentheses; \* *p* < 0.05 \*\* *p* < 0.01 \*\*\* *p* < 0.001.

#### **7. Conclusions**

Since the reform and opening up, due to the differences in economic development between urban and rural areas and between regions in China, a large number of migrant workers have flowed from rural areas to cities and from central and western regions to eastern regions. Unable to obtain local *hukou* (household registration), they are not truly local and cannot enjoy their fair share of local public resources. Since 2013, the household registration system has been further reformed. This study advances the body of knowledge by investigating the effect of what E.S. Lee has proposed 'intervening obstacles' in the 'push-and-pull' theory. Based on a nation-wide large-scale survey in 46 Chinese cities, this paper studies the relationship between the difficulty of obtaining a local *hukou* and long-term residence intention. The main conclusions are as follows.

First, an investigation on influence factors on migrants' subjective evaluations on *hukou difficulty* presents that migrants with low education, low income and no property in destination might be vulnerable under the current *hukou* system. This implies that the current *hukou* system mainly unfriendly to migrant workers with low human capital and weak economic conditions.

Second, if other control variables remain unchanged, this paper found that the negative impact of *hukou* difficulty on migrant workers' residence intention is not linear, and only when the threshold in obtaining *hukou* is too high and difficult to achieve will migrant workers choose to return hometown in the long term. This may indicate that after nearly 10 years of household registration (*hukou*) system reform, most cities have gradually achieved equal access to basic public services, and migrant workers can enjoy more public services than before, though not necessarily the same as the local. As a result, *hukou* in many cities is no longer the decisive factor in determining whether migrant workers will stay in the destination for a long time. However, although China's household registration system (*hukou*) reform has been improving, it still hinders migrants' residence intention to some extent and has considerable potential to be optimized. We believe that current household registration (*hukou*) system has two influences on migrants' residence intention: first, migrants who without local *hukou* cannot enjoy public services such as medical services and social security services equally with local people; also, migrants without local *hukou* cannot easily reunite with their families locally because they do not have access to local public resources such as public schools for their children equally. Moreover, the long-standing *hukou* system leads to the lack of parental companionship and care for left-behind children, which has a negative impact on their physical and mental health [45]. That may encourage migrant workers to return home in the long term. In this sense, the application of E.S. Lee's 'intervening obstacles' in the push–pull theorem could be extended to administrative barriers. The mechanism of this obstacle is, however, not as linear as physical distance might do. This provides implication for countries and regions within country globally to facilitate immigration policies and designation of benefits granted to non-citizen. Further research on the threshold that influence residence intention is necessary to collect more empirical evidence for this viewpoint.

Finally, the results of our model show that the human capital level of migrant workers, such as educational background and income level, is negatively correlated with residence intention. This may be because they have always been on the margins of local society and have been unable to integrate into local society due to the restrictions of the *hukou* system.

In terms of policy suggestions, the author suggests that future urban development strategies should give more consideration to migrants, especially in the distribution of educational resources, medical resources and other welfare. Thus, it can promote migrant workers to better integrate into the local society and enhance their willingness to stay. In particular, three policy tools are proposed in line with the findings. First, an 'intervention unobstructed tool' needs to be implemented to hedge the current obstacles. In detail, the current residence permit (similar to greencard that allows migrants who reside in destination for more than half year but have not yet obtained local *hukou*) system is suggested to upgrade so that non-*hukou* migrants could enjoy key settlement benefits

in cities, including safe, clean, affordable housing, equal compulsory education opportunity regardless of parental *hukou* status, higher medical insurance reimbursement ratios. Second, investment on public services and facilities needs to be based on settle population size, rather than size of population with local *hukou*. Third, distribution of national fiscal and land resources in this field is suggested to shift from GDP and income level base to inbound migrants' size base, in order to match the service and settlement demand of incremental migrants under the current taxation schemes in urban China.

There are also some obvious limitations in our research. For example, the sampling time of this study is from 2020. Due to the impact of COVID-19, many migrant workers could not go out for work normally and even chose to return to their hometown, thus causing some deviation in the results. Moreover, the objects of our study are local migrant workers. Migrant workers who have returned to their hometown are not considered, so the problem of survivor bias will also occur in this research.

**Author Contributions:** Conceptualization, H.O.; Formal analysis, Y.W.; Methodology, P.L.; Project administration, P.L.; Resources, H.O.; Supervision, Y.W.; Writing—review & editing, P.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research is funded by the National Social Science Fund (NSSF) Major Program with Grant No. 22ZDA056.

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Institute of Spatial Planning and Regional Economy of the Chinese Academy of Macroeconomic Research (30 May 2021).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data presented in this study are not publicly available to protect the privacy of the study's participants.

**Acknowledgments:** Yufeng Wu would like to thank Rong Zhou for her comments on an earlier draft of this paper.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Jiankun Yang 1,2, Min He <sup>1</sup> and Mingwei He 1,\***


**Abstract:** Analyzing commuting-time satisfaction could help to improve the subjective well-being of society. This study aimed to explore the nonlinear relationship between commuting satisfaction and commuting times in different groups and its influencing factors. An empirical study was conducted in Kunming, China. Firstly, applying a random forest algorithm revealed that there was a nonlinear relationship between commuting satisfaction and commuting time. Secondly, the k-means clustering algorithm was used to divide the respondents into three types of commuter: short-duration-tolerant (group 1), medium-duration-tolerant (group 2), and long-duration-tolerant (group 3). It was found that the commuting-time satisfaction of these three clustered groups had different threshold effects. Specifically, the commuting satisfaction of group 1 showed a nonlinear downward trend, which decreased significantly at 12 and 28 min, respectively; the commuting satisfaction of group 2 rapidly decreased at 35 min; the commuting satisfaction of group 3 first increased in the range of 20–30 min, decreased significantly after 45 min, and decreased sharply above 70 min. These time thresholds were consistent with the ideal commuting times (ICTs) and tolerance thresholds of the commuting times (TTCTs) of the three clustered groups, which indicates that the ICT and TTCT had significant effects on commuting satisfaction. Lastly, the results of the multinominal logistic model showed that variables such as the commuting mode, job–housing distance, income, and educational background had significant effects on the three clustered groups. The policy implications of the study are that commuting circles should be planned with the TTCT as a constraint boundary and ICT as the optimal goal; in addition, different strategies should be adopted for different commuting groups to improve commuting satisfaction.

**Keywords:** commuting satisfaction; commuting time; nonlinearity; group difference; threshold effect; commuting preference and tolerance

#### **1. Introduction**

Commuting satisfaction affects physical and mental health, job performance, life satisfaction, well-being, etc. Studying the influencing factors of commuting satisfaction is important for improving public health, increasing economic efficiency, and promoting social sustainability. Commuting satisfaction is a key indicator for measuring citizens' subjective well-being [1–3], evaluating the level of urban transport services [4,5], and evaluating sustainable social development [6,7]. How to improve commuting satisfaction is a common concern for city managers, planners, and researchers in related fields. The factors affecting commuting satisfaction mainly include the commuting time [8,9], commuting mode [10–13], built environment [14,15], service level [16,17], and perceived attitude [18,19]. For the commuting mode, there are a few reasons why walking or cycling are associated with higher satisfaction, such as moderate commuting times and lower commuting costs [20], more exposure to green space [21], increased social interaction, and the promotion of physical and mental health [22]. Congestion is the reason for low

**Citation:** Yang, J.; He, M.; He, M. Exploring the Group Difference in the Nonlinear Relationship between Commuting Satisfaction and Commuting Time. *Sustainability* **2022**, *14*, 8473. https://doi.org/10.3390/ su14148473

Academic Editors: Mengqiu Cao, Claire Papaix, Tianren Yang and Benjamin Büttner

Received: 3 April 2022 Accepted: 1 July 2022 Published: 11 July 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

satisfaction with car commuting [23,24]. Service levels regarding transfers, connections, departure frequencies, platform facilities, and information acquisition are the reasons for low satisfaction with public transportation (buses and subways) [25–27]. For built environments, the residents of compact urban neighborhoods have better commuting satisfaction than residents of sprawling suburban neighborhoods [28]; the walking satisfaction can be explained by the safety, lack of congestion, and cleanliness of sidewalks [29]; the availability of bike lanes and whether buses are running along the bike lanes affect the commuting satisfaction for cyclists [30]. For preferences and attitudes, a mismatch between the chosen commuting mode and preferred commuting mode has a negative impact on commuting satisfaction [11,31]; commuters who have a positive attitude towards commuting activities have higher satisfaction levels [32–34].

Commuting time is seen as an important influence on commuting satisfaction, the complex relationship between commuting satisfaction and commuting time has been a focus of attention [9,35,36]. When other variables are controlled, some research argues that there is a negative linear effect of commuting time on commuting satisfaction [37–39]. However, other research found, through hypothetical experiments, that there is a nonlinear relationship between commuting satisfaction and commuting time, with ideal commuting times (10–20 min) and acceptable commuting times (30–40 min) being responsible for this nonlinear relationship [40]. People have the best commuting experience and perceived emotions at the ideal commuting time [41]; conversely, when the commuting time exceeds the acceptable or tolerable threshold, they show significant negative emotions and attitude evaluations [42]. The results of statistical modeling support a weak positive effect of an ideal commuting time on commuting satisfaction [43], while the effect of tolerance thresholds on satisfaction has rarely been empirically studied.

The above results have opened up new perspectives for exploring the complex relationship between commuting satisfaction and commuting time. However, three questions deserve further exploration. Firstly, do the nonlinear characteristics of commuting-time satisfaction differ between hypothetical and actual contexts? Analyzing this issue can help to understand the impact of commuting time on satisfaction from both subjective and objective perspectives, and then formulate more effective policies to improve commutingtime satisfaction. Secondly, although the ideal commuting time has a positive effect on commuting satisfaction, the magnitude of the positive effect is relatively small, which leads to the question of whether all commuting groups are the most satisfied around the ideal commuting time. Studying this question can compensate for the lack of attention in the existing literature to group heterogeneity in satisfaction with ideal commute time. Thirdly, both the ideal commuting time and the tolerance threshold have group differences, and these two subjective time thresholds are the key points at which commuting satisfaction changes; therefore, is there also a group difference in commuting-satisfaction change with commuting time, and which factors can explain these differences? Exploring this question contributes to closing the gap in the published literature on commuting satisfaction in terms of nonlinearity and heterogeneity to develop differentiated and personalized urban transport policies.

To answer the above three research questions, firstly, the overall laws of commutingsatisfaction change regarding the hypothetical commuting time and actual commuting time were compared. Next, *k-means clustering* was conducted by combining the actual, ideal, and tolerance values of the respondents' commuting times, and classifying them into three groups: short-duration tolerance, medium-duration tolerance, and long-duration tolerance. Furthermore, *the random forest algorithm* was applied to examine the group difference in commuting-time satisfaction. Finally, *a multinomial logistic regression model* was developed to identify the explanatory variables significant for the three clustered groups.

The novelty of this study is threefold: firstly, it shows that commuting satisfaction is inconsistent with regard to the hypothetical commuting time and actual commuting time, which means that commuters' attitudes to commuting times in hypothetical scenarios are different from their perceived experiences of actual commuting times. Previous

studies focused either on the relationship between commute satisfaction and hypothetical commute time or on the relationship between commute satisfaction and actual commute time; they did not analyze the difference in changes in commute satisfaction between these two scenarios. This findings tells us that when developing an optimization strategy for commute-time satisfaction, we should not only start from hypothetical scenarios, it is also necessary to integrate actual situations. Secondly, a fresh finding is that ideal or moderate actual commuting times have a positive effect on commuting satisfaction only for the long-duration-tolerance commuting group, which is not universal. The implication of this finding is that strategies to improve commuting satisfaction by shortening commuting time to ideal expectations are the most effective for long-duration-tolerance commuters. Lastly, the study reveals that there is a group difference in the nonlinear relationship between commuting satisfaction and the actual commuting time; on this basis, it was verified that the commuting-time boundary points that caused these nonlinear changes were close to the ideal commuting time and tolerance threshold of the commuters. Considering individual preferences and tolerance for commuting, this study provides a new perspective for analyzing the threshold effect of commute time satisfaction. This finding enriches the knowledge of threshold theory in terms of commuting satisfaction nonlinearity and heterogeneity.

The second part of the article provides a literature review of the relationship between commuting satisfaction and commuting time. The third section introduces the study's objectives, data, and methods. The fourth section presents the findings and discusses them. The final section draws conclusions and policy implications and outlines the limitations.

#### **2. Literature Review**

Commuting satisfaction is a perceptual emotional and cognitive evaluation of the difference between commuters' expectations of service levels and their actual commuting experiences [41]. Higher levels of travel services lead to better perceptions and emotions [44]. Commuting time is a key measure of the service level; it has a significant impact on commuting satisfaction. Some research concludes that commuting satisfaction is negatively correlated with commuting time. Olsson et al. [45] found that the longer the actual commuting time (ACT), the lower the commuting satisfaction. Higgins et al. [24] showed that the proportion of dissatisfied samples became larger as the ACT increased. Zhu et al. [46] revealed that trip duration had a negative association with commuting mood. Two empirical studies in China have also shown that commuting satisfaction decreases with increasing ACT [47,48].

Other research shows a nonlinear relationship between commuting satisfaction and commuting time. Young [49] found that commuting satisfaction rose first and then decreased with the hypothetical commuting time (HCT). Subsequently, the relevant literature on the positive utility of an ideal commuting time and the negative utility of an acceptable (tolerable) commuting time emerged [50–54]. The ideal commuting time (ICT) reflects commuters' preferences for commuting times; people's ICTs are mainly around 10–20 min [55,56], while the tolerance threshold for commuting times (TTCT) reflects commuters' tolerance of commuting times; people's TTCTs was 30–40 min [57,58]. The researchers asked the respondents to evaluate their satisfaction with different hypothetical commuting times; they found that the commuting satisfaction increased before an HCT of 15 min, while it dropped sharply after 30 min, showing significant nonlinear distribution characteristics [36,40,42]. Zhao et al. [59] found that commuting satisfaction was highest when the commuting time was 10–30 min.

Recently published literature strengthens the research on the nonlinearity of commutetime satisfaction. The route-analysis model constructed by Humagain et al. [43] showed that the ICT had a weak positive effect on commuter satisfaction. From the perspective of commuter cognitive dissonance, Ye et al. [41] showed that commuter satisfaction increased before the ICT and decreased after the ICT. Jang et al. [9] obtained an opposite nonlinear relationship through machine learning; that is, commuter-time satisfaction first decreased (0–35 min) and then increased (36–70 min). The reason behind this is that some commuters

are willing to accept a longer commuting time to obtain a better living environment. Further research by Humagain et al. [36] observed group differences in the relationship between commuting satisfaction and HCT and showed that the nonlinear relationship only applied to a small number of commuters.

These research findings have opened a new window for exploring the complex relationship between commuting satisfaction and commuting time. However, two questions still need to be explored. Firstly, is there a difference between the hypothetical commutingtime satisfaction (experimental scenario) and actual commuting-time satisfaction (objective reality)? In addition, both the ideal commuting time and the tolerance threshold have group differences, and these two subjective commute time boundary points have a significant impact on commuter satisfaction, which means that it is valuable to reveal group differences in commute-time satisfaction from the perspective of commuters' preferences over and tolerance of commute times.

#### **3. Research Data, Objectives, and Methods**

#### *3.1. Implementation of the Survey*

As the capital city of China's Yunnan Province, Kunming is a regional international city in Southwest China. "*The Commuting Monitoring Report for 36 Major Cities in 2020, China"* shows that the commuting-monitoring indicators for Kunming are similar to those of other cities [60], which means that using Kunming as a study case is representative. The paper-assisted personal interviewing (PAPI) technology was used to implement the survey. The PAPI implementation steps are "design questionnaires, train investigators, conduct trial surveys, optimize questionnaires, conduct formal surveys, eliminate invalid questionnaires, and establish a database". Although the implementation cost of the PAPI survey method is relatively high, its advantage is that investigators not only provide necessary explanations to respondents' questions through face-to-face interviews, but also directly observe the statuses of the respondents filling in the questionnaires, which is helpful for preliminarily judging the quality of returned questionnaires.

Taking into account the aggregation of the people flow, the spatial distribution of the samples, and the feasibility of the implementation, the survey selected eight core commercial complexes in different locations. The eight commercial complexes were the Joy-City Business Center (Wuhua District), Shuncheng Business Center (Wuhua District), Tongde Plaza (Panlong District), Wanda Plaza (Xishan District), Dadu Shopping Mall (Guandu District), International Ginza Complex (Guandu District), Wuyue Plaza (Chenggong District), and No.1 City of Colorful Yunnan (Chenggong District). The specific survey locations were mostly cafes, milk-tea shops, bookstores, and parent waiting areas in children's training centers. The consumers in these places are generally in a leisure state, and they were more willing to listen to the surveyor's introduction and agree to the survey.

Before the survey was carried out, the supervisor conducted the necessary training for the investigators, so that the investigators could master the precautions and basic skills for a random sampling survey. Two investigators formed a group; one was responsible for instructing the respondents to fill in the questionnaire, and the other was responsible for recording the respondents' times for answering the questionnaire. The investigators uniformly wore white work clothes with a logo and wore the official investigation work permit issued by the institute on their chests, to gain the trust and support of the customers as much as possible. The investigators randomly asked the customers if they were willing to take the survey, and told them that, if they completed the questionnaire, they would receive a red envelope with RMB 10 of cash, which reduced the rejection rate and encouraged the respondents to fill out the questionnaire carefully.

The final version of the questionnaire was revised based on the feedback from the pilot survey in April 2020. Two offline random sample surveys were conducted on commuters in Kunming. The first formal survey was conducted in May 2020, obtaining 352 valid samples (sample 1); the second formal survey was conducted in January 2021, collecting 224 valid samples (sample 2). Through data cleaning, samples that were incomplete and with answering times of less than 8 min (an empirical value obtained in the trial survey) were eliminated, and 576 complete samples were finally obtained. The average commute time of sample 1 and sample 2 were 28.1 and 26.1 min, respectively. The Mann– Whitney U test was performed on these two independent samples, and it was found that their commute-time distributions were not significantly different (*p* > 0.1); The average commuting satisfaction of sample 1 and sample 2 were 4.5 and 4.7, respectively; the Mann–Whitney U test showed that there was no significant difference in the distribution of commuting satisfaction between these two independent samples (*p* > 0.1). These test results demonstrated that there was no statistical difference between the two samples, and they could be combined for this study. Furthermore, in sample 1, the proportions of active commuting, cars, e-bikes, and public transportation were 21.9%, 33.0%, 20.1%, and 25.0%, respectively, while in sample 2, the proportions of these four commuting modes were 25.9%, 26.9%, 16.7%, and 30.6%, respectively. These two sets of data were similar, indicating that there was no obvious deviation between the two samplings.

#### *3.2. Data from the Survey*

#### 3.2.1. Commuting Time

Three questions were set in the questionnaire to obtain the respondents' ACTs, ICTs, and TTCTs. Question 1: "On average, how many minutes does it take to commute from your residence to your workplace by your most frequently used commuting mode?" (ACT); question 2: "What is your preferred ideal commute time in minutes from your residence to your workplace?" (ICT); and question 3: "What is the maximum commute time you can tolerate in minutes from your residence to your workplace?" (TTCT). The ACT reflects a retrospective estimate of the respondents' average daily commuting time in actual situations. The ICT presents the respondents' ideal preferences for commuting times in hypothetical situations. The TTCT refers to psychologically tolerable or acceptable commuting times for the respondents.

The average ACT, ICT, and TTCT obtained in this survey were 27.1, 17.5, and 37.8 min, respectively. In addition, as shown in Table 1, the proportion of the respondents whose ACTs were within their ICTs was 29.8%, while that of the respondents whose ACTs exceeded their TTCTs was 18.3%; these findings are similar to those of other researchers. A survey conducted in Kunming in 2014 found that the average ACT, ICT, and TTCT were 28.7, 18.6, and 37.4 min, respectively; in addition, the ACT was less than or equal to the ICT in 28.7% of their samples, and the ACT was greater than the TTCT in 15.3% of their samples [61].


**Table 1.** The description of the samples.


**Table 1.** *Cont.*

#### 3.2.2. Commuting Mode

The commuting mode in this study refers to the travel mode respondents most frequently used to commute from their residence to the workplace on workdays. All the samples included four types of commuting mode: active commuting (walking, cycling, and shared bicycles), cars (private cars, taxis, and shared cars), e-bikes (electric bicycles, electric mopeds), and public transportation (subway and buses). E-bikes mainly include three types of electric bicycles, electric mopeds, and electric scooters [62]. The e-bikes in this study refer to electric mopeds (motorcycle type, driven by electric motors, without pedals) and electric bicycles (bicycle type, mainly powered by electricity, supplemented by human power, with pedals). In Kunming, the users of such e-bikes need to register them with the relevant government department and apply for a license.

The proportions of cars and public transportation were 29.8% and 27.8%, respectively. These survey data are slightly higher than the corresponding statistical data in the "*Kunming Urban Transportation Annual Development Report in 2019, China*", which reports that the travel sharing rates for cars and public transportation were 25.9% and 25.4%, respectively [63]. Since the report's classification of other commuting modes is inconsistent with this study, no explanation for the sample proportions of active commuting and e-bikes is provided here.

It should also be pointed out that the reason e-bikes are listed separately in this study is that the commuting share rate of e-bikes is not low in Kunming [64]. Furthermore, there are essential differences in the performance functions (speed, acceleration/deceleration, and physical energy consumption) of the e-bike and active modes (walking and cycling) [65]. Listing e-bikes as active commute modes would not be conducive to analyzing the group differences in commuting satisfaction. In addition, during the questionnaire survey, there was no COVID-19 spread in Kunming; its urban commuting system operated as usual; which can be intuitively inferred from the above data on the commuting time and commuting mode.

#### 3.2.3. The Job–Housing Relationship

The questionnaire asked about the names of the communities where the respondents lived and worked. On the Baidu map, the centroid of the community where each respondent lives and works were marked, and each interviewee's job–housing relationship was measured based on the straight-line distance between the two centroids (SDTC). We referred to the indicator of happy commuting (commuting distance within 5 km) and the average commuting distance in Kunming (7.5 km) in "*National Commuting Monitoring Report for Major Cities in 2020, China*" [60]. The respondents' job–housing relationships were divided into three ordered categories: SDTC within 4 km was defined as a job–housing balance; SDTC within the range of 5 to 9 km was defined as the mild job–housing distance; SDTC exceeding 9 km was defined as severe job–housing distance.

#### 3.2.4. Commuting Satisfaction

This study measured commuting satisfaction with the HCT [40]. A total of six hypothetical scenarios were set up, each of which corresponded to a different HCT. An option that matched the respondent's attitude reflection from five satisfaction options (very satisfied, satisfied, neutral, dissatisfied, and very dissatisfied) was chosen. The first scenario had a commuting time of 0 min, with an additional note that it meant "telecommuting"; the second scenario had a commuting time of 15 min because people's ICTs were concentrated in the range of 10–20 min [41,50,55,56]; therefore, setting the items in this way was helpful for analyzing the changes in commuting satisfaction around the ICT; the commuting times of the third and fourth scenarios were 30 and 45 min, respectively, because the respondents' acceptable or tolerable commuting times were mainly concentrated in the range of 30–45 min [36,42,57,58], and the context set in this way helped to reveal the changing characteristics of commuting satisfaction when the commuting time approached or exceeded the TTCT. To analyze the impact of long or extreme commuting [66–69] on commuting satisfaction, the last two scenarios of commuting times of 60 and 75 min were also set.

This study also measured the respondents' satisfaction with their actual commutes. The *scale of travel satisfaction* (STS) focuses on the two dimensions of commuters' cognitive and affective evaluations of commuting activities in reality [70]. A total of nine items were set in the STS to measure commuting satisfaction. The STS has been proven to be practical and reliable by some empirical studies [23,71,72]. To ease the test burden on respondents, Ye et al. [41,73] reduced the items of the STS to seven and applied it to a commuting-satisfaction study of Xi'an citizens, in China. Since their results showed that the reduced version of the STS (STS-R) was still effective, this study also applied it. The seven items of the STS-R are shown in Table 2. In each item, −3 means very dissatisfied, 0 means neutral, and +3 means very satisfied. The reliability and validity of the seven questions of the STT-R were tested; the results showed that *Cronbach's alpha* and *KMO* were 0.923, 0.915, indicating that the internal consistency of the STT-R was good and the seven survey questions were valid.



#### *3.3. Objectives and Methods*

Research objective 1: Examining differences in commuter satisfaction with changes in HCT and ACT, respectively. On one hand, the commuting satisfaction under the hypothetical scenario was categorized; those who selected "very satisfied" and "satisfied" were defined as the satisfied type, while those who selected "very dissatisfied" and "dissatisfied" were defined as the dissatisfied type, and those who selected the intermediate option were defined as the neutral type. For each HCT, the proportions of respondents in these three categories were counted separately to plot the distribution of the commuting satisfaction along with the HCT. On the other hand, for ease of calculation, 4 was added to each measured value obtained by STS-R, so a score of 1 represented very dissatisfied, a score of 4 represented neutral, and a score of 7 represented very satisfied. On this basis, the arithmetic mean of the seven question items was calculated, which was the actual commuting satisfaction. *A random forest algorithm* was used to explore the nonlinear relationship between actual commuter satisfaction and the ATC.

Research objective 2: Revealing the differences in commuting satisfaction with ACT among clustered groups. First, *the k-means clustering algorithm* [74] was used to divide all the respondents into different groups. Second, *the random forest algorithm* [75] was applied to draw the relationship of local dependency between commuting satisfaction and ACT for clustered groups. Furthermore, the respective nonlinear characteristics of their commuting-time satisfaction could be intuitively observed. Finally, from the perspective of commuters' psychological preferences and tolerance of commuting times, the ICTs and TTCTs of the clustered groups were introduced and combined with the nonlinear changing characteristics of their commuting-time satisfaction to capture the time-threshold effect of commuting satisfaction.

Research objective 3: Identifying influencing factors of clustered groups with differences in commute time satisfaction. *A multinominal logistic regression model* [76] was set up, with the clustered groups of different commuting-time satisfaction levels as the unordered categorical dependent variable. The independent variables were commuting mode, job–housing relationship, and individual characteristics.

#### **4. Results and Discussion**

#### *4.1. Change in Commuting Satisfaction with Hypothetical Commuting Time and Actual Commuting Time*

Figure 1 shows the change in the sample proportion distribution of the commuting satisfaction under the hypothetical situation. When the HCT was 15 min, the proportion of respondents who were satisfied was the highest, reaching 81%, which supports the previous research results; that is, when the commuting time is close to the ICT (10–20 min), the commuting satisfaction is the best [36,40,42]. This phenomenon can be explained by cognitive dissonance theory, which states that people's feelings and experiences are the best when attitudes (ideal preferences) and behaviors (real situations) are in harmony [77]. As the HCT of 15 min aligns with people's ideal commute time, this value could offer commuters the best experience. In addition, commuting utility theory can also be used as an explanation for this phenomenon, which holds that ideal commuting time brings positive utility to people [50].

**Figure 1.** The proportions of commuting satisfaction under hypothetical commuting times.

When the HCT was 0 min, the proportion of respondents who were satisfied dropped by 5 percentage points, but it was still as high as 76%, which indirectly shows that the respondents had relatively optimistic responses to telecommuting. Furthermore, this proportion is larger than the findings of other scholars [36,40,42,49] and smaller than those of Humagain [36], which may be related to the differences in the study region, and the possibility that the demand for telecommuting was stronger during the epidemic cannot be ruled out.

When the HCT increased from 15 to 30 min, the proportion of the respondents who answered that they were satisfied quickly dropped from 81% to 39%, while the proportion of the respondents who answered that they were dissatisfied increased from 6% to 23%. Similarly, when the HCT increased from 30 to 45 min, the proportion of respondents expressing dissatisfaction increased significantly, from 23% to 64%. The explanation for these statistical results is similar to the findings of other countries; that is, people have acceptable or tolerable thresholds for commuting times at the psychological level [36,40,42,57]. There was a significant drop in perceived satisfaction when the HCT exceeded 60 min; the majority of the respondents stated that they were dissatisfied; this is in line with reality, because people's perceived moods under long or extreme commuting are mostly negative [38,67]. Interestingly, when the HCTs were 45, 60, and 75 min, 12%, 5%, and 4% of the samples responded with satisfaction, respectively, indicating that a small number of the respondents still showed a willingness to accept long commuting times, which may have been the result of respondents' benefit trade-offs [8]. These results suggest that it is necessary to explore group differences in commuting-time satisfaction.

Generally, there was a nonlinear variation in the commuting satisfaction with the HCT. The participants responded positively to telecommuting. The commuting satisfaction presented a weak growth trend in the range of 0–15 min. When the HCT was in transit from 15 to 30 min, the commuting satisfaction decreased for the first time. When the HCT exceeded 30 min and approached 45 min, the commuting satisfaction decreased significantly for the second time. However, this is only a statistical result under hypothetical conditions it is necessary to further explore the changing characteristics of commute-time satisfaction in actual situations.

To reveal how commuting satisfaction changes with the ACT, the *random forest algorithm* was used to plot the local dependence relationship of the respondents' commuting satisfaction with the ACT. Taking 70% of the samples as the training set and 30% of the samples as the test set, the goodness-of-fit (explained variability) of the model was 77%. As shown in Figure 2a, the overall trend of the commuting satisfaction decreased nonlinearly as the commuting time increased. The decline in commuting satisfaction was significant before the ACT of 30 min; particularly within the range of 20–25 min, the decline in commuting satisfaction was the greatest; after 30 min, the decline in commuting satisfaction was relatively flat; few upward trends in commuting satisfaction were observed before the ACT of 20 min.

These results suggest that the respondents' stated commuting satisfaction in the hypothetical scenario was not entirely consistent with their perceived commuting satisfaction in the face of the ACT. Although the commuting satisfaction showed nonlinear changes with commuting time in both contexts, the positive effect of a moderate or ideal commuting time on commuting satisfaction was not captured in reality; furthermore, only 5% of the sample showed an increase in commuting satisfaction, even when the HCT increased from 0 to 15 min. Therefore, one question that arises is whether the notion that ideal commute times lead to positive commuting satisfaction is only applicable to a minority of commuters, and not a universal observation.

**Figure 2.** *Cont*.

**Figure 2.** The nonlinear relationship between commuting satisfaction and actual commuting time. Note: (**a**) Total sample; (**b**) Group 1; (**c**) Group 2; (**d**) Group 3.

#### *4.2. Group Differences in the Nonlinear Relationships between Commuting Satisfaction and Actual Commuting Time*

The question of whether there was a group difference in the relationship between commuting satisfaction and commuting time was explored. First, the total samples were *k-means* clustered with three indicators: ICT, TTCT, and ACT. These variables reflected the three dimensions of the commuters' ideal preferences, tolerance levels, and actual experiences of commuting times, which is more comprehensive than clustering with only one of them. Because these three indicators are group differences, the clustering groups obtained by them may help to explore the group difference in commuting-time satisfaction.

The theoretical basis for clustering the respondents using these three variables is that the ICT reflects commuters' preferences in terms of commuting times [50,53]. When the ACT is close to the ICT, the perceived satisfaction of commuters is better [41,43,78]. While the TTCT reflects commuters' tolerance of commuting times [57,58], When the ACT approaches or even exceeds the TTCT, the negative motions of commuters significantly increase [54], which leads to a sharp satisfaction decrease [40,42]. In addition, the advantage of clustering respondents in this way is that it not only reflects the relationship between the actual commuting time and commuting satisfaction, but also helps to reveal the specific impact of respondents' subjective commuting-time boundary points on commuting satisfaction.

As shown in Table 3, the average TTCTs for the three commuting groups obtained by clustering were 28.8, 39.0, and 63.7 min, respectively. In addition, the distribution of the TTCTs of the three clustered groups was found to be significantly different (*p* < 0.001) by the *Kruskal–Wallis* non-parametric test, which further illustrates the validity of the clustering. This study refers to the three clusters as "group 1: short-duration-tolerance commuters", "group 2: medium-duration-tolerance commuters", and "group 3: long-duration-tolerance commuters", respectively.

**Table 3.** Average ICTs, ACTs, and TTCTs for three clustered groups.


Next, the *random forest algorithm* was used to establish the local dependence relationship between the commuting satisfaction and ACT of each clustered group. In each clustered group, 70% of the samples were used as the training set and 30% of the samples were used as the test set. The goodness-of-fit (variability explained) of the *random forest models* for these three cluster groups was 72%, 84%, and 68%, respectively. As shown

in Figure 2b, the commuting satisfaction of group 1 changed slowly before the ACT was 15 min, and decreased in the 15–25 min range; when the ACT exceeded 25 min, the commuting satisfaction decreased significantly. As shown in Figure 2c, the commuting satisfaction of group 2 decreased rapidly when the ACT was about 35 min. However, when the ACT exceeded 40 min, the decrease in commuting satisfaction was significantly weakened, and a slight rebound occurred after 50 min. As shown in Figure 2d, when the ACT was between 20 and 30 min, the commuting satisfaction of group 3 showed an upward trend, and it declined after 45 min. Especially when the ACT exceeded 70 min, the commuting satisfaction sharply declined; this is yet another demonstration of the negative impact of extreme commuting on commuting experience.

These nonlinear threshold effects can be clearly explained from the perspective of commuting individuals' preferences regarding and tolerance of commuting times. The average ICT and TTCT for the short-duration-tolerance commuting group were 13.6 and 28.8 min, respectively, which were close to the time thresholds at which the commuting satisfaction for this group decreased significantly. The average ICT and TTCT for the longduration-tolerance commuting group were 24 and 63.7 min, respectively, which fell exactly in the rising and falling range of the commuting satisfaction for this group. Similarly, the average TTCT for the medium-duration-tolerance commuting group was 39 min, which coincides with the time threshold for a rapid decrease in commuting satisfaction for this group. The behavioral threshold theory is helpful for analyzing changes in commutingtime satisfaction [40]. The theory contends that commuters have an acceptable or tolerable threshold for commuting times. Different commuter groups may have different tolerance thresholds, so the time-threshold effect of commuting satisfaction is also different.

Firstly, these results show that there was a nonlinear relationship between commuting satisfaction and commuting time. The decline in commuting satisfaction with commuting time exhibited nonlinear characteristics that changed significantly at specific thresholds, and these commuting-time thresholds were very close to the average ICT and TTCT. Secondly, there was a group difference in the nonlinear relationship between commuting satisfaction and commuting time. The commuters who tolerated longer commuting times tended to have a larger time threshold, which affected their commuting satisfaction with a significant decrease. Finally, in the lower range of the actual commuting times, the commuting satisfaction of group 1 did not change significantly, and the commuting satisfaction of group 2 decreased slightly, while the commuting satisfaction of group 3 increased. This suggests that ideal or moderate commuting times have no apparent negative perceived utility for the majority of commuters; instead, there is a positively perceived utility for longduration-tolerance commuters. Without a different group breakdown of the commuters in terms of actual commuting time, the local characteristics of the positive effect of the commuting time on commuting satisfaction would not be visible. It would also not be possible to capture the time threshold that led to a significant decrease in commuting satisfaction among the different commuting groups.

#### *4.3. The Influencing Factors of the Clustered Group with Different Levels of Commuting Satisfaction*

To identify the influencing factors of the clustered groups with different levels of commuting satisfaction, we visualized the average satisfaction and the proportional distribution of the three clustered groups for the commuting mode, job–housing relationship, and individual characteristics. Next, we constructed *a multinominal logistic regression* model to test the statistical significance.

As shown in Table 4, group 3 had the largest proportion of public transport commuters, group 1 had the largest proportion of active commuters, and the proportion of car commuters was the largest in group 2. The four commuting modes in descending order of commute satisfaction were walking and cycling (5.33), e-bikes (4.75), public transportation (4.61), and cars (4.50), which is roughly the same as the conclusions of other studies. Active commuting always results in the highest satisfaction [10,12,73]; sometimes car commuting

has the lowest satisfaction [19]; and sometimes public transit commuting has the lowest satisfaction [10,13]. This may be related to regional differences.


**Table 4.** Sample distribution of three clustered commuter groups.

The largest proportion of respondents whose job–housing distance was balanced belonged to group 1; the largest proportion of respondents whose job–housing distance was mild belonged to group 2. The average commuting satisfaction of the commuters with a balanced job–housing distance was 4.97; that of commuters with a mild job–housing distance was 4.56; and that of the commuters with a severe job–housing distance was 4.30. These data show that the more balanced the job–housing distance, the higher the commute satisfaction.

The smallest percentage of male respondents belonged to group 1, while the largest percentage of female respondents belonged to group 1. The male respondents had a higher average commute satisfaction than the female respondents. The respondents with highschool degrees and below comprised the largest proportion in group 1 and displayed the highest levels of commute satisfaction; the respondents with postgraduate education comprised the smallest proportion in group 1 and had the lowest commute satisfaction. The proportion of respondents with a personal monthly income of more than RMB 7000 in group 1 was 19.7%, and their commuting satisfaction was 4.63, which was lower than for those with a personal monthly income of less than RMB 7000. In terms of the respondents' ages, group 1 had the largest percentage of respondents aged 18 to 30; the respondents were less satisfied with their commute as they grew older.

The above descriptive statistics show that the respondents with different commuting modes, job–housing relationships, and individual characteristics had different likelihoods of belonging to the different clustered groups, and their average satisfaction was also different.

As shown in Table 5, the *likelihood ratio test* of the model was significant, indicating that the independent variables helped to improve the explanatory power of the model. In addition, the *three pseudo-R-square values* (*McFadden, Cox–Snell, and Negorko*) of the model were 0.140, 0.242, and 0.281, respectively, which showed that the model explains about 20% of the variance of the original variable.


**Table 5.** The fit information for the model.

As shown in Table 6, the estimation results of each independent variable in the model were as follows. Compared with group 2, the active commuters were more likely to belong to group 1, and this probability was 7.14 (100/14) times that of the public transport commuters; the e-bike commuters were more likely to belong to group 1, and this probability was 3.45 (100/29) times that of the public transport commuters. Compared with group 1, the probability of active commuters, e-bike commuters, and car commuters belonging to group 3 was 0.133, 0.270, and 0.516 times that of the public transit commuters, respectively. These results suggest that the active commuters were the most likely to comprise the shortduration-tolerance commuting group, followed by the e-bike commuters, while the publictransit commuters were the most likely to be in the long-duration-tolerance commuting group, followed by the car commuters. As shown in Table 7, the survey data also showed that the average ACT and TTCT of the public-transport respondents were 34.2 and 42.6 min; those of the respondents who commuted by e-bike were 22.9 and 36.9 min, while those of the active commuters were 16.9 and 31.9 min.

Compared with group 2, the odds of commuters with a balanced job–housing distance being in group 1 were 5.75 (1000/174) times those of commuters with a severe distance between in their job and their housing; the odds of commuters with a mild job–housing distance being in group 1 were 2.50 (1000/400) times those of commuters with a severe job–housing distance. These results show that the more balanced the job–housing distance for the commuters, the more likely they were to belong to the short-duration-tolerance commuting group. Compared with group 1, commuters with a balanced job–housing relationship were 0.225 times more likely to be in group 3 than commuters with a severe distance between their work and their housing; the odds of commuters with mild job–housing distances being in group 3 were 0.424 times those of commuters with severe job–housing distances, which means that the greater the job–housing distance, the more likely the commuters were to belong to the long-duration-tolerance commuting group. Generally, the greater the job–housing distance, the longer the commuting time. The estimated results are in line with reality. In the survey data, the ACT and TTCT of the respondents with balanced job–housing relationships were 22.8 and 35.8 min, respectively, while those of the respondents with severe job–housing distances were 37.2 and 44.4 min, respectively.

Compared with group 1, the odds of commuters with college and undergraduate degrees being in group 2 or 3 were 0.438 and 0.472 times those of commuters with postgraduate degrees and above. Compared with group 3, the odds of commuters with a high-school degree or below being in group 1 were 4.1 (1000/244) times those of commuters with a Master's degree or doctorate. These results show that the commuters with higher education levels were more likely to be in the long-duration-tolerance commuting group, and the commuters with lower education levels were more likely to be in the shortduration-tolerance commuting group. Compared with group 3, the odds of commuters whose monthly incomes were RMB 5000–7000 being in group 1 were 4.99 (1000/502) times those of commuters whose monthly incomes were more than RMB 7000, which indicates that the lower the monthly income, the more likely the commuters were to be in the short-duration-tolerance commuting group.


**Table 6.** Results of the multinominal logistic regression model.

A possible reason behind these results is that most of the low-education and lowincome groups are migrant workers in cities in China; they rarely own property in the city and usually choose to rent apartments near their work locations or live in companyprovided dormitories; therefore, their commuting times are relatively short, which also leads to a low level of commuting tolerance for this group. This reasoning is supported by the data in Table 7; the ACT and TTCT for the respondents with high-school or technical secondary school degrees were 24.2 and 34.5 min, respectively, which were lower than those for those with a college or university educational background (26.7 and 37.5 min) and those with postgraduate educational background (34.5 and 42.8 min). At the same time, the proportion of respondents with a low level of education and a balanced job– housing distance was 69.1%, which was significantly higher than that of the other two categories (61.0% and 45.9%).

The ACT and TTCT for the respondents with monthly personal incomes of less than RMB 7000 were 26.2 and 37.2 min, respectively, both of which were lower than those with monthly personal incomes of more than RMB 7000 (29.6 and 39.9 min). Meanwhile, the corresponding sample shares for these two categories of respondents with balanced job–housing relationships were 65% and 45.9%, respectively.

The model results also suggest that, compared with group 1, male commuters were more likely to be in group 2, and this probability was 1.594 times that of female commuters. In Chinese family structures, women have more family responsibilities, women are more likely to work close to where they live, which leads to women having shorter commuting times. The proportion of male commuters with severe job–housing distance was 2.9% higher than that of female commuters. The age of the commuters did not show statistical significance, which may have been due to the relatively low age of the respondents. Therefore, the samples could not effectively represent the overall characteristics of the commuters.


**Table 7.** Average commuting times and job–housing relationships of the different commuting groups.

#### **5. Conclusions**

This study took Kunming as a case study and revealed the nonlinear relationship between commuting satisfaction and commuting time in both hypothetical situations and actual situations. First, it was found that the nonlinear relationship between commuting satisfaction and hypothetical commuting times differed from that between commuting satisfaction and actual commuting times. Second, the three variables of the ideal commuting time, actual commuting time, and commuting-time tolerance threshold of the commuters were integrated to cluster the samples. On this basis, the random forest algorithm was used to reveal the nonlinear relationship between the commuting satisfaction and actual commuting times of the different clustered groups. Furthermore, from the perspectives of their preferences regarding and tolerance of commuting times, the threshold effect of the commuting times on commuting satisfaction was analyzed. Thirdly, some factors that could explain why the nonlinear relationship between commuting satisfaction and commuting time had group differences were extracted.

The conclusions of this study are as follows. Firstly, there was a nonlinear relationship between the commuting satisfaction and commuting times in both the hypothetical and actual situations, but their respective nonlinear features were not consistent. Specifically, the commuting satisfaction was better when the hypothetical commuting times were 0 and 15 min, and there was a slight increase in the range of 0–15 min; when the hypothetical commuting time increased from 30 to 45 min, the commuting satisfaction decreased significantly. In the actual situation, the commuting satisfaction of the whole sample tended to decrease slowly in the range of actual commuting times of 0–15 min, while there was a rapid decrease in the range of 22–28 min, with a continued decrease after the actual commuting time of 30 min, but at a slower rate. It was therefore concluded that the positive effects of moderate or ideal commuting times on commuting satisfaction only apply to a minority of commuters.

Secondly, there was a group difference in the nonlinear relationship between commuting satisfaction and actual commuting times. The commuting satisfaction of the shortduration-tolerance commuting group did not change significantly at 0–15 min, tended to decline above 15 min, and decreased significantly beyond 25 min. The commuting satisfaction of the medium-duration-tolerance commuting group decreased significantly at 35 min. The commuting satisfaction of the long-duration-tolerance commuting group increased with the actual commuting time (20–30 min), then fluctuated downwards (30–60 min), and decreased sharply when the actual commuting time exceeded 70 min. It follows that, when the actual commuting time was within the range of ideal commuting times, the perceived satisfaction of the short-duration-tolerance commuters remained largely unchanged, while

the perceived satisfaction of the long-duration-tolerance commuters increased; when the actual commuting time exceeded the tolerance threshold, the perceived satisfaction of three clustered groups was severely reduced; as expected, extreme commuting led to very negative perceived experiences.

Finally, the study also concluded that variables such as the commuting mode, job– housing relationship, and individual characteristics provided significant explanations for differences in commuting satisfaction in the clustered group. The commuters who used active commuting modes, those with balanced job–housing relationships, and commuters with lower levels of education were the most likely to be in the short-duration-tolerance commuting group. Conversely, the commuters who used public transportation, those with severe job–housing distance, and highly educated commuters were the most likely to be long-duration-tolerant commuters.

The policy implications obtained from the conclusions are as follows. First, the results of the hypothetical situational experiment show that since the respondents reported a positive response to the 0 min of hypothetical commuting time, we can try to implement some moderate telecommuting systems to test whether remote working helps to improve commuting satisfaction and subjective well-being. Additionally, a dynamic evaluation of the effect of implementing remote work could be conducted to identify whether people's commuting demands are different in the short, medium, long term, as well as during the epidemic (a special period), which will help to provide policy advice for policymakers to formulate mechanisms for telecommuting.

Second, it is important to plan a commuting circle based on the time-threshold effect of nonlinear changes in commuting satisfaction, to try to use the commuting time tolerance threshold as a constraint boundary, and to move as close as possible to ideal commuting times.

Lastly, according to the group differences in the commuting-time satisfaction and its influencing factors, different strategies should be adopted for different commuting groups when formulating policies for urban transport optimization. For example, the commuting quality of the short-duration -tolerant commuters, most of whom live and work in the same area and use active commuting, may be further improved by enhancing the soft and hard facilities of the active travel system; for the long-duration -tolerant commuters, most of whom have a significant distance between their work and housing and use public transport to commute, the operation and service level of public transport could be improved in the short term to reduce their commuting pressure, and the allocation of resources could be optimized in the long term to achieve a relative balance between work and housing.

The limitation of this study is that the representativeness of the sample was not strong. For example, the proportion of commuters over 40 years old was slightly lower and the proportion of commuters with low education was also relatively small, which is related to the selection of commercial complexes as the survey locations. The method of approaching the respondents needs to be improved to ensure that the sample better captures the overall characteristics of commuters. The two surveys (May 2020 and January 2021) were both conducted in the urban area of Kunming, which inevitably resulted in a respondent answering the same questionnaire twice. Trying to avoid such survey issues could help to improve the data quality. Additionally, there are differences in transportation policies and commuting systems in different cities, which means that the perceived satisfaction of commuters in different cities may also be different. It would be interesting to explore the heterogeneity of commuting satisfaction in cities of different scales and its influencing factors.

Furthermore, this study only focused on the threshold effect of commuting satisfaction for different groups in terms of commuting time. In the future, the complex relationship between the commuting satisfaction and commuting times of different groups, including different commuting modes (especially multimodal commuting), different job–housing distances, and different built environments, could be explored. These studies will have potential value for the formulation of differentiated and humanistic urban transport policies.

It should also be added that future research could explore the correlation between commuting satisfaction and perceived utility; if the correlation is strong, the nonlinear characteristics of commuting satisfaction can be drawn upon to modify the perceived utility function, which may facilitate research on commuting-mode-choice behavior or the mutual transfer of commuting modes.

**Author Contributions:** Conceptualization, M.H. (Mingwei He), J.Y. and M.H. (Min He); methodology, J.Y., M.H. (Mingwei He); formal analysis, J.Y., M.H. (Mingwei He) and M.H. (Min He); investigation, J.Y.; data curation, J.Y., M.H. (Mingwei He); writing—original draft preparation, J.Y., M.H. (Mingwei He); writing—review and editing, M.H. (Mingwei He), J.Y. and M.H. (Min He); supervision, M.H. (Min He), M.H. (Mingwei He); funding acquisition, M.H. (Mingwei He). All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China (No. 71861017); Yunnan Fundamental Research Projects (Grant no.202001AT070030).

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of the Kunming University of Science and Technology (1 January 2020).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data used to support the findings of this study are available from the authors upon request.

**Conflicts of Interest:** The authors declare that they have no competing interests.

#### **References**

