# Familiar Road Loyalty Modeling Considering the Effect of Truckers’ Emotional Value

Authors: Panyi Wei; Jianling Huang; Yanyan Chen; Ronggui Zhou; Ning Chen; Yunchao Zhang
Journal: Journal of Advanced Transportation
 (2023)

---

## Abstract

This paper investigates the internal mechanism of the trucker’s willingness to use familiar roads by constructing a structural equation model of road loyalty, in which the influence of the trucker’s “emotional value” is additionally considered. The proposed method can be used to understand the trucker’s psychological needs to improve the level of road service. Based on questionnaire data, AMOS software was used to analyze the correlations and corresponding parameters among 10 latent variables and 30 explicit variables. The model results show that, in addition to the value of roads as a commodity, the emotional dependence of truckers in the process of using them also has an impact on the perceived value of road services. The results of model index scores show that truck drivers’ loyalty to the road does not represent their satisfaction, and familiarity with the road is still “forced” to be the first choice of most truckers due to the cost and trust in the reliability of unfamiliar roads when the road service quality and experience do not meet truckers’ expectations. The latent variable scores indicate that improvements in “road safety” and “costs” are the key points to improve truckers’ overall satisfaction with road freight access.


---

## Body

## 1. Introduction

As the main mode of cargo transportation in China, road freight is the largest industry in the transportation industry, with the largest number of employees and being closely related to social production and life, and the volume of road freight accounted for 73.8% of the total freight in China in 2020 [1]. Road service quality and service level have an important impact on the driving experience and route choice of road freight drivers (hereafter referred to as “truck drivers”). Evaluating the quality and level of road service from the perspective of truck drivers and improving the road experience of truck drivers are issues that deserve great attention in the process of high-quality development of road infrastructure and the road freight industry [2–4].Previous research on truck behavior choice can be mainly divided into truck travel influence factor research and truck path selection research. For the study on the influence factors of truck travel, the existing studies mainly focus on personal attributes (such as driving age, economic level, and education level), psychological conditions (such as irritability, fatigue, and anger), characteristics of the goods carried and driving road conditions, etc. Arentze et al. [5] believe that personal economic income, road conditions, and road toll policy have certain impact on the road choice of truck drivers and suggest that the way of toll can guide drivers to choose the driving path. Tao and Zhang [6] incorporated psychological cognitive factors such as truck drivers’ emotions and states into the logit model to improve the goodness of fit and explanatory power of the behavioral choice model. Feng et al. [7] found that heavy truck drivers prefer to choose high-grade roads and avoid urban roads when making travel choices. In addition, freight logistics cost, transportation time, full-load rate, number of logistics nodes, road preference, and pass satisfaction significantly affect the travel choice behavior of urban truck drivers [8–10]. In terms of truck route selection models, a large number of scholars build travel route selection models based on behavioral selection influencing factors extracted from different data. Toledo et al. [11] fused GPS and RP data to construct a travel route selection model for truck drivers and comprehensively analyzed the influence of travel characteristics (travel time, distance, and road class) and truck driver attributes on travel selection preferences. Wu et al. [12] used driving behavior record data of dangerous goods transportation vehicles from 2016 to 2018 and established a Bayesian relational model incorporating key factors such as driver behavior characteristics, cargo attributes, and driver violation records; Sharma et al. [13] used Bluetooth data, loop detector data, and variable information sign data to model the route selection behavior of truck drivers, and applied binary logit and mixed logit models were fitted to the route selection results of van drivers. Although a large number of studies have investigated and modeled truck drivers’ route choice behavior, few studies have considered the impact of truck drivers’ emotional value, loyalty, and other factors on their route choice behavior decisions.Loyalty evaluation has been widely used in the field of transportation in order to understand the traveler’s choice mechanism and demand for transportation modes. For example, Chou and Kim [14] defined public transportation loyalty and analyzed the relationship between several indicators and public transportation loyalty based on passenger questionnaire data and found that the level of public transportation service has a significant impact on passenger loyalty, and the level of passenger satisfaction is the closest to the level of passenger loyalty, but the former cannot completely replace the latter. Sun [15] constructed a public transportation loyalty evaluation model by considering the emotional value of passengers. The model, through the analysis of Xiamen bus passenger questionnaire data, found that passengers have a low level of satisfaction with the overall level of bus service but are forced to continue to rely on bus travel due to a variety of factors. Zhu [16] constructed a high-speed rail loyalty evaluation model, considering service quality, corporate image, passenger satisfaction, and passenger loyalty, analyzed the relevant factors affecting the quality of high-speed rail services, and found that passenger satisfaction has a direct and positive influence on loyalty. Thompson and Schofield [17] found, through the analysis of the factors influencing passenger bus loyalty, that the ease of public transportation accessibility is the main factor influencing passengers’ public transportation loyalty and that it exceeds safety and speed. Johnson et al. [18] considered the frequency of passengers taking public transportation, divided public transportation satisfaction into overall satisfaction and cumulative satisfaction, and constructed a correlation analysis model between the two, concluding that the greater the overall satisfaction is, the higher the cumulative satisfaction. Eboli and Mazzulla [19] constructed a dual-goal analysis model of public transportation satisfaction and loyalty in a dual-objective analysis model, and the results showed that time reliability, frequency of departure, and bus-car environment have a direct effect on the quality of bus service and have an indirect effect on passenger bus satisfaction and loyalty.From the abovementioned studies, it can be seen that research on the loyalty of the public transportation mode has made some achievements and that the relevant research results can play a supporting role in improving the quality of public transportation services and enhancing the level of public transportation services. By analogy with the relationship between travelers and travel modes and the loyalty of passengers to public transportation, the process of using a road can be regarded as the enjoyment of the “service” provided by the road to truck drivers, and the quality and level of service of the road determine the willingness of truck drivers to choose the road again, i.e., the loyalty. If the loyalty is low, it means that the experience of using the road does not satisfy the truck driver; hence, the truck driver’s willingness to choose the road again would be low under the same transportation origin and destination.From the literature review, we found that “loyalty” is considered to be an important indicator to objectively reflect the level of customer recognition of a product. Integrating the shortcomings of existing studies on truck drivers’ route choice behavior and the application of loyalty evaluation in transportation mode choice, this paper combines the two, tries to establish a road loyalty evaluation index system from the perspective of the commodity attributes of roads and truck drivers’ enjoyment of road services, and constructs a structural equation model for road loyalty evaluation based on questionnaire data. In addition, considering that the choice of a familiar road by truck drivers is not only a short-term decision but also involves the change of personal habits and emotional attitudes, the influence of emotional accumulation on the change of loyalty is considered in the model construction to enhance the explanatory power of the model.


## 2. Methodology

### 2.1. Construction of the Structural Model

The driving decision behavior of truck drivers on the road is often related to their physiological, psychological, and personal characteristics [6] and other latent variables that are difficult to measure accurately, and traditional statistical methods cannot deal with these latent variables effectively, while structural equation modeling is a statistical method to analyze the relationship between variables based on the covariance matrix of variables [20], which can deal with both latent variables and their indicators. At the same time, traditional linear regression analysis allows for measurement error in the dependent variable, but it has to assume that the independent variables are error-free, which also tends to cause problems such as inaccurate analysis of the results.In order to analyze the influence of truck drivers’ emotional value in terms of route choice, the concept of truck drivers’ perception of road value was introduced with reference to the concept of customers’ perception of commodity value in marketing. In marketing, customer’s perception of the value of a product generally consists of the following two parts: (1) the inherent “utility value” of the product itself, which represents the most direct use value that the product can bring to the customer, and (2) the emotional value generated by the customer’s use of the product, which represents the accumulation of emotions generated in the process of using the product. Together, the two constitute the actual value of the product; moreover, the actual value of the product and the “cost” together determine the customer’s “value perception” of the product [21]. In addition to the “value perception,” customers have “expectations” about the quality and value of the goods they consume, and different people have different “expectation levels.” The difference between “value perception” and “expectation level” is expressed as the customer’s “satisfaction” with the product and it affects the model’s outcome variable “loyalty” [22]. This process is shown in Figure 1.Figure 1
Path of the intrinsic mechanism of customer loyalty to goods.Combined with the characteristics of truck drivers enjoying road services, the “utility value” of roads is divided into three potential variables: “traffic environment,” “road environment,” and “value-added services.” In addition, the research results of Zhao et al. [23] show that the “emotional value” of travelers is more sensitive to the convenience and safety of transportation modes. Referring to this conclusion, this paper assumes that truck drivers have a direct relationship between the “emotional value” and “road safety and convenience.” Based on Figure 1, a structural model of truck drivers’ loyalty to familiar roads is built, as shown in Figure 2.Figure 2
Structural model of truckers’ loyalty to familiar roads.


### 2.2. Model Index System Construction

Since latent variables could not be observed directly through survey data, it required a set of response indicators to measure each of them. Referring to existing literature, observed variables involved in each initial measurement model were set as follows.Considering that the latent variables cannot be directly observed, observable explicit variables are selected to measure the latent variables in this study.


#### 2.2.1. Traffic Environment

The latent variables of “traffic environment” were measured by four explicit variables: “daily traffic flow on the road,” “frequency of severe congestion,” “speed limit value on the road,” and “percentage of large vehicles.”


#### 2.2.2. Road Environment

The “road environment” latent variable is measured by three explicit variables: “route driving comfort,” “road driving comfort,” and “natural environment along the road.”


#### 2.2.3. Road Safety

Road safety includes the overall recognition of road safety by truck drivers, the proportion of high-risk road sections, and whether the truck drivers interviewed have been involved in traffic accidents on the road. The overall recognition of road safety comes from the subjective driving experience of the interviewed truck drivers; the proportion of high-risk road sections reflects the safety of the road sections themselves; whether the interviewed truck drivers have been involved in traffic accidents on the road section represents the specific performance of road safety to a certain extent, and the accidents also have an impact on the drivers’ emotions to a certain extent.


#### 2.2.4. Value-Added Services

Value-added services can be understood as services that customers can feel in addition to the value of the goods themselves. For truck drivers, the gas stations along the road, service areas, and emergency assistance along the road can be seen as value-added services.


#### 2.2.5. Emotional Value

The emotional value measurement model constructed by Babin et al. [24] based on 12 measurement indicators is considered to have strong portability. In this paper, combining the transport characteristics of road freight, nine indicators are selected to describe the degree of the emotional dependence of truck drivers on familiarity with the road, as shown in Table 1.Table 1
The designed content of questionnaires in the survey.
Latent variablesSymbolsObserved variablesNotationsTraffic environmentXTRAF(1) Daily traffic flow on the roadX1(2) Frequency of experiencing severe congestionX2(3) Road speed limit valueX3Road environmentXROAD(1) Percentage of large vehiclesX20(2) Route driving comfortX4(3) Road driving comfortX5(4) Natural environment along the roadX21Road safetyXSAFE(1) Overall recognition of road safetyX6(2) Proportion of high-risk road sectionsX7(3) Whether traffic accidents have occurred on the roadX22Value-added servicesXSERV(1) Satisfaction with the use of gas stations along the routeX8(2) Satisfaction with the use of service areas along the routeX9(3) Satisfaction with emergency rescue services along the routeX23Emotional valueXEMOT(1) The effect of choosing a familiar road on moodX10(2) Feeling of safety when driving on familiar roadsX11(3) Comfortable driving on familiar roadsX12(4) Satisfaction with the reliability of driving time on familiar roadsX13(5) Satisfaction with the reliability of costs incurred on familiar roadsX14(6) Satisfaction with driving conditions on familiar roadsX24(7) Satisfaction with the quality of service on familiar roadsX25(8) Whether the idea of trying other routes comes up in the process of using familiar roadsX26(9) Whether the choice of familiar roads is limited by other factorsX27Fee costXCOST(1) Traveling time costX15(2) Monetary costs such as fuel costs and toll feesX16Value perceptionYVALU(1) Satisfaction with the safety of the road compared to the cost of the roadY1(2) Satisfaction with the cost of driving on the road compared to the safety of the roadY2Expectation levelXEXPE(1) Expectation of overall driving experienceX17(2) Expectation of road safetyX18(3) Expectation of smoothness of the roadX19(4) Expectation of personalized demand for value-added services along the routeX28Satisfaction with familiar roadsYSATI(1) Overall satisfaction level of road useY3(2) Satisfaction level of road use expectationY4(3) Gap with the ideal road use experienceY5Loyalty on familiar roadsYLOYA(1) Whether familiar roads are preferred in the next transportation situation with the same origin and destinationY6(2) Whether familiar roads are more reliable than unfamiliar roadsY7(3) Whether familiar roads are still preferred over unfamiliar roads with better traffic environmentY8(4) Whether familiar roads are still preferred over unfamiliar roads with better road environmentY9(5) Whether familiar roads are still preferred over unfamiliar roads with better road safetyY10(6) Whether familiar roads are still preferred over unfamiliar roads with better operational service qualityY11(7) Are familiar roads still preferred over unfamiliar roads with lower costs?Y12


#### 2.2.6. Fee Cost

Considered from the perspective of broad cost, fuel cost, and road tolls, plus other components, such cost components belong to the monetary cost of road freight and transportation time belongs to the time cost of road freight, which together constitute the fee cost of road freight.


#### 2.2.7. Value Perception

Customer perception of the value of goods is mainly from two sources: one is the price of goods, and the second is the level of commodity services. Considering that the cost of transportation includes the cost of time and money and security is an important guarantee of transport, this paper selects road safety and costs as the value perception measurement indicators, as shown in Table1.


#### 2.2.8. Expectation Level

Expectation represents the customer’s expectation of the value of the services that can be provided by the goods. In this paper, three variables are used to describe truck drivers’ expectations of the proposed road, as shown in Table1.


#### 2.2.9. Satisfaction with Familiar Roads

According to the satisfaction measurement model proposed by Shiftan et al. [25], this paper uses “overall satisfaction with road use,” “satisfaction with road use expectations,” and “gap with ideal road use experience” as satisfaction indicators.


#### 2.2.10. Loyalty on Familiar Roads

Shiftan et al. [25] transferred the loyalty model in marketing to the measurement of passenger loyalty to buses; analogous to the impact of transportation on travelers, the impact of roads on truck drivers is equally important. Referring to this model, this paper establishes a measurement model of truck drivers’ loyalty to the road.Ultimately, the variables needed to construct the structural equation model in this paper are shown in Table1.


## 2.1. Construction of the Structural Model

The driving decision behavior of truck drivers on the road is often related to their physiological, psychological, and personal characteristics [6] and other latent variables that are difficult to measure accurately, and traditional statistical methods cannot deal with these latent variables effectively, while structural equation modeling is a statistical method to analyze the relationship between variables based on the covariance matrix of variables [20], which can deal with both latent variables and their indicators. At the same time, traditional linear regression analysis allows for measurement error in the dependent variable, but it has to assume that the independent variables are error-free, which also tends to cause problems such as inaccurate analysis of the results.In order to analyze the influence of truck drivers’ emotional value in terms of route choice, the concept of truck drivers’ perception of road value was introduced with reference to the concept of customers’ perception of commodity value in marketing. In marketing, customer’s perception of the value of a product generally consists of the following two parts: (1) the inherent “utility value” of the product itself, which represents the most direct use value that the product can bring to the customer, and (2) the emotional value generated by the customer’s use of the product, which represents the accumulation of emotions generated in the process of using the product. Together, the two constitute the actual value of the product; moreover, the actual value of the product and the “cost” together determine the customer’s “value perception” of the product [21]. In addition to the “value perception,” customers have “expectations” about the quality and value of the goods they consume, and different people have different “expectation levels.” The difference between “value perception” and “expectation level” is expressed as the customer’s “satisfaction” with the product and it affects the model’s outcome variable “loyalty” [22]. This process is shown in Figure 1.Figure 1
Path of the intrinsic mechanism of customer loyalty to goods.Combined with the characteristics of truck drivers enjoying road services, the “utility value” of roads is divided into three potential variables: “traffic environment,” “road environment,” and “value-added services.” In addition, the research results of Zhao et al. [23] show that the “emotional value” of travelers is more sensitive to the convenience and safety of transportation modes. Referring to this conclusion, this paper assumes that truck drivers have a direct relationship between the “emotional value” and “road safety and convenience.” Based on Figure 1, a structural model of truck drivers’ loyalty to familiar roads is built, as shown in Figure 2.Figure 2
Structural model of truckers’ loyalty to familiar roads.


## 2.2. Model Index System Construction

Since latent variables could not be observed directly through survey data, it required a set of response indicators to measure each of them. Referring to existing literature, observed variables involved in each initial measurement model were set as follows.Considering that the latent variables cannot be directly observed, observable explicit variables are selected to measure the latent variables in this study.


### 2.2.1. Traffic Environment

The latent variables of “traffic environment” were measured by four explicit variables: “daily traffic flow on the road,” “frequency of severe congestion,” “speed limit value on the road,” and “percentage of large vehicles.”


### 2.2.2. Road Environment

The “road environment” latent variable is measured by three explicit variables: “route driving comfort,” “road driving comfort,” and “natural environment along the road.”


### 2.2.3. Road Safety

Road safety includes the overall recognition of road safety by truck drivers, the proportion of high-risk road sections, and whether the truck drivers interviewed have been involved in traffic accidents on the road. The overall recognition of road safety comes from the subjective driving experience of the interviewed truck drivers; the proportion of high-risk road sections reflects the safety of the road sections themselves; whether the interviewed truck drivers have been involved in traffic accidents on the road section represents the specific performance of road safety to a certain extent, and the accidents also have an impact on the drivers’ emotions to a certain extent.


### 2.2.4. Value-Added Services

Value-added services can be understood as services that customers can feel in addition to the value of the goods themselves. For truck drivers, the gas stations along the road, service areas, and emergency assistance along the road can be seen as value-added services.


### 2.2.5. Emotional Value

The emotional value measurement model constructed by Babin et al. [24] based on 12 measurement indicators is considered to have strong portability. In this paper, combining the transport characteristics of road freight, nine indicators are selected to describe the degree of the emotional dependence of truck drivers on familiarity with the road, as shown in Table 1.Table 1
The designed content of questionnaires in the survey.
Latent variablesSymbolsObserved variablesNotationsTraffic environmentXTRAF(1) Daily traffic flow on the roadX1(2) Frequency of experiencing severe congestionX2(3) Road speed limit valueX3Road environmentXROAD(1) Percentage of large vehiclesX20(2) Route driving comfortX4(3) Road driving comfortX5(4) Natural environment along the roadX21Road safetyXSAFE(1) Overall recognition of road safetyX6(2) Proportion of high-risk road sectionsX7(3) Whether traffic accidents have occurred on the roadX22Value-added servicesXSERV(1) Satisfaction with the use of gas stations along the routeX8(2) Satisfaction with the use of service areas along the routeX9(3) Satisfaction with emergency rescue services along the routeX23Emotional valueXEMOT(1) The effect of choosing a familiar road on moodX10(2) Feeling of safety when driving on familiar roadsX11(3) Comfortable driving on familiar roadsX12(4) Satisfaction with the reliability of driving time on familiar roadsX13(5) Satisfaction with the reliability of costs incurred on familiar roadsX14(6) Satisfaction with driving conditions on familiar roadsX24(7) Satisfaction with the quality of service on familiar roadsX25(8) Whether the idea of trying other routes comes up in the process of using familiar roadsX26(9) Whether the choice of familiar roads is limited by other factorsX27Fee costXCOST(1) Traveling time costX15(2) Monetary costs such as fuel costs and toll feesX16Value perceptionYVALU(1) Satisfaction with the safety of the road compared to the cost of the roadY1(2) Satisfaction with the cost of driving on the road compared to the safety of the roadY2Expectation levelXEXPE(1) Expectation of overall driving experienceX17(2) Expectation of road safetyX18(3) Expectation of smoothness of the roadX19(4) Expectation of personalized demand for value-added services along the routeX28Satisfaction with familiar roadsYSATI(1) Overall satisfaction level of road useY3(2) Satisfaction level of road use expectationY4(3) Gap with the ideal road use experienceY5Loyalty on familiar roadsYLOYA(1) Whether familiar roads are preferred in the next transportation situation with the same origin and destinationY6(2) Whether familiar roads are more reliable than unfamiliar roadsY7(3) Whether familiar roads are still preferred over unfamiliar roads with better traffic environmentY8(4) Whether familiar roads are still preferred over unfamiliar roads with better road environmentY9(5) Whether familiar roads are still preferred over unfamiliar roads with better road safetyY10(6) Whether familiar roads are still preferred over unfamiliar roads with better operational service qualityY11(7) Are familiar roads still preferred over unfamiliar roads with lower costs?Y12


### 2.2.6. Fee Cost

Considered from the perspective of broad cost, fuel cost, and road tolls, plus other components, such cost components belong to the monetary cost of road freight and transportation time belongs to the time cost of road freight, which together constitute the fee cost of road freight.


### 2.2.7. Value Perception

Customer perception of the value of goods is mainly from two sources: one is the price of goods, and the second is the level of commodity services. Considering that the cost of transportation includes the cost of time and money and security is an important guarantee of transport, this paper selects road safety and costs as the value perception measurement indicators, as shown in Table1.


### 2.2.8. Expectation Level

Expectation represents the customer’s expectation of the value of the services that can be provided by the goods. In this paper, three variables are used to describe truck drivers’ expectations of the proposed road, as shown in Table1.


### 2.2.9. Satisfaction with Familiar Roads

According to the satisfaction measurement model proposed by Shiftan et al. [25], this paper uses “overall satisfaction with road use,” “satisfaction with road use expectations,” and “gap with ideal road use experience” as satisfaction indicators.


### 2.2.10. Loyalty on Familiar Roads

Shiftan et al. [25] transferred the loyalty model in marketing to the measurement of passenger loyalty to buses; analogous to the impact of transportation on travelers, the impact of roads on truck drivers is equally important. Referring to this model, this paper establishes a measurement model of truck drivers’ loyalty to the road.Ultimately, the variables needed to construct the structural equation model in this paper are shown in Table1.


## 2.2.1. Traffic Environment

The latent variables of “traffic environment” were measured by four explicit variables: “daily traffic flow on the road,” “frequency of severe congestion,” “speed limit value on the road,” and “percentage of large vehicles.”


## 2.2.2. Road Environment

The “road environment” latent variable is measured by three explicit variables: “route driving comfort,” “road driving comfort,” and “natural environment along the road.”


## 2.2.3. Road Safety

Road safety includes the overall recognition of road safety by truck drivers, the proportion of high-risk road sections, and whether the truck drivers interviewed have been involved in traffic accidents on the road. The overall recognition of road safety comes from the subjective driving experience of the interviewed truck drivers; the proportion of high-risk road sections reflects the safety of the road sections themselves; whether the interviewed truck drivers have been involved in traffic accidents on the road section represents the specific performance of road safety to a certain extent, and the accidents also have an impact on the drivers’ emotions to a certain extent.


## 2.2.4. Value-Added Services

Value-added services can be understood as services that customers can feel in addition to the value of the goods themselves. For truck drivers, the gas stations along the road, service areas, and emergency assistance along the road can be seen as value-added services.


## 2.2.5. Emotional Value

The emotional value measurement model constructed by Babin et al. [24] based on 12 measurement indicators is considered to have strong portability. In this paper, combining the transport characteristics of road freight, nine indicators are selected to describe the degree of the emotional dependence of truck drivers on familiarity with the road, as shown in Table 1.Table 1
The designed content of questionnaires in the survey.
Latent variablesSymbolsObserved variablesNotationsTraffic environmentXTRAF(1) Daily traffic flow on the roadX1(2) Frequency of experiencing severe congestionX2(3) Road speed limit valueX3Road environmentXROAD(1) Percentage of large vehiclesX20(2) Route driving comfortX4(3) Road driving comfortX5(4) Natural environment along the roadX21Road safetyXSAFE(1) Overall recognition of road safetyX6(2) Proportion of high-risk road sectionsX7(3) Whether traffic accidents have occurred on the roadX22Value-added servicesXSERV(1) Satisfaction with the use of gas stations along the routeX8(2) Satisfaction with the use of service areas along the routeX9(3) Satisfaction with emergency rescue services along the routeX23Emotional valueXEMOT(1) The effect of choosing a familiar road on moodX10(2) Feeling of safety when driving on familiar roadsX11(3) Comfortable driving on familiar roadsX12(4) Satisfaction with the reliability of driving time on familiar roadsX13(5) Satisfaction with the reliability of costs incurred on familiar roadsX14(6) Satisfaction with driving conditions on familiar roadsX24(7) Satisfaction with the quality of service on familiar roadsX25(8) Whether the idea of trying other routes comes up in the process of using familiar roadsX26(9) Whether the choice of familiar roads is limited by other factorsX27Fee costXCOST(1) Traveling time costX15(2) Monetary costs such as fuel costs and toll feesX16Value perceptionYVALU(1) Satisfaction with the safety of the road compared to the cost of the roadY1(2) Satisfaction with the cost of driving on the road compared to the safety of the roadY2Expectation levelXEXPE(1) Expectation of overall driving experienceX17(2) Expectation of road safetyX18(3) Expectation of smoothness of the roadX19(4) Expectation of personalized demand for value-added services along the routeX28Satisfaction with familiar roadsYSATI(1) Overall satisfaction level of road useY3(2) Satisfaction level of road use expectationY4(3) Gap with the ideal road use experienceY5Loyalty on familiar roadsYLOYA(1) Whether familiar roads are preferred in the next transportation situation with the same origin and destinationY6(2) Whether familiar roads are more reliable than unfamiliar roadsY7(3) Whether familiar roads are still preferred over unfamiliar roads with better traffic environmentY8(4) Whether familiar roads are still preferred over unfamiliar roads with better road environmentY9(5) Whether familiar roads are still preferred over unfamiliar roads with better road safetyY10(6) Whether familiar roads are still preferred over unfamiliar roads with better operational service qualityY11(7) Are familiar roads still preferred over unfamiliar roads with lower costs?Y12


## 2.2.6. Fee Cost

Considered from the perspective of broad cost, fuel cost, and road tolls, plus other components, such cost components belong to the monetary cost of road freight and transportation time belongs to the time cost of road freight, which together constitute the fee cost of road freight.


## 2.2.7. Value Perception

Customer perception of the value of goods is mainly from two sources: one is the price of goods, and the second is the level of commodity services. Considering that the cost of transportation includes the cost of time and money and security is an important guarantee of transport, this paper selects road safety and costs as the value perception measurement indicators, as shown in Table1.


## 2.2.8. Expectation Level

Expectation represents the customer’s expectation of the value of the services that can be provided by the goods. In this paper, three variables are used to describe truck drivers’ expectations of the proposed road, as shown in Table1.


## 2.2.9. Satisfaction with Familiar Roads

According to the satisfaction measurement model proposed by Shiftan et al. [25], this paper uses “overall satisfaction with road use,” “satisfaction with road use expectations,” and “gap with ideal road use experience” as satisfaction indicators.


## 2.2.10. Loyalty on Familiar Roads

Shiftan et al. [25] transferred the loyalty model in marketing to the measurement of passenger loyalty to buses; analogous to the impact of transportation on travelers, the impact of roads on truck drivers is equally important. Referring to this model, this paper establishes a measurement model of truck drivers’ loyalty to the road.Ultimately, the variables needed to construct the structural equation model in this paper are shown in Table1.


## 3. Data Collection and Analysis

### 3.1. Data Collection

The questionnaire of the paper was mainly targeted to drivers of three freight companies in two regions of China, Beijing and Hebei. We announced that all respondents would be rewarded with a WeChat red packet of 5–10 RMB after completing the questionnaire as required. Considering the lack of road familiarity for drivers with too little driving experience and the Chinese driving age requirement for drivers to use highways, we asked all respondents to have at least 3 years of driving experience. A total of 287 questionnaires were distributed, and excluding incomplete and obviously wrong questionnaires, a total of 268 valid questionnaires were obtained, with a return rate of 93.4%. As the descriptive statistics reported in Table2, all 268 respondents were professional truck drivers with at least 3 years of driving experience (AVG = 15.3 years, SD = 4.9 years), with an age range of 23–62 years (AVG = 46.5 years, SD = 4.5 years). Among them, there were 259 male drivers, accounting for 96.7%, and 9 female truck drivers, accounting for 3.3%, which is comparable to the ratio of male to female drivers in the three transport companies interviewed and in line with the current characteristics that professional truck drivers in China are much more male than female. In addition, it can also be seen from the abovementioned data that the average age of professional truck drivers is larger (AVG = 46.5 years), which is related to the shortage of professional freight drivers in China in recent years and the reluctance of young people to work as professional truck drivers, which also objectively indicates the serious aging of the profession of truck drivers in China.Table 2
Descriptive statistics of respondents.
AttributesStatisticsGenderMale (96.7%), female (3.3%)Driving experience (years)AVG = 15.3, SD = 4.9 ≤3 (0%), 4-5 (9.7%), 6–10 (15.3%), 11–15 (36.2%), 16–20 (31.0%), ≥21 (7.8%)Age (years)AVG = 46.5, SD = 4.5 ≤23 (0%), 24–30 (12.3%), 31–40 (20.9%), 41–50 (26.1%), 51–60 (38.8%), >60 (1.9%)


### 3.2. Data Reliability Check

To ensure that the survey variables listed in Table1 are stable measures of truck drivers’ loyalty to familiar roads, homogeneity reliability tests were conducted on the variables shown in the table. In this paper, Cronbach’s alpha coefficient is chosen to test the homogeneity reliability among the variables. If the alpha coefficient is greater than 0.7, the homogeneity reliability between the variables is considered high [26]. Table 3 shows the results of the homogeneity reliability test after excluding the variables with poor alpha coefficient performance. It can be seen that the homogeneity of the measured variables is generally high, indicating that the variables investigated by the questionnaire perform well in terms of consistency before and after measuring truck drivers’ loyalty to familiar roads.Table 3
Results of survey data reliability analysis.
Latent variableCronbach’s alphaVariables need to be excludedComposite reliabilityXTRAF0.679X200.733XROAD0.716None0.716XSAFE0.855None0.855XSERV0.608X230.716XEMOT0.779None0.779XCOST0.801None0.801YVALU0.817None0.817XEXPE0.768None0.768YSATI0.725None0.725YLOYA0.711None0.711


### 3.3. Data Validity Check

Validity reflects the extent to which the target data characterize a particular characteristic [27]. To ensure that the survey variables listed in Table 1 can accurately measure truck driver loyalty on familiar roads, validity tests were conducted on the variables listed in the table. The KMO test and Bartlett’s spherical test were shown to be better for validity checking of data before factor analysis [15]. According to the results of existing studies, the corresponding variables can measure loyalty more accurately when the results of both tests meet the following conditions:(i)
KMO statistic values greater than 0.7 (range 0 to 1), the measured variables are strongly correlated.(ii)
Bartlett’s sphericity test values with probability of significance less than 0.05, the measured variables are independent to some extent.(iii)
The factor loadings are greater than 0.5, and the measured variable set (significant variables) can be useful.Table4 shows the results of the validity tests for each latent variable. From Table 4, it is clear that the survey variables listed in Table 1 can accurately measure truck drivers’ fidelity to familiarity with the road. However, considering that the variable is more difficult to play a measurement role when the load of the rotation factor of the explicit variable is less than 0.5 [28], the variable with the load of the rotation factor less than 0.5 is deleted, and the results are shown in Table 5.Table 4
Validity testing of model variables.
Latent variableKMO statisticBartlett’s spherical testContribution rate of cumulative variance (%)XTRAF0.749P<0.0559.623XROAD0.768P<0.0554.364XSAFE0.844P<0.0568.334XSERV0.725P<0.0559.874XEMOT0.874P<0.0561.205XCOST0.5 (fixed)P<0.0561.191YVALU0.5 (fixed)P<0.0561.258XEXPE0.736P<0.0552.689YSATI0.813P<0.0567.167YLOYA0.766P<0.0558.341Table 5
Variables that need to be excluded.
Latent variableVariables need to be excludedFactor loadXTRAFX200.328XROADX210.165XSAFEX220.234XSERVX230.212XEMOTX240.293X250.401X260.167X270.381XCOSTNoneNoneYVALUNoneNoneXEXPEX280.286YSATINoneNoneYLOYANoneNone


## 3.1. Data Collection

The questionnaire of the paper was mainly targeted to drivers of three freight companies in two regions of China, Beijing and Hebei. We announced that all respondents would be rewarded with a WeChat red packet of 5–10 RMB after completing the questionnaire as required. Considering the lack of road familiarity for drivers with too little driving experience and the Chinese driving age requirement for drivers to use highways, we asked all respondents to have at least 3 years of driving experience. A total of 287 questionnaires were distributed, and excluding incomplete and obviously wrong questionnaires, a total of 268 valid questionnaires were obtained, with a return rate of 93.4%. As the descriptive statistics reported in Table2, all 268 respondents were professional truck drivers with at least 3 years of driving experience (AVG = 15.3 years, SD = 4.9 years), with an age range of 23–62 years (AVG = 46.5 years, SD = 4.5 years). Among them, there were 259 male drivers, accounting for 96.7%, and 9 female truck drivers, accounting for 3.3%, which is comparable to the ratio of male to female drivers in the three transport companies interviewed and in line with the current characteristics that professional truck drivers in China are much more male than female. In addition, it can also be seen from the abovementioned data that the average age of professional truck drivers is larger (AVG = 46.5 years), which is related to the shortage of professional freight drivers in China in recent years and the reluctance of young people to work as professional truck drivers, which also objectively indicates the serious aging of the profession of truck drivers in China.Table 2
Descriptive statistics of respondents.
AttributesStatisticsGenderMale (96.7%), female (3.3%)Driving experience (years)AVG = 15.3, SD = 4.9 ≤3 (0%), 4-5 (9.7%), 6–10 (15.3%), 11–15 (36.2%), 16–20 (31.0%), ≥21 (7.8%)Age (years)AVG = 46.5, SD = 4.5 ≤23 (0%), 24–30 (12.3%), 31–40 (20.9%), 41–50 (26.1%), 51–60 (38.8%), >60 (1.9%)


## 3.2. Data Reliability Check

To ensure that the survey variables listed in Table1 are stable measures of truck drivers’ loyalty to familiar roads, homogeneity reliability tests were conducted on the variables shown in the table. In this paper, Cronbach’s alpha coefficient is chosen to test the homogeneity reliability among the variables. If the alpha coefficient is greater than 0.7, the homogeneity reliability between the variables is considered high [26]. Table 3 shows the results of the homogeneity reliability test after excluding the variables with poor alpha coefficient performance. It can be seen that the homogeneity of the measured variables is generally high, indicating that the variables investigated by the questionnaire perform well in terms of consistency before and after measuring truck drivers’ loyalty to familiar roads.Table 3
Results of survey data reliability analysis.
Latent variableCronbach’s alphaVariables need to be excludedComposite reliabilityXTRAF0.679X200.733XROAD0.716None0.716XSAFE0.855None0.855XSERV0.608X230.716XEMOT0.779None0.779XCOST0.801None0.801YVALU0.817None0.817XEXPE0.768None0.768YSATI0.725None0.725YLOYA0.711None0.711


## 3.3. Data Validity Check

Validity reflects the extent to which the target data characterize a particular characteristic [27]. To ensure that the survey variables listed in Table 1 can accurately measure truck driver loyalty on familiar roads, validity tests were conducted on the variables listed in the table. The KMO test and Bartlett’s spherical test were shown to be better for validity checking of data before factor analysis [15]. According to the results of existing studies, the corresponding variables can measure loyalty more accurately when the results of both tests meet the following conditions:(i)
KMO statistic values greater than 0.7 (range 0 to 1), the measured variables are strongly correlated.(ii)
Bartlett’s sphericity test values with probability of significance less than 0.05, the measured variables are independent to some extent.(iii)
The factor loadings are greater than 0.5, and the measured variable set (significant variables) can be useful.Table4 shows the results of the validity tests for each latent variable. From Table 4, it is clear that the survey variables listed in Table 1 can accurately measure truck drivers’ fidelity to familiarity with the road. However, considering that the variable is more difficult to play a measurement role when the load of the rotation factor of the explicit variable is less than 0.5 [28], the variable with the load of the rotation factor less than 0.5 is deleted, and the results are shown in Table 5.Table 4
Validity testing of model variables.
Latent variableKMO statisticBartlett’s spherical testContribution rate of cumulative variance (%)XTRAF0.749P<0.0559.623XROAD0.768P<0.0554.364XSAFE0.844P<0.0568.334XSERV0.725P<0.0559.874XEMOT0.874P<0.0561.205XCOST0.5 (fixed)P<0.0561.191YVALU0.5 (fixed)P<0.0561.258XEXPE0.736P<0.0552.689YSATI0.813P<0.0567.167YLOYA0.766P<0.0558.341Table 5
Variables that need to be excluded.
Latent variableVariables need to be excludedFactor loadXTRAFX200.328XROADX210.165XSAFEX220.234XSERVX230.212XEMOTX240.293X250.401X260.167X270.381XCOSTNoneNoneYVALUNoneNoneXEXPEX280.286YSATINoneNoneYLOYANoneNone


## 4. Results

### 4.1. Modeling and Results Analysis

Based on the results of the data reliability test and validity test, a structural equation model of truck drivers’ loyalty to familiar roads was constructed using AMOS 21.0, as shown in Figure3.Figure 3
Truckers’ loyalty model for familiar roads.The survey data were parsed using the model in Figure3 to obtain the initial calculation results, as shown in Figure 4. Five indicators, the Absolute Fitness Index (AFI), root mean square error of approximation (RMSEA), cardinality ratio of freedom (χ2/df), Value-Added Fitness Index (VFI), and Parsimonious Fitness Index (PFI), were selected to assess the goodness of fit of the model, and the fitness criteria of each indicator are shown in Table 6.Figure 4
Results of the initial model.Table 6
Model Suitability Index and suitability criteria.
Model Goodness-of-Fit Test IndexIndicator adaptation criteriaAFI>0.9RMSEA<0.05χ2/df1∼3VFI>0.9PFI>200As seen from Figure4, the model has four indicators that satisfy the fitness criteria, but the AFI is less than 0.9, indicating that the initial model has an unacceptable goodness of fit and needs to be adjusted. The correlation coefficient between the latent variables XTRAF and XROAD is very high (0.931), indicating a higher-order common factor between the two. To better explain the loyalty system, the latent variable XTRAF was removed from the initial model, a new latent variable “road traffic environment (XROADTRAF)” was defined to explain the overall road traffic environment conditions, and the model was readjusted. The adjusted structural equation model and the calculated results are shown in Figure 5.Figure 5
Results of the adjusted model.As seen from Figure5, all five indicators of the adjusted model meet the fitness criteria, indicating that the model fit is good and can be used for loyalty assessment. In addition, the following two conclusions can be drawn from the model calculation results, as shown in Figure 5:(1)
The effect of “emotional value” on “value perception” is significant. With 1 unit increase of “emotional value,” “value perception” would be promoted by 0.372 unit, which confirms that in addition to the utility value of the good itself, the accumulation of emotional experiences over time significantly affects truckers’ perception of the value of the route. Therefore, considering the emotional value of truck drivers will enhance the overall explanatory power of the model.(2)
“Satisfaction with familiar roads” has a direct and significant impact on “loyalty on familiar roads.” This result shows that satisfaction is the premise and important influencing factor of truck drivers’ loyalty, but satisfaction cannot replace loyalty.


### 4.2. The Index Score of Variables

The index score reflects the truck drivers’ recognition of the road service quality represented by this variable, with higher scores indicating greater satisfaction with the indicator and vice versa. The variable index scores are calculated as follows:(1)Sobvious=x¯i−1maxxi−minxi×100,Slatent=∑i=1kwix¯i−∑i=1kwi∑i=1kwimaxxi−minxi×100,where Sobvious is the index score of the significant variable; Slatent is the index score of the latent variable; x¯i is the mean value of the significant variable; k is the number of significant variables corresponding to the latent variable; wi is the regression coefficient of the ith significant variable in the measurement model; maxxi is the highest score of the variable; and minxi is the lowest score of the variable. Each variable has a full score of 100.Using the above formula, the index scores of each variable were calculated, as shown in Table7.Table 7
Index scores of each variable.
Latent variableObserved variableStandardized weightScore of observed variableScore of latent variableYVALUY10.52366.3468.79Y20.53671.18YSATIY30.71972.8369.17Y40.70471.39Y50.42959.4YLOYAY60.71487.1180.31Y70.80389.41Y80.64174.68Y90.52573.42Y100.53383.65Y110.62271.49Y120.63478.37XROADTRAFX10.73761.4858.36X20.5454.17X30.61862.78X40.57354.19X50.73557.85XSAFEX60.62271.3465.96X70.63560.7XSERVX80.50862.4864.49X90.51266.49XEMOTX100.73977.1476.34X110.69173.14X120.60876.14X130.63269.57X140.88783.14XCOSTX150.61366.4963.2X160.72160.4XEXPEX170.54149.5756.74


### 4.3. Discussion

From Table7, the following can be seen:(1)
Among all latent variables, “loyalty on familiar roads (YLOYA)” has the highest score (80.31). This indicates that most of the surveyed truck drivers prefer to choose the road they are familiar with, given other similar conditions.(2)
Compared to the higher score of “loyalty on familiar roads (YLOYA),” the score of “satisfaction with familiar roads (YSATI) is lower (69.17). This further indicates that truckers’ satisfaction with familiar roads is not a substitute for loyalty to familiar roads, and the higher loyalty scores and lower satisfaction scores reflect to some extent that most truckers will still choose to drive on a road they are familiar with when considering the cost of using the road and the unknown reliability of an unfamiliar road, although they are not very satisfied with the familiar roads.(3)
Analyzing the scores of “emotional value (XEMOT),” it is found that the two variables “the impact of choosing familiar roads on mood (X10)” and “satisfaction with the reliability of familiar roads in terms of cost (X14)” have the highest scores, which indicates that these two variables are the content of the interviewed truck drivers’ satisfaction with familiar roads, and compared with that in Figure 5, it is found that the regression coefficients of these two variables are also higher relative to the “emotional value,” which indicates that truck drivers’ emotions toward familiar roads mainly come from these two experiences.(4)
Analyzing the scores of loyalty on familiar roads (YLOYA), it is found that the scores of whether the familiar road is preferred in the next transportation situation (Y6) and whether the familiar road is more reliable than the unfamiliar road (Y7) are higher, which indicates that the truckers’ loyalty to the familiar road comes from the overall trust in the reliability of the familiar road to a greater extent.(5)
Comparative analysis of “whether familiar roads are still preferred over unfamiliar roads with better traffic environment (Y8),” “whether familiar roads are still preferred over unfamiliar roads with better road environment (Y9),” “whether familiar roads are still preferred over unfamiliar roads with better road safety (Y9),” “whether familiar roads are still preferred compared to unfamiliar roads with better road safety (Y10),” “whether familiar roads are still preferred compared to unfamiliar roads with better operational service quality (Y11),” and “whether familiar roads are still preferred compared to unfamiliar roads with lower cost (Y12)” shows the scores for the five significant variables, with Y10 scoring the highest and Y12 scoring the second highest. This indicates that for most of the truck drivers surveyed, trust in road safety is the primary factor in choosing familiar roads, followed by cost, while traffic environment, road environment, and operational service quality are not essential.(6)
The abovementioned analysis results show that the road loyalty evaluation model established in this thesis can analyze the influence of different factors on truck drivers’ route choice. The analysis of “emotional value” and other related indicators verifies that emotional factors play an important role in truckers’ route selection. In particular, the results of the large differences in the scores of “loyalty on familiar roads (YLOYA)” and “satisfaction with familiar roads (YSATI)” are consistent with Tao and Zhang study [6] on travelers’ attitudes toward public transportation choice. The results of the study on travelers’ attitudes toward public transportation choice are consistent with the finding that truckers are not satisfied with their road use experience and are not allowed to repeat their visits under certain constraints. This reflects that although China has developed rapidly in terms of road mileage in recent years, there is still a long way to go in terms of improving the level of road services, especially for truckers’ experience of using them. Therefore, the future improvement of the regional road network in China should not only be based on the traffic volume survey data but also on the psychological and road usage experience of road users, such as truck drivers, to understand their real feelings of using the road in order to truly do a good job of road network planning. In addition, according to the scores of relevant significant variables, “frequency of experiencing severe congestion (X2),” “route driving comfort (X4),” “road driving comfort (X5),” and several other variables have low scores. Therefore, it is suggested that road operation and management units should take targeted measures to reduce the frequency of severe road congestion, improve the comfort of route driving through methods such as the improvement of signage, and improve the comfort of road driving on old roads by strengthening the maintenance of road surfaces.


## 4.1. Modeling and Results Analysis

Based on the results of the data reliability test and validity test, a structural equation model of truck drivers’ loyalty to familiar roads was constructed using AMOS 21.0, as shown in Figure3.Figure 3
Truckers’ loyalty model for familiar roads.The survey data were parsed using the model in Figure3 to obtain the initial calculation results, as shown in Figure 4. Five indicators, the Absolute Fitness Index (AFI), root mean square error of approximation (RMSEA), cardinality ratio of freedom (χ2/df), Value-Added Fitness Index (VFI), and Parsimonious Fitness Index (PFI), were selected to assess the goodness of fit of the model, and the fitness criteria of each indicator are shown in Table 6.Figure 4
Results of the initial model.Table 6
Model Suitability Index and suitability criteria.
Model Goodness-of-Fit Test IndexIndicator adaptation criteriaAFI>0.9RMSEA<0.05χ2/df1∼3VFI>0.9PFI>200As seen from Figure4, the model has four indicators that satisfy the fitness criteria, but the AFI is less than 0.9, indicating that the initial model has an unacceptable goodness of fit and needs to be adjusted. The correlation coefficient between the latent variables XTRAF and XROAD is very high (0.931), indicating a higher-order common factor between the two. To better explain the loyalty system, the latent variable XTRAF was removed from the initial model, a new latent variable “road traffic environment (XROADTRAF)” was defined to explain the overall road traffic environment conditions, and the model was readjusted. The adjusted structural equation model and the calculated results are shown in Figure 5.Figure 5
Results of the adjusted model.As seen from Figure5, all five indicators of the adjusted model meet the fitness criteria, indicating that the model fit is good and can be used for loyalty assessment. In addition, the following two conclusions can be drawn from the model calculation results, as shown in Figure 5:(1)
The effect of “emotional value” on “value perception” is significant. With 1 unit increase of “emotional value,” “value perception” would be promoted by 0.372 unit, which confirms that in addition to the utility value of the good itself, the accumulation of emotional experiences over time significantly affects truckers’ perception of the value of the route. Therefore, considering the emotional value of truck drivers will enhance the overall explanatory power of the model.(2)
“Satisfaction with familiar roads” has a direct and significant impact on “loyalty on familiar roads.” This result shows that satisfaction is the premise and important influencing factor of truck drivers’ loyalty, but satisfaction cannot replace loyalty.


## 4.2. The Index Score of Variables

The index score reflects the truck drivers’ recognition of the road service quality represented by this variable, with higher scores indicating greater satisfaction with the indicator and vice versa. The variable index scores are calculated as follows:(1)Sobvious=x¯i−1maxxi−minxi×100,Slatent=∑i=1kwix¯i−∑i=1kwi∑i=1kwimaxxi−minxi×100,where Sobvious is the index score of the significant variable; Slatent is the index score of the latent variable; x¯i is the mean value of the significant variable; k is the number of significant variables corresponding to the latent variable; wi is the regression coefficient of the ith significant variable in the measurement model; maxxi is the highest score of the variable; and minxi is the lowest score of the variable. Each variable has a full score of 100.Using the above formula, the index scores of each variable were calculated, as shown in Table7.Table 7
Index scores of each variable.
Latent variableObserved variableStandardized weightScore of observed variableScore of latent variableYVALUY10.52366.3468.79Y20.53671.18YSATIY30.71972.8369.17Y40.70471.39Y50.42959.4YLOYAY60.71487.1180.31Y70.80389.41Y80.64174.68Y90.52573.42Y100.53383.65Y110.62271.49Y120.63478.37XROADTRAFX10.73761.4858.36X20.5454.17X30.61862.78X40.57354.19X50.73557.85XSAFEX60.62271.3465.96X70.63560.7XSERVX80.50862.4864.49X90.51266.49XEMOTX100.73977.1476.34X110.69173.14X120.60876.14X130.63269.57X140.88783.14XCOSTX150.61366.4963.2X160.72160.4XEXPEX170.54149.5756.74


## 4.3. Discussion

From Table7, the following can be seen:(1)
Among all latent variables, “loyalty on familiar roads (YLOYA)” has the highest score (80.31). This indicates that most of the surveyed truck drivers prefer to choose the road they are familiar with, given other similar conditions.(2)
Compared to the higher score of “loyalty on familiar roads (YLOYA),” the score of “satisfaction with familiar roads (YSATI) is lower (69.17). This further indicates that truckers’ satisfaction with familiar roads is not a substitute for loyalty to familiar roads, and the higher loyalty scores and lower satisfaction scores reflect to some extent that most truckers will still choose to drive on a road they are familiar with when considering the cost of using the road and the unknown reliability of an unfamiliar road, although they are not very satisfied with the familiar roads.(3)
Analyzing the scores of “emotional value (XEMOT),” it is found that the two variables “the impact of choosing familiar roads on mood (X10)” and “satisfaction with the reliability of familiar roads in terms of cost (X14)” have the highest scores, which indicates that these two variables are the content of the interviewed truck drivers’ satisfaction with familiar roads, and compared with that in Figure 5, it is found that the regression coefficients of these two variables are also higher relative to the “emotional value,” which indicates that truck drivers’ emotions toward familiar roads mainly come from these two experiences.(4)
Analyzing the scores of loyalty on familiar roads (YLOYA), it is found that the scores of whether the familiar road is preferred in the next transportation situation (Y6) and whether the familiar road is more reliable than the unfamiliar road (Y7) are higher, which indicates that the truckers’ loyalty to the familiar road comes from the overall trust in the reliability of the familiar road to a greater extent.(5)
Comparative analysis of “whether familiar roads are still preferred over unfamiliar roads with better traffic environment (Y8),” “whether familiar roads are still preferred over unfamiliar roads with better road environment (Y9),” “whether familiar roads are still preferred over unfamiliar roads with better road safety (Y9),” “whether familiar roads are still preferred compared to unfamiliar roads with better road safety (Y10),” “whether familiar roads are still preferred compared to unfamiliar roads with better operational service quality (Y11),” and “whether familiar roads are still preferred compared to unfamiliar roads with lower cost (Y12)” shows the scores for the five significant variables, with Y10 scoring the highest and Y12 scoring the second highest. This indicates that for most of the truck drivers surveyed, trust in road safety is the primary factor in choosing familiar roads, followed by cost, while traffic environment, road environment, and operational service quality are not essential.(6)
The abovementioned analysis results show that the road loyalty evaluation model established in this thesis can analyze the influence of different factors on truck drivers’ route choice. The analysis of “emotional value” and other related indicators verifies that emotional factors play an important role in truckers’ route selection. In particular, the results of the large differences in the scores of “loyalty on familiar roads (YLOYA)” and “satisfaction with familiar roads (YSATI)” are consistent with Tao and Zhang study [6] on travelers’ attitudes toward public transportation choice. The results of the study on travelers’ attitudes toward public transportation choice are consistent with the finding that truckers are not satisfied with their road use experience and are not allowed to repeat their visits under certain constraints. This reflects that although China has developed rapidly in terms of road mileage in recent years, there is still a long way to go in terms of improving the level of road services, especially for truckers’ experience of using them. Therefore, the future improvement of the regional road network in China should not only be based on the traffic volume survey data but also on the psychological and road usage experience of road users, such as truck drivers, to understand their real feelings of using the road in order to truly do a good job of road network planning. In addition, according to the scores of relevant significant variables, “frequency of experiencing severe congestion (X2),” “route driving comfort (X4),” “road driving comfort (X5),” and several other variables have low scores. Therefore, it is suggested that road operation and management units should take targeted measures to reduce the frequency of severe road congestion, improve the comfort of route driving through methods such as the improvement of signage, and improve the comfort of road driving on old roads by strengthening the maintenance of road surfaces.


## 5. Conclusions

Based on structural equation modeling and a truck driver questionnaire, this paper establishes a method for evaluating truck drivers’ loyalty to familiar roads. The method takes into account the influence of truck drivers’ “emotional value” factors on the outcome variables to improve the comprehensiveness of the model variables and to help better analyze the inner mechanism of truck drivers’ choice of transportation routes. The overall findings of the study are as follows:(1)
“Emotional value” is indeed an important factor influencing “road loyalty,” which indicates that in addition to the technical condition and service level of the road itself, the long-term accumulation of truck drivers’ emotional experience during the use of a road has an impact on the choice of transportation routes. Therefore, when planning a regional road network, a survey on emotions and reasons for road use should be conducted for traffic participants in the region to understand the real reasons why they frequently use the roads in the region.(2)
“Road loyalty” cannot be replaced by “road satisfaction”; the latter is the intuitive feeling of truck drivers in the process of road use, while the former is a choice that must be made. Regardless of the use of the experience, the choice does not mean satisfaction, and truck drivers may choose a long-term transport route as a “helpless” move. Therefore, the high volume of road traffic does not mean that users have a better experience of using the road, or it may be simply because there is no other road to take. Therefore, the road operation and management units, if we want to really improve the road experience, should be aimed at road users, especially truck drivers, to carry out extensive research by understanding the voice of road users to get the road service level to improve the views and suggestions.(3)
The truck drivers’ loyalty to the familiar road, carrying a greater degree from the familiar road reliability trust, is mainly attributed to the “road safety” and “fee cost” of trust. This indicates that saving time and money costs under the premise of ensuring safety are the primary consideration of truckers in choosing transportation routes. Therefore, in China, improving road safety and reducing toll costs are the two effective ways for toll road operators to attract more traffic, especially trucks, to improve the efficiency of toll roads.(4)
As for other results, traffic environment, road environment, value-added services, etc., were found to have an impact on truck driver loyalty, which also indicates that the quality of all services perceived by truck drivers while driving has an impact on their route choice. In addition, the level of road service should be considered in terms of “overall recognition of road safety,” “proportion of high-risk roads,” “cost of travel time,” “cost of fuel and tolls,” and “other monetary costs.” We can provide some policy suggestions to the traffic department and road operation and management units, such as strengthening safety management to reduce the occurrence of traffic accidents along the route and special treatment of frequently congested road sections to reduce the frequency of congestion.(5)
Finally, this paper focuses on the road freight sector and uses truck drivers as the respondents, but as operating vehicle drivers, truck drivers’ affective factors are affected to some extent by their professional habits and operational benefits, such as the significant effect of cost mentioned in finding (3); however, we do not know whether this finding holds for small car drivers. Therefore, as the next step, we will analyze whether the general drivers’ loyalty to familiar roads is the same as the conclusion obtained in this paper and compare and analyze the analysis results of general drivers and professional drivers to further discuss the effects of affective factors on different groups of drivers in route selection in order to provide more scientific research support for road network planning.



---

*Source: 6045467-2023-02-07.xml*