Modeling and analyzing of family intention for the customized student routes: A case study in China

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Modeling and analyzing of family intention for the customized student routes: A case study in China Jingjing Hao a,b,c , Ling Zhang a,b,c , Xiaofeng Ji b,c , Jinjun Tang d ,

∗

a

School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China Yunnan Integrated Transport Development and Regional Logistics Management Think Tank, Kunming University of Science and Technology, Kunming 650504, China c Yunnan Engineering Research Center of Modern Logistics, Kunming University of Science and Technology, Kunming 650504, China d Smart Transport Key Laboratory of Hunan Province, School of Traffic and Transportation Engineering, Central South University, Changsha, 410075, China b

article

info

Article history: Received 1 April 2019 Received in revised form 12 September 2019 Available online xxxx Keywords: Children’s school travel Structural equation modeling Family intention Policy making Customized student route China

a b s t r a c t In China, the emergence of new school travel mode using customized student routes has effectively alleviated the burden of most family considering children transportation. However, since such mode is still in the exploratory stage, it is urgent to analyze family intention for such school travel services. In this study, a whole theoretical framework considering travel constraint and ‘‘service quality-satisfaction-behavior’’ is constructed to explore the choice intention of parents. The structural equation modeling is firstly applied to analyze households demand towards customized student routes from four dimensions: travel constraints, low satisfaction, service quality and household influences. The family intention features were then collected from 545 household survey questionnaires in Zhaotong City, Yunnan Province, China. Finally, the parameters in the model are calibrated, and the validation of model application is further tested based on survey results. Interestingly, it is found that Chinese households tend to show more concerns about basic service functions of customized student routes than personalized services such as online appointment and payment functions. In addition, it is discovered that low satisfaction with current school travel modes and travel constraints exert great impacts on households’ choice intention for customized student routes while characteristics of household, such as household income expressed a moderating effect on the relationship between low satisfaction and behavior intention and the proportion of non-workers in household and the presence of elderly people in household expressed a moderating effect on the relationship between travel constraints and behavior intention. Those conclusions will provide theoretical basis for the policy makers in making relevant policies about school bus management. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Children’s school travel is a tricky issue that has been attracted widespread concerns from different people including parents, government policy makers and academic researchers. At present, China has not formed a perfect legal and policy system for child/children travel, and there are many deficiencies in the safety management of school buses. Although, ∗ Corresponding author. E-mail address: [email protected] (J. Tang). https://doi.org/10.1016/j.physa.2019.123399 0378-4371/© 2019 Elsevier B.V. All rights reserved.

Please cite this article as: J. Hao, L. Zhang, X. Ji et al., Modeling and analyzing of family intention for the customized student routes: A case study in China, Physica A (2019) 123399, https://doi.org/10.1016/j.physa.2019.123399.

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the regulations on the safety of school buses have been implemented for years, the effect of management still needs further improvement. Particularly, in the current period, as the expansion and development of the customized student routes gradually enhance in China, the demand intention of families are necessary to be explored because it will provide theoretical basis for the policy makers in making relevant policies about school bus management. Furthermore, the government could simultaneously manage the school bus drivers, companies and school to build a safe school commuting environment. As the main decision-maker of their child/children’s school travel modes, parents generally consider child/children’s safety as the most important factor [1,2]. Therefore, the travel mode of picking up or sending child by parents becomes a common choice accepted by most of families. Generally, in this mode, parents pay more attention to convenience and safety [3]. They ignore child/children’s self-development since this school commuting mode directly weakens children’s ability to handle things independently and further affects their performance at school [4]. Furthermore, this mode also would reduce chances for students to do physical exercises, which may lead to many health problems such as obesity [5,6]. Focusing on the problems results from the commuting mode, scholars attempt to explore the reason why many parents choose to pick up and send children by themselves. In the research of McDonald and Aalborg [3], they found the reason mainly come from convenience and safety factors, and more than 50% parents do not allow their children to walk to school without parental supervision. Stewart et al. [7] found that the main factor affecting parents’ choice is the distance between home and school while safety is their secondary concern. Moreover, many studies stated that most families did not allow their child/children to go to school on foot or by bike independently because they feel anxious about the safety of those travel modes [8,9]. In China, the parents also face the similar issues on the choice of children travel modes. The study conducted by Wang [10] provided evidence that more than 90% of Chinese parents who were concerned about travel safety preferred private car for escorting their children. Due to complicated traffic environment and unique family structure, this problem becomes more emerging and expresses new characteristics. (1) the picking-up and sending periods generally coincide with peak hours of urban traffic, which would generate additional pressure to the urban transportation system; (2) the commuting time of parents is not synchronous with the picking up or sending time of children travels, which would seriously affect the normal working habit of parents; (3) individual motorized travel modes has a negative impact on the urban environment. In order to alleviate the above contradictions, a series measures are conducted including accompanying with the elderly to conveniently pick up or send children, renting or purchasing houses around the school, and using family carpooling mode to serve the children living in the same community and commuting the same school. Currently, with the popularization and promotion of Internet, Navigation and Communication technologies, a new customized travel mode for student has appeared in most cities of China, which aims to provide children with ‘‘one-stop’’ and ‘‘nanny-style’’ shuttle services. The customized student routes is involved by a professional third-party company to provide safe and convenient travel services for children: (1) customized student routes adopt online appointment and offline ride mode; (2) customized student routes escort and drop children at the location selected by the parents (usually inside the community or at the entrance of the community); (3) customized student routes implement the ‘‘point-topoint’’ transportation service mode, without setting up the station in the middle, directly sending the students to their school; (4) customized student routes are equipped with a GPS supervision and a device of reminding boarding and disembarking, with the help of offering parents to monitor their children’s position in real time as well as to ensure their children’s safety; (5) customized student routes implement the system of ‘‘one person-one seat’’, equipped with special personnel to carry out safety education for children. The emergence of customized student routes will effectively alleviate the travel contradiction between parents and children, reduce the burden on parents, and also relieve urban congestion and decrease environmental pollution. Although possessing various advantages comparing with traditional modes, the current customized mode is still in the exploring and developing stage. Specifically, whether this mode will affect the choice intention of Chinese parents and how Chinese parents will accept this mode are both need to be further explored. At present, Chinese scholars pay more attention to the study of children’s travel characteristics and family pickup modes [11–13], and research focusing on household demand behaviors analysis of customized student routes is still rare. Previously, most of studies related to school travel behavior focused on the determinants of existing travel patterns, such as walking and cycling, parental escort, as well as a service of walking school bus [7,14,15]. Meanwhile, different theoretical or conceptual models (e.g., Theory of planned behavior by Ajzen [16]; Socio-cognitive theory by Bandura [17]) have been proposed to explain the decision of children’s travel modes. However, few studies have taken the intervention of customized mode of school travel modal shift into account, and there is no unified theory that is recognized by scholars and can be used to fully explain the decision-making process [18–20]. Fortunately, similar researches have been conducted in fields of leisure behavior and customer behavior. Particularly, travel constraints theory [21] and ‘‘service quality-satisfaction-behavior’’ theory framework [22,23] are typically employed for understanding individuals’ behavioral shift, and shared the same factors (e.g., individual, family, external influence and physical environment) that influencing behavior intention, which could be a useful conceptual framework referred in this study. It is worth noting that the moderating effect of household dimension variables like household income, household size for travel behavior intention is widely recognized although the effect is not examined in the field of school travel [24,25]. In essence, family intention of customized student routes belongs to the concept of behavior intention. As a part of behavior intention, the household dimension variables may also play important moderating effect in the decision process of Chinese family’s behavior of Please cite this article as: J. Hao, L. Zhang, X. Ji et al., Modeling and analyzing of family intention for the customized student routes: A case study in China, Physica A (2019) 123399, https://doi.org/10.1016/j.physa.2019.123399.

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picking up children to school. How to examined this complicated relationship, an effectivity model should be adopted. The structural equation modeling, discrete choice model and machine learning model are effective approaches [26,27]. Compared with these models, structural equation modeling is most widely used and has advantages in dealing with the influence mechanism of independent variables on dependent variables, identifying the internal relations between factors, and dealing with the causal relationship between variables. Therefore, a structural equation modeling is conducted in this study for testing the relations between antecedent variables and family intention behavior. Our research objective, therefore, is to model and analyze the Chinese family intention for customized student routes based on existing related theories, mainly focus on the role of household in this mode. We take a case study in Zhaotong City, Yunnan Province, China as an example to conduct the modeling and analyzing of family intention for the customized student routes. Zhaotong City is one of the key cities for the innovation of transportation in Yunnan Province. The government of the city has an urgent need for developing customized student routes to promote the innovation of transportation. Under the support of the government and related enterprises, we conducted a questionnaire survey of family intention for the customized student routes. Although Zhaotong is a developing city, the research framework proposed in this paper has a good applicability in the development of customized student routes. It can apply to other cities where has the demand for developing customized student routes if we update the model according to cities’ own characteristics. The main contributions of this study are twofold. First, an analysis framework combined theories travel constraint and ‘‘service quality-satisfaction-behavior’’ is proposed, and structural equation modeling is conducted for testing the relations between antecedent variables and family intention behavior, which is a more thoughtful methodology provided for a better understanding of school travel mode choice. Second, different from the majority of existing studies, our study focuses on the moderating role of families, in which, assuming the relations of ‘‘low satisfaction-behavior intention’’ and ‘‘travel constraints-behavior intention’’ significantly moderates by household dimension variables. The conclusion obtained in this study provides a chance to gain novel insights for the development and optimization of customized student routes modes, especially in the context of China. 2. Methodology 2.1. Model structure The family intention of customized student routes refers to the subjective probability or possibility of a behavior intention to choose customized student routes. Limited researches have focused on the determinants of choosing customized student routes to school. However, to the best of our knowledge, a good body of empirical and experimental studies in fields of travel and consumer marketing have verified relationships between antecedent variables and behavior intention (i.e., outcome variable). The relationships between various antecedent various that are associated with behavior intention are potentially very complex, including direct and indirect effect (e.g., moderating effect). In this paper, a combination framework containing travel constraints theory [21], ‘‘service quality-satisfaction-behavior’’ theory [22,23], was adopted for model building. According to relevant theory and previous findings, we therefore classified the antecedent variables into four dimensions: travel constraints, low satisfaction, service quality and household influences. Each variable and its role that plays in family intention are briefly summarized, and the research hypotheses are also proposed in the following sub-sections. 2.1.1. Travel constraints Travel constraints refer to variables that hinder or prohibit travel behavior, and cause changes in people’s travel preferences and travel modes, which can be divided into intrapersonal, interpersonal, and structural constraints [21]. Intrapersonal constraints refer to the impediments associated with individual psychological conditions. Interpersonal constraints refer to the characteristics of reference group (family, friends, working partners, etc.) in decision-making process. Structural constraints are external variables that restrict the individual’s behavior intention. Travel constraints have been introduced into travel behavior decision studies by many scholars as a common analytical framework [28,29]. As the main constraints considering in the decision-making of household in this study, the travel constraints can be summarized as three lower antecedent variables which are intra-household constraints, children’s travel constraints and school travel condition constraints. Intra-household constraints refer to internal resources that influence decision-making of household, which are mainly related to the attributes of parents. It belongs to the concept of intrapersonal constraints in travel constraints theory [30,31]. Previous literatures [3,4,17,31] found that interactions and constraints among family members have become significant factors influencing children’s school travel behavior. In particularly, factors like working state, job flexibility, working hours, transportation cost, all influence parental decision-making mechanism on how their children commute to school. Children’s travel constraints belong to the concept of interpersonal constraints in travel constraints theory. It refers to the personal characteristics of the students who influence family decision-making, including factors such as age, gender, physical health, personality, grade, etc. [32,33]. Existing literature has confirmed that parents are generally more inclined to escort younger girls or children who are less optimistic, and introverted [34,35]. Please cite this article as: J. Hao, L. Zhang, X. Ji et al., Modeling and analyzing of family intention for the customized student routes: A case study in China, Physica A (2019) 123399, https://doi.org/10.1016/j.physa.2019.123399.

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School travel condition constraints are related to the concept of structural constraints in travel constraint theory. It refers to the external variables that restrict children’s school travel, including distance and time. In the study of children’s school travel patterns in San Francisco, USA, McDonald and Aalborg [3] found that 75% of parents would choose to drive to escort children within 2 miles driving distance. Besides, some relevant researches have also confirmed that whether a family owns a car is also a factor affecting the conditions of school travel [35]. According to the aforementioned discussion, we can propose an assumption as follows: Hypothesis 1. Travel constraints negatively affect behavior intention. 2.1.2. Low satisfaction Low satisfaction is a psychological construct that describes an individual subjective sentiment about the use of a service or product. It is often considered as a key impetus of long-term relationships between suppliers and purchasers [22,36]. Jung et al. [20] revealed a driver would give up the original routes and choose new ones with low satisfaction to the original route. In addition, the more dissatisfied a person feel with the current supply service, the higher active he/she will be to find alternatives [20]. In this study, low satisfaction refers to the dissatisfied sentiment of parents to the current school travel (e.g., private car, bicycle, public transit, etc.). As such, the customized student route, as a more satisfactory school travel mode, would be preferred by parents for their children’s school commuting. Therefore, we assume that: Hypothesis 2. Low satisfaction positively affects behavior intention. 2.1.3. Service quality Service quality is defined as the customer’s overall judgment of the excellence and superiority of a service, which directly affects behavior intention [20,37,38]. Several scholars focused on the service quality of alternative school travel mode, such as Kearns et al. [39] found that, the main reason for the parents switch to a service of walking school bus is that it meets the parents’ need for the safety and the health of the children. Likewise, FHWA [40] found that safety and convenience are two important factors affecting families to switch to Safe Routes to School program. Moreover, they also found that parents are very concerned about the transportation routine design. Additionally, it is worth noting that in the field of travel behavior, the relationship between service quality and behavior intention has been tested in many empirical studies [41]. Generally, the higher the quality of travel mode service, the greater the decision-maker’s intention to choose the mode of travel. In this study, the service quality refers to the households’ overall judgment of customized student route service. Therefore, we assume that: Hypothesis 3. Customized student routes service quality positively affects behavior intention. Meanwhile, although the relationship between travel constraints and low satisfaction is rarely tested, many studies focus on the impact of travel constraints on individuals’ assessment or judgment in the travel decision-making process. For example, Kam et al. [29] pointed out that travel constraints can affect tourists’ willingness to revisit the destination. During the investigation, we also found that there is a phenomenon that families with large travel constraints generally have low satisfaction with travel modes. Therefore, we assume that: Hypothesis 4. Travel constraints positively affect low satisfaction. 2.1.4. Household influences Household influences involve its characteristics (e.g., household income, household size, etc.), which significantly influence children’s school travel mode. McDonald [34], Werts and Zwets [35] confirmed that household income is a determinant of school travel decision. They found that families with higher incomes have a greater chance to adopt travel mode with private vehicles rather than choosing walking or public transport to school. Evenson et al. [42] emphasized that the proportion of non-workers in household has a negative impact on the behavior of children’s walking or cycling to school, as families are more likely to send or pick up children from school, which is also consistent with the results in McDonald [31]. While no studies have ever examined or identified the moderating effect of household influences on relations of ‘‘travel constraints-behavior intention’’ and ‘‘low satisfaction-behavior intention’’, it has attracted considerable attention from service and leisure literature [24]. A moderator is a variable that influences the strength or the direction of a relationship between two variables. In this paper, household income, the proportion of non-workers, the presence of elderly people are defined as moderator variables. According to previous researches, we assume that: Hypothesis 5a. Household income significantly moderates the ‘‘travel constraints-behavior intention’’ relation. Please cite this article as: J. Hao, L. Zhang, X. Ji et al., Modeling and analyzing of family intention for the customized student routes: A case study in China, Physica A (2019) 123399, https://doi.org/10.1016/j.physa.2019.123399.

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Fig. 1. Structural equation modeling of Chinese family intention of customized student routes.

Hypothesis 5b. The proportion of non-workers in household significantly moderates the ‘‘travel constraints-behavior intention’’ relation. Hypothesis 5c. The presence of elderly people in household significantly moderates the ‘‘travel constraints-behavior intention’’ relation. Hypothesis 6a. Household income significantly moderates the ‘‘low satisfaction-behavior intention’’ relation. Hypothesis 6b. The proportion of non-workers in household significantly moderates the ‘‘low satisfaction-behavior intention’’ relation. Hypothesis 6c. The presence of elderly people in household significantly moderates the ‘‘low satisfaction-behavior intention’’ relation. Eventually, based on the theoretical framework of travel constraint and ‘‘service quality-satisfaction-behavior’’ and previous literature, the proposed conceptual model is shown in Fig. 1. 2.2. Model specification Structural equation modeling is effective approach to build, estimate and verify causal models, which consists of two components of measurement model and structural model. The measurement model component determines whether the latent variables can be measured effectively using the corresponding observed variables, and the structural model describes the casual relationships among latent variables. In this study, measurement model component mixes two second-order factor models and two first-order factor models; and structural model component not only contains the relationships between antecedent variables and outcome variable, but also mixes the influence path of moderator variables on outcome variable (as shown in Fig. 2). 2.2.1. The measurement model component Second-order factor model [43] is a special type of measurement model (shown in Fig. 3(a) and (b)), it represents hypothesis about hierarchical relations between variables in measurement model through the specification of secondorder factors with presumed direct causal effects on first-order factor. Kline [43] stated that all first-order factors are endogenous but the second-order can be defined as exogenous. Accordingly, the measurement model in our research can be described as follows: Y MM = ΛY MM ηMM + ε MM

(1)

Please cite this article as: J. Hao, L. Zhang, X. Ji et al., Modeling and analyzing of family intention for the customized student routes: A case study in China, Physica A (2019) 123399, https://doi.org/10.1016/j.physa.2019.123399.

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Fig. 2. Relationship among different models.

ηMM = Γη MM ξ MM + ζ MM

(2)

where, Y MM is the observed variables; ΛY MM = aη1 , aη2 , aη3 , . . . , aηy

[

]T

is the factor loadings matrix of observed variables

]T

on the first-order factors; η represents the first-order factors; Γη = rξ 1 , rξ 2 , rξ 3 , . . . , rξ η is the factor loadings matrix of the first-order factors on the second-order factors; ξ MM represents the second-order factors; ζ MM is the disturbances of the first-order factors (unique variance that is not shared with the common second-order factor); and ε MM represents the residuals. In this study, the first-order factors contain six lower antecedent variables (intra-household constraints, children’s travel constraints, school travel condition constraints, basic service quality, personalized service quality and transportation routines service quality), one antecedent variable (low satisfaction) and one outcome variable (behavior intention), while the second-order factors contain two antecedent variables (travel constraints and service quality). MM

MM

[

2.2.2. The structural model component For structural model component (shown in Fig. 4(a)), the relationships between antecedent variables and outcome variable can be expressed as follows:

ηBI SM = BBI SM ηLSA SM + ΓBI SM ξBI SM + ζBI SM ηLSA

SM

= ΓLSA

SM

ξLSA

SM

+ ζLSA

SM

(3) (4)

where ηBI SM and ηLSA SM are the endogenous latent variables, represent behavior intention and low satisfaction, respectively; ξBI SM is the exogenous latent variables of ηBI SM , in this study, it includes two antecedent variables (travel constraints and service quality); ξLSA SM is the exogenous latent variables of ηLSA SM , in this study, it includes one antecedent variables (travel constraints); BBI SM = [β11 , β12 , β13 , . . . , β1i ]T is the path coefficients matrix of the endogenous latent variables [ ]T ηLSA SM on behavior intention; ΓBI SM = ω11 , ω12 , ω13 , . . . , ω1j is the path coefficients matrix of the exogenous latent variables ξBI SM on behavior intention; ΓLSA SM = [ω21 , ω22 , ω23 , . . . , ω2l ]T is the path coefficients matrix of the exogenous latent variables ξLSA SM on low satisfaction; ζBI SM and ζLSA SM are the disturbances. As mentioned above, moderating effect is another component of structural model (shown in Fig. 4(b)). When the direction and size of the relationship between two variables depend on the third variable, that is, there is a moderating effect. The third variable is the moderator variable. In our model, three household dimension variables are defined as moderator variables, and they are continuous variables. Meanwhile, the antecedent variables and outcome variable are also continuous variables. Chin et al. [44] recommended that, a hierarchical regression modeling that with interaction terms (i.e., product terms) is a commonly used method for testing moderating effect, when all variables are continuous Please cite this article as: J. Hao, L. Zhang, X. Ji et al., Modeling and analyzing of family intention for the customized student routes: A case study in China, Physica A (2019) 123399, https://doi.org/10.1016/j.physa.2019.123399.

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Fig. 3. The measurement model component (a) second-order factor model 1, (b) second-order factor model 2, (c) first-order factor model 1, (d) first-order factor model 2.

Fig. 4. The structural model component (a) the influence path of antecedent variables on outcome variable, (b) the influence path of moderator variables on outcome variable.

in the model. Accordingly, the relationships between moderator variables and other variables in structural model can be expressed as follows:

ηBI H5 = ω1j TC ξ TC + AZ + M ξ TC Z + ΛH5

(5)

ηBI

(6)

H6

= β1i

η

LSA LSA

+ AZ + H η

LSA

Z +Λ

H6

where ηBI H5 and ηBI H6 both are the outcome variable (behavior intention); ξ TC and ηLSA are two antecedent variables (travel constraints variable and low satisfaction variable, respectively); ω1j TC is the path coefficients of travel constraints on behavior intention; β1i LSA is the path coefficients of low satisfaction on behavior intention; Z = [z1 , z2 , z3 ] is the matrix of moderator variables, representing the household income, the proportion of non-workers in household and the presence of elderly people in household variables, respectively; A = [a1 , a2 , a3 ]T is the path coefficients matrix of Z on behavior intention; M = [λ1 , λ2 , λ3 ]T is the regression coefficients matrix of interaction terms ξ TC Z ; H = [λ4 , λ5 , λ6 ]T is the regression coefficients matrix of interaction terms ηLSA Z ; ΛH5 and ΛH6 represent the residuals matrix. 2.3. Model solution Structural equation modeling is established based on known knowledge. It was empirically confirmed that use of known knowledge gave more accurate estimates and less computational times in most cases especially for models with Please cite this article as: J. Hao, L. Zhang, X. Ji et al., Modeling and analyzing of family intention for the customized student routes: A case study in China, Physica A (2019) 123399, https://doi.org/10.1016/j.physa.2019.123399.

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Table 1 Source of initial scale for questionnaire design. Variables

Labels Lower antecedent variables Travel constraints

Antecedent Low variables Satisfaction Service quality Outcome variable

Behavior intention

Labels Design basis for measurement items

TC

Intra-household constraints Children’s travel constraints School travel condition constraints

HTC CTC STC

Contextualized from Crawford et al. [21] and Kam et al. [29]

LSA

–

–

Contextualized from Jung et al. [20]

SQ

Basic service quality BSQ Personalized service quality PSQ Transportation routines service quality TSQ

Contextualized from Scott et al. [41] and Jung et al. [20]

BI

–

Contextualized from Ajzen [16], Perugini and Bagozzi [49]

–

complex structure [43,45,46]. Considering the model structure of this study, we tested the proposed model using a twostep approach in which the measurement model was examined first, followed by the structural model [47]. The two-step approach was conducted by Mplus 7.0 software, and the right solution was obtained in the following steps. (1) Using a confirmatory factor analysis (CFA) to assess the accuracy of measurement model. In this process, an iterative testing and adjustment for measurement model is conducted by using Mplus 7.0 based on the result of CFA, which aims to form an acceptable measurement model to represent the antecedent variables and outcome variable for the proposed model. (2) Using the maximum likelihood method to calibrate the structural model and optimize the initial model by using Mplus 7.0. As a result, the model parameter estimates and the model fit indexes are calculated, which can be used for model optimization. On this basis, we obtained the optimal model by repeated experiments and corrections. (3) Identifying empirical support for the hypotheses in Fig. 1 based on the parameter estimates from the final model. The hypotheses of H1, H2, H3 and H4 in Fig. 1 are tested by using Eqs. (3)–(4), and whether they are supported or not determined by the significance of the path coefficients matrix (i.e., BBI SM , ΓBI SM and ΓLSA SM ). The hypotheses of H5a–H6c in Fig. 1 are tested by using Eq. (5)–(6), and whether they are supported or not determined by the significance of the regression coefficients matrix of interaction terms (i.e., H and M ). 3. Questionnaire design, data collection and processing 3.1. Questionnaire design The initial scale of questionnaire in this study, mainly derived from the maturity scale in existing literature, were monitored and revised under the Chinese situation (shown in Table 1). It contains the measurement items for all factors (variables) listed in Fig. 1. Subsequently, the questionnaire was pre-tested using a random sample of 69 households in Zhaotong City, Yunnan Province, China. In the end, 52 responses were obtained. Following the work in Sun [48], the measurement items that with bad or low ability for measuring the variables of each scale are identified based on corrected item-total correlation (CITC) and Cronbach α , using a cut-off value of 0.3 for CITC or by deleting items with Cronbach α improved largest. Eventually, a purified scale was obtained, and a questionnaire is further formed to maximize the quality of sample data collection. All measurement items are measured on a five-point Likert-type scale. Meanwhile, the household influences dimension data were obtained directly through the questions in the questionnaire, and the items involved are as follows:‘‘What is the annual income of your family?’’, ‘‘How many people are there in your family?’’, ‘‘What is the number of non-workers in your family?’’, ‘‘Is there any elderly living in your family?. The proportion of non-workers in the households used in the study was calculated by the ratio of the number of non-workers in the household to the total number of households. 3.2. Data collection and analysis Data collection for this paper took place in Zhaoyang District in the Zhaotong City, Yunnan Province, China, from August 2 to 8, 2017. Data was collected using a questionnaire survey from households who have escorted child to school. Zhaoyang District, as the economic center of Zhaotong City, are facing a relatively high demand for customized school route to relieve the burden of families escorting children to school. 195 residential areas are located in Zhaoyang District. Considering the cost-effective of the survey and the available information of these 195 residential areas, this study employed two-stage sampling technique to conducted the questionnaire survey. The first stage involved the selection of subgroups from all the 195 residential areas by using stratified random sampling method. The key of this stage was to determine the criteria of stratification. As this research aims to estimate the role of household on the choice of the customized school route, it is important to have a representative sample with more information about households. Existing literature [14] has confirmed that the household income, household size, Please cite this article as: J. Hao, L. Zhang, X. Ji et al., Modeling and analyzing of family intention for the customized student routes: A case study in China, Physica A (2019) 123399, https://doi.org/10.1016/j.physa.2019.123399.

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Table 2 Statistical features of samples. Household characteristics

Frequency

Percentage

Household annual income Below 20,000 RMB 20,000–40,000RMB 40,000–60,000RMB 60,000–80,000RMB Over 80,000RMB

80 135 161 123 46

14.68% 24.77% 29.54% 22.57% 8.44%

20 29 131 221 144

3.67% 5.32% 24.04% 40.55% 26.42%

234 311

42.94% 57.06%

248 214 55 28

45.50% 39.27% 10.09% 5.14%

Household size 1 2 3 4 5 and above

Frequency

Percentage

Below 0.5 km 0.5–1 km 1–3 km 3–5 km Over 5 km

106 120 232 58 29

19.45% 22.02% 42.57% 10.64% 5.32%

152 116 69 55 131

27.89% 21.28% 12.66% 10.09% 24.04%

22

4.04%

School travel mode

Household type Household with elderly people Household without elderly people

Children’s school travel characteristics School trip distance

Escorting Escorting Escorting Escorting Escorting Others

by by by by by

car e-bicycle bus bicycle walk

Car ownership 0 1 2 3 and above

etc. affect the children’s school travel mode. However, limited data of the households are available. Only the housing price of these residential areas was obtained from the website of China real estate transaction, which could be approximately reflected the kinds of household. Thus, three residential areas were selected using stratified random sampling method from all of the 195 residential areas based on the criteria of housing price. In second stage, 200 households were picked in each of the three residential areas using typical sampling method. Our trained investigators approached potential respondents in the main entrance/exit, squares and shops situated within each selected residential area. Respondents were selected based on the question whether any of the children had escorted in recent years. Only if the answer was affirmative, the respondent could proceed with the questionnaire. Each participant corresponds to one household. The survey questionnaire had three sections. The first section concerns about household characteristics. The second section is applied to capture children’s school travel characteristics. The third section refers to the measurement scale of travel constraints, low satisfaction, service quality and behavioral intention. In the end, 600 responses were completed. After removing the questionnaires with missing data, 545 valid questionnaires were finally obtained. A summary of the characteristics for the sample is shown in Table 2. We used SPSS 18.0 to do descriptive statistical analysis for the sample data, then the value of skewness and kurtosis of each observed variable was output. It can be seen that the values are meet the range of 2–7. Therefore, the strategy of model estimation in this study is maximum likelihood (ML) estimation [50,51]. 4. Result of the measurement model component As the second-order factor model is included in measurement model, a confirmatory factor analysis is conducted for the first-order construct model, followed by the second-order construct model. This aims to assess the predictive ability of the observed variables on the respective latent variables, and further evaluate the accuracy of measurement model. 4.1. First-order factor model As suggested by Hair et al. [52], reliability indicators, convergent validity and discriminant validity are used to evaluate the measurement model. Reliability can be evaluated by using the Cronbach’s α and composite reliability (CR). As shown in Table 3, the Cronbach’s α value of each variable (include one outcome variable, one antecedent variable and six lower antecedent variables) exceeds the critical value of 0.70, and the CR value is in the range of 0.79∼0.89, which also exceeds the critical value of 0.70. This indicates that the measurement items have good internal consistency reliability, and the scale has good reliability. Convergent validity can be examined by using the standardized factor loadings (aηy ) and average variance extracted (AVE). The aηy values of all observed variables exceed the required threshold of 0.40, and are statistically significant at p < 0.001 (shown in Table 3). Moreover, the AVE values of the variables (include one outcome variable, one antecedent variable and six lower antecedent variables) ranges from 0.50 to 0.69, all of which are greater than the critical value of 0.50 (shown in Table 3). These findings confirm that the convergent validity exists in the current study. Please cite this article as: J. Hao, L. Zhang, X. Ji et al., Modeling and analyzing of family intention for the customized student routes: A case study in China, Physica A (2019) 123399, https://doi.org/10.1016/j.physa.2019.123399.

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Table 3 Reliability and validity analysis results. First-order factors

Observed variable

Labels

Standardized factor loadings aηy

Cronbach’s α

AVE

CR

BIb

Often Preferred Recommend to others

BI1 BI2 BI3

0.81 0.84 0.79

0.894

0.66

0.85

BSQd

Safety Economy Punctuality Comfort Waiting time Station layout

BSQ1 BSQ2 BSQ3 BSQ4 BSQ5 BSQ6

0.94 0.68 0.72 0.74 0.62 0.84

0.828

0.58

0.89

PSQd

Online appointment Online payment Real-time monitoring Special care

PSQ1 PSQ2 PSQ3 PSQ4

0.66 0.9 0.79 0.79

0.834

0.62

0.87

TSQd

Transportation routines Fixed sites Directly to school

TSQ1 TSQ2 TSQ3

0.89 0.82 0.77

0.815

0.66

0.85

HTCd

Work flexibility constraint Escorting route constraint Escorting time schedule constraint Escorting monetary cost Escorting time cost

HTC1 HTC2 HTC3 HTC4 HTC5

0.8 0.57 0.96 0.52 0.89

0.879

0.59

0.87

CTCd

Age Gender Physical health Character

CTC1 CTC2 CTC3 CTC4

0.78 0.51 0.69 0.82

0.825

0.50

0.80

STCd

School trip distance School travel time Car ownership

STC1 STC2 STC3

0.89 0.82 0.78

0.842

0.69

0.87

Safetya Conveniencea Punctualitya Comforta Economya

LSA1 LSA2 LSA3 LSA4 LSA5

0.65 0.90 0.83 0.42 0.69

0.888

0.51

0.83

c

LSA

Note. All standardized factor loadings aηy are significant at p < 0.001. These items are reversely coded. b The variable is outcome variable. c The variable is antecedent variable. d These variables are lower antecedent variables. a

Table 4 Analysis results of discriminant validity. BI BSQ PSQ TSQ HTC CTC STC LSA

BI

BSQ

PSQ

TSQ

HTC

CTC

STC

LSA

0.814 0.20 0.43 0.21 0.04 0.34 0.04 −0.28

0.764 −0.67 0.75*** 0.29* 0.53** 0.48*** −0.25*

0.790 −0.67 0.12* 0.34* 0.30* −0.15*

0.828 0.25* 0.25* 0.74*** −0.52***

0.768 −0.13* 0.02 −0.56***

0.710 −0.28 −0.05

0.831 0.11

0.717

Note. Below the diagonal line is the correlation coefficient between the variables, and on the diagonal line is the square root of the variable AVE value. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.

Discriminant validity can be examined by assessing the correlations among variables. The results (shown in Table 4) illustrate that the correlation values corresponding to the respective construct reach the required standard, indicating discriminate validity has received adequate support [52]. 4.2. Secord-order factor model As mentioned in 2.2.1, the second-order factors refer to the hypothesis about the direct causal effects of itself on first-order factor. It should be noted that it has no direct indicators (i.e., observed variables), but is predicted through Please cite this article as: J. Hao, L. Zhang, X. Ji et al., Modeling and analyzing of family intention for the customized student routes: A case study in China, Physica A (2019) 123399, https://doi.org/10.1016/j.physa.2019.123399.

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Table 5 The results of secord-order factor model. Second-order factors

First-order factors

Standardized factor loadings rξ η

TCa

HTCb ITCb STCb

0.72∗∗∗ 0.53∗∗∗ 0.55∗∗∗

SQa

BSQb PSQb TSQb

0.75∗∗ 0.44∗∗ 0.87∗∗

Note. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. These variables are antecedent variables. b These variables are lower antecedent variables. a

Fig. 5. Structural model estimation results.

first-order factor. Only the value of standardized factor loadings rξ η can be obtained for the accuracy of second-order factor model using the approach of confirmatory factor analysis. The results showed that the standardized factor loadings rξ η of all the second-order factors range from 0.44 to 0.87, and are statistically significant (as shown in Table 5). This indicates that the second-order construct are well explained by the first-order construct. Consequently, it can be seen that the sample data in this paper have good reliability and validity, and can be effectively applied to the modeling and analysis of family intention for customized student routes. 5. Result of the structural model component The structural model was then solved using Mplus 7.0 (regardless of the moderation of the household influences dimension). The results (χ 2 /df = 2.237, p < 0.000, RMSEA = 0.048, CFI = 0.920, TLI = 0.925, SRMR = 0.058) show that, the first five indicators meet the required threshold (referenced literature Hair et al. [52]: χ 2 /df < 3, RMSEA < 0.05, CFI > 0.90, TLI > 0.90, SRMR < 0.05) except for SRMR. However, it is worth noting that Hu and Bentler [50] pointed out that the model fitting is acceptable when the value of SRMR is less than 0.08. Accordingly, the results indicate that the model express good fitting performances. In addition, the results also show that the antecedent variables (TC, LSA, and SQ) explains 38.1% of the total variance in outcome variable (BI), and the antecedent variable travel constraints explains 37.5% of the total variance in the other antecedent variable (LSA) which both exceed the acceptable recommended value of 0.36. This indicates that the model has an acceptable interpretability for the sample data. 5.1. Hypothesis testing The hypothesis relationship between the three antecedent variables TC, LSA, SQ, and outcome variable BI is tested using Eqs. (3)–(4). The path coefficients results of the specific path (H1–H4) are shown in Fig. 5. The results indicate that all hypothesis are supported. It can be seen that SQ is the most contributing factor affecting parents’ choice of customized student routes among variables SQ, TC and LSA. Its path coefficient ω12 is 0.461, which is at the highest level. Therefore, parents will pay more attention to the quality of their services when choosing customized student routes, and pay less attention to their own travel constraints and current travel low satisfaction. On the contrary, for the commuters or travelers, in their makingdecision of travel mode choice, they will consider their own travel constraints (such as time restriction and cost restriction) and current travel mode low satisfaction. Yang et al. [53] found that, travel time constraint is the determinant that influences the choice of urban residents’ travel modes during the morning and evening peak periods. Please cite this article as: J. Hao, L. Zhang, X. Ji et al., Modeling and analyzing of family intention for the customized student routes: A case study in China, Physica A (2019) 123399, https://doi.org/10.1016/j.physa.2019.123399.

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J. Hao, L. Zhang, X. Ji et al. / Physica A xxx (xxxx) xxx Table 6 Moderating effect of household influences on ‘‘TC-BI’’ and ‘‘LSA-BI’’. Regression steps and variables

∆F and significance

∆R2

a value and significance

Step one

24.926***

0.191

–

–

HI1→BI HI2→BI HI3→BI

– – –

– – –

0.127*** −0.479*** −0.501***

6.678 −4.659 −5.861

Regression steps and variables

∆F and significance

∆R2

λ value and significance

t-value

Step two

4.069***

0.202

–

–

TC × HI1→BI TC × HI2→BI TC × HI3→BI LSA × HI1→BI LSA × HI2→BI LSA × HI3→BI

– – – – – –

– – – – – –

0.169ns −0.326** −0.379*** 0.235** 0.271ns 0.284ns

1.317 −2.081 −5.255 2.255 1.087 0.965

t-value

Note. **p < 0.01, ***p < 0.001, ns is not significant.

From Table 5, the standardized factor loadings value rξ η of TSQ and BSQ on SQ are 0.87 and 0.75, respectively, both at a higher level. It can be understood that TSQ and BSQ are the key factors in SQ and to affect BI. It can be found that although the customized student line is a new type of school travel service mode under the internet economy, its personalized functions such as online payment and online appointment provide more convenient services for parents. However, customized student line service in China is still in its infancy and exploration stage, and parents have not yet formed a systematic understanding of this model. They still have strong demand for basic service functions such as TSQ1 (standardized factor loading aηy value of 0.89 as shown in Table 3), BSQ1 (standardized factor loading aηy value of 0.94 as shown in Table 3), and BSQ6 (standardized factor loading aηy value of 0.84 as shown in Table 3). This also confirms that the safety found in most studies is the primary consideration for parents to choose children’s school travel mode [7]. In addition, LSA has a positive effect on parents choosing customized student routes, with a path coefficient β11 of 0.416. Among them, LSA3 and LSA2 are the main factors affecting the low satisfaction of current travel modes, and then influencing parents’ choice intention of customized student routes. The standardized factor loadings aηy are 0.83 and 0.90 respectively (shown in Table 3). At present, most parents in China choose the pick-up pattern. As mentioned above, the commuting time schedule of parents and the school travel time schedule of children are frequently conflicting in China. So, the transport of children’s school travel has caused great inconvenience to parents’ daily activities. Moreover, the desynchronized time leads to the unpunctuality that parents escort children. Timely delivery of children to and from school is a challenge for most Chinese parents. This is also why Chinese parents have a strong demand for customized student routes. The above conclusions also provide several suggestions for the opening and promotion of customized student routes. That is, we should pay more attention to the convenience and punctuality of the route. Especially at the current situation of traffic congestion in most cities of China, the transportation routes for customized student routes should be designed more reasonably to avoid the problem of children being late for school due to road congestion. From Fig. 5, it can be found that the path coefficient ω11 of TC on BI is 0.262, which is at a low level. Notably, the path coefficient ω21 of TC on LSA is 0.396, which is higher. It can be understood that TC firstly affects LSA, and then further impact BI, as the direct relation between TC and BI is weak. 5.2. Moderating effect The moderating effect of household influences to the relations ‘‘TC-BI’’ and ‘‘LSA-BI’’ is examined using Eqs. (5)–(6). Specifically, we completed the estimation of a and λ based on the approach provided by Chin et al. [44]. First, on the basis of the results of hypothesis testing, we added the effect paths, three moderator variables of household influences (household income (HI1), the proportion of non-workers in household (HI2), and the presence of elderly people in household (HI3)) on behavior intention. Then, the effect path of each interaction term on the behavior intention was added. It should be mentioned that in order to avoid the occurrence of the collinearity problem, the dimensions of household influences (HI1, HI2, HI3), TC and LSA were centralized before the moderating effect was verified, and the interaction term was constructed. The results obtained are shown in Table 6. It can be observed from Table 6 that HI1 has no moderating effect on the relationship between TC and BI, but has a positive moderating effect on the relationship between LSA and BI, that is, H5a is not supported while H6a is supported. In the meantime, HI2 and HI3 have a significant negative moderating effect on the relationship between TC and BI, but have no moderating effect on the relationship between LSA and BI. That is, H5b and H5c are supported, while H6b and H6c are not supported. In order to clearly reveal the moderating effect of HI1 on the relationship between LSA on BI, we divide the sample group into two groups based on household income: high-income family group and low-income family group. The effects of Please cite this article as: J. Hao, L. Zhang, X. Ji et al., Modeling and analyzing of family intention for the customized student routes: A case study in China, Physica A (2019) 123399, https://doi.org/10.1016/j.physa.2019.123399.

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Fig. 6. Moderating effect analysis under travel constraints and low satisfaction.

LSA on BI in high-income families and low-income families are calculated using the regression equation, and the results are shown in Fig. 6(a). Similarly, we separately divided the sample into high-non-worker ratio families and low-non-worker ratio families based on the presence of non-workers in household. Based on the presence of elderly people in household, the sample is divided into household with elderly people and household without elderly people to more clearly reveal the moderating effects of HI2 and HI3 on TC on BI. The results are shown in Fig. 6(b) and (c), respectively. It shows that: In low-income families, households with different low satisfactions have less difference in the preference for customized student routes; while in high-income families, this difference becomes greater. In other words, with the increase of household income, the effect of LSA on BI is correspondingly enhanced. This also reflects that, with the increase of household income, families’ low satisfaction with the current school travel mode is also increased. Customized student routes are a kind of personalized travel service model, and their higher prices are often more easily accepted by highincome families. Therefore, customized student routes would mainly be designed to high-income families during the market incubation period in order to achieve good promotion results. In the high-non-worker ratio families, households with different travel constraints have less difference in the preference for customized student routes; while low-non-worker ratio families, this difference is greater. In the two groups, the household with elderly people and the household without elderly people, also get the same conclusion as the proportion of non-workers in the household. That is, as the proportion of non-workers in the household increases and there are elderly people living together, the impact of TC on BI is correspondingly reduced. The result also reflects the significant role that non-workers and the elderly people play in children’s school travel. Besides, most families with high non-workers ratio and those with elderly people do not select customized student routes, based on the consideration for saving travel costs. It also provides suggestion to the market orientation of customized student routes: more attentions should be paid to the families with no elderly people and busy parents. 6. Conclusions and discussion The problem of children’s school travel has always been a hot issue. Chinese parents generally have strong demand for customized student routes, which effectively alleviates the travel constraints and life pressures caused by picking up children. In this paper, an analysis framework combined theories travel constraint and ‘‘service quality-satisfactionbehavior’’ was proposed for a better understanding of Chinese family intention for customized student routes. A questionnaire survey was conducted in Yunnan, China and 545 valid sample including household characteristics, school travel characteristics and behavior intention information were obtained. Using the sample data, the applicability of our proposed model was tested by using a two-step approach of structural equation modeling. Meanwhile, the relationships between antecedent variables (i.e., TC, LSA, SQ) and outcome variable (i.e., BI) were estimated, and the moderating effects of three household dimension variables on the relations of ‘‘LSA-BI’’ and ‘‘TC-BI’’ were tested as well. The result shows that Chinese parents are more concerned about their basic service functions for customized student routes, and they have lower demand intention for personalized travel services such as online appointments and online payments. Also, the LSA of current school travel modes directly affects the Chinese household choice intention of customized student routes, the more dissatisfied with current travel modes, the stronger the choice intention of customized student routes. However, TC has a weak impact on choice intention of customized student routes. It indirectly affects choice intention of customized student routes, by affecting the LSA of parents with current travel modes. Further, household influences have a moderating effect on the relationship between LSA, TC and BI. HI1 has a positive impact on the relationship between LSA and BI. The HI2 and HI3 have negative impacts on choice intention. It indicates that the customized student route is a high-investment project, which makes it difficult for enterprises to gain operating income in the early stage of operation. At the same time, child/children’s school travel is about people’s livelihood and the customized student routes can effectively alleviate the traffic congestion coursed by driving child to school by themselves. In China, in some cities like Beijing, Zhejiang and Jiangsu, etc., many efforts have been devoted into the policy research and operating pattern innovation related to the improvement of school travel model for children [12,13,54,55]. However, Please cite this article as: J. Hao, L. Zhang, X. Ji et al., Modeling and analyzing of family intention for the customized student routes: A case study in China, Physica A (2019) 123399, https://doi.org/10.1016/j.physa.2019.123399.

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children’s school travel is still a tricky issue that has been concerned widely. The customized student route, as an emerging school travel mode, would be an effective alternative mode. In this paper, we proposed a more thoughtful methodology for better understanding the China families’ choice behavior intention to customized student route, which would contribute to help create a better travel environment for children’s school commuting. Although there are many differences of economic and social setting among different cities, such as the level of economic development, urban form, admissions policy and the culture context, etc. But the customized student route is in infancy in China, factors that affect families’ choice behavior intention to customized student route is similar. Our findings can provide a good reference for other cities in China on understanding what and how affects families’ choice behavior intention to customized student route. And it can help boost its popularity, and decrease the share of household escort through the implementation of relevant policies and manage measures, thus creating a better travel environment for children’s school commuting. Based on our analytical results, the following implications are emphasized. Implication 1: Currently, customized travel mode is widely popular in China, which involves daily commuting mode such as Didi and share-bike (e.g., Mobile Bike and Hello Bike). The advantage of these travel modes is personalization and convenience. Although the personalized and convenient services for customized student route is needed as well, our analytical results provided evidence that the basic service quality such as safety is still the primacy concerns for families. Thus, governments or transportation administration should pay more attention to basic functions optimization of the customized student route, such as transportation routes design, stops planning, transportation services upgrading. Some measures to enhance parents’ awareness and acceptance and thus effectively cultivating the market also should be conducted. Implication 2: Our analytical results provided evidence that household influences have a moderating effect on the parents’ choice of customized student routes. For families with high household income, the lower the satisfaction with current school travel mode, the greater probability or possibility of parents are to choose customized student route. Meanwhile, as the proportion of non-workers in the household increases and there are elderly people living together, families’ travel constraints on children’s school travel is correspondingly reduced, which eventually results in the less probability of parents choosing customized student routes. Therefore, to increase the popularity of customized student routes and expand its market share, governments or transportation administrations should pay more attention on the families who have no elderly people and with busy parents while with high household income in the exploring stage. In the above process, the most obstacle is the high operating cost and safety management concerns like in school bus service [13,54]. To solve this issue effectively, the policy makers should make relevant subsidies and preferential policy. Particularly in the early stage, the government should give more financial support for those enterprise who engage in customized student routes, which can help to maintain its sustainable operation and further promote its marketoriented development. In terms of the safety management issue, it is necessary for government and related transportation administrators to make safety regulations of customized student routes like school bus service, and raise the threshold of operate license in enterprises’ strength of operating and capital. This study also has several limitations. For instance, the proportion of male and female parents in the household will also have an impact on the choice intention of the customized student routes. This paper had not analysis it effect on the BI. In the future study, we will add the proportion of male and female parents in the household to analysis the family intention of customized student routes. At the same time, the structural equation modeling also has its defect on explanation of causality. At our subsequent study, multivariable linear regression model, discrete choice model and structural equation model will be comprehensively used to analyze on the family intention of the customized student routes. What is more, there exists a significant difference in decision-making process between children’s school travel modes and commuting modes. For example, internet-based car services such as Didi Taxi are also in the category of customized travel services, and passengers show strong sensitivity to the personalized service functions of such model. This mainly lies in that the taxi mode has become a common way for urban residents to travel, and its transportation routes and service quality have been widely accepted and recognized by passengers. As a result, passengers are more concerned with the convenience of personalized service features from the new custom travel modes like Didi Taxi. This also leads to heterogeneity in the choice of customized travel modes, and the next research work will be carried out on this issue. Acknowledgments The research is funded by the National Natural Science Foundation of China (71701215), Foundation of Central South University, China (No. 502045002), Postdoctoral Science Foundation of China (No. 2018M630914 and 2019T120716). References [1] L.M. Wen, D. Fry, C. Rissel, H. Dirkis, A. Balafas, D. Merom, Factors associated with children being driven to school: implications for walk to school programs, Health Educ. Res. 23 (2008) 325–334. [2] E.O.D. Waygood, Y.O. Susilo, Walking to school in Scotland: Do perceptions of neighbourhood quality matter? IATSS Res. 38 (2015) 125–129. [3] N.C. McDonald, A.E. 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Please cite this article as: J. Hao, L. Zhang, X. Ji et al., Modeling and analyzing of family intention for the customized student routes: A case study in China, Physica A (2019) 123399, https://doi.org/10.1016/j.physa.2019.123399.

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Please cite this article as: J. Hao, L. Zhang, X. Ji et al., Modeling and analyzing of family intention for the customized student routes: A case study in China, Physica A (2019) 123399, https://doi.org/10.1016/j.physa.2019.123399.

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Please cite this article as: J. Hao, L. Zhang, X. Ji et al., Modeling and analyzing of family intention for the customized student routes: A case study in China, Physica A (2019) 123399, https://doi.org/10.1016/j.physa.2019.123399.