# Study on Peak Travel Avoidance Behavior of Car Travelers during Holidays

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Methodology

#### 3.1. Framework of NL Model

#### 3.2. Estimation Method of NL Model

_{mn}is the utility of the traveler n selecting scheme m; V

_{mn}is a fixed term in the utility function of the explanatory variables associated with traveler n and alternative m; ε

_{mn}is a random component in the utility function, which captures all other factors unobserved by the researcher; A

_{n}is the set of selection schemes.

_{(r/m)n}and V

_{mn}of traveler n are both linear, the expressions of V

_{(r/m)n}and V

_{mn}are obtained, which are shown in Equations (2) and (3), respectively.

^{2}and adjusted goodness of fit ρ

^{−2}are commonly used to measure the overall fit of the model. When ρ

^{2}reaches 0.2–0.4, the model has high accuracy [56].

## 4. Data and Modeling

#### 4.1. Sample Description

#### 4.2. Model Specification

## 5. Results and Discussion

#### 5.1. Estimation Results of NL Model

^{2}of the lower and upper levels of the NL model are 0.213 and 0.277, respectively, which meet the requirements of accuracy, indicating a strong ability to explain tourists’ decision-making behavior [58]. Moreover, the inclusion coefficient λ

_{2}is the parameter estimation value [59], which reflects the rationality of the NL model structure, and λ

_{2}means that the model structure is reasonable between 0 and 1. The estimate of the inclusion coefficient λ

_{2}in this model is 0.273, which indicates a clear hierarchy between the upper and lower layers of the NL model, and the selected tree structure is reasonable.

- (1)
- Decision-making behavior analysis of travel destination of the lower model

_{1}of occupation is positive, indicating that administrative personnel and workers are more inclined to go to their originally planned tourist attractions, which may be due to the limited travel time of this group, and their travel destinations are mostly planned and not easy to change due to time constraints.

_{2}of the tourist group is positive, indicating that the greater the number of tourists in a group, the more inclined they are to visit the original tourist attractions. This may be because a larger group size requires more planning, making it more difficult to change the destination. The estimated coefficient β

_{3}of the relationship with one’s tourist group is negative, indicating that tourists traveling with family and friends are less inclined to change tourist attractions, which may be because the travel plan of a group is not easy to change. The estimated coefficient β

_{4}of tour duration time is positive, indicating that with more days of travel, tourists are more inclined to change tourist attractions. The estimated coefficient β

_{5}of the number of visits is negative, indicating that with the increase in the number of visits to the original tourist attraction, tourists are more inclined to change tourist attractions. The estimated coefficient β

_{6}of tourism motivation is positive, which indicates that the tourists with leisure and sightseeing motives are more inclined to continue to the original planned tourist attraction. The estimated coefficient β

_{7}is positive, indicating that tourists who visit tourist attractions for less than 4 h are more inclined to change tourist attractions.

_{8}of tourist attraction type is positive, indicating that tourists who plan to visit tourist attractions are more inclined to change tourist attractions. The estimated coefficient β

_{9}of tourist attraction grade is positive, indicating that when the tourist attraction tourists planned to visit is 3A or above, they are more inclined to continue to that original tourist attraction. The estimated coefficient β

_{10}of tourist attraction ticket price is positive, indicating that tourists are more inclined to go to the original tourist attraction if the ticket price is less than 100 yuan. The estimated coefficient β

_{11}of the crowding degree of tourist attractions was negative, indicating that the more crowded the planned site is, the more inclined tourists are to change sites.

_{12}of road congestion around tourist attractions is positive, indicating that tourists are more inclined to change tourist attractions as the congestion duration increases. The estimated coefficient β

_{13}of holiday admission ticket prices is positive, indicating that tourists are more inclined to change tourist attractions if the tourist attractions they planned to visit implement a holiday admission ticket price increase strategy.

- (2)
- Decision-making behavior analysis of travel time of upper model

_{1}of age is positive, indicating that tourists aged between 18 and 44 are more inclined to travel during holidays, which may be because these people mostly go to work or school and have time off during holidays. The estimated coefficient θ

_{2}of family resident population is positive, indicating that the tourist groups of 1–2 people are more inclined to go on non-holiday trips. This may be because families with more permanent residents have more people to consider and their time is mutually influenced and restricted, so most of them can only go on holiday trips.

_{3}of tour duration time is positive, indicating that the more days of travel, the more tourists tend to travel in non-holidays.

_{6}of the crowding degree of tourist attractions is negative, indicating that the more crowded tourist attractions are during a holiday, the less inclined tourists are to visit it during the holiday.

_{4}is positive, which indicates that the tourists who travel in the city and its suburbs are more inclined to travel during holidays. The estimated coefficient θ

_{5}is positive, indicating that the tourists whose last trip time was during the holiday are more inclined to travel during the holiday.

_{7}of road congestion around tourist attractions is positive, indicating that tourists are more inclined to travel during non-holidays given the increase in road congestion duration during holidays. The estimated coefficient θ

_{8}of holiday admission ticket prices is negative, which indicates that tourists are less inclined to travel during holidays if the tourist attractions planned to go to implement a holiday admission ticket price increase strategy. The non-holiday admission ticket coefficient θ

_{9}is positive, indicating that tourists are more inclined to travel during non-holidays if the tourist attraction tickets are discounted.

#### 5.2. Sensitivity Analysis of Variables

_{13}is positive, meaning that the longer congestion time, the more tourists tend to change destinations. The elastic value analysis results in Figure 5a show that with the increase in road congestion duration around the original tourist attraction, the probability of choosing to visit a different tourist attraction keeps increasing, as does the elastic value. When the congestion duration is more than 1 h, the elastic value is greater than 1, and the tourists are elastic under the influence of road congestion duration. This shows that the change in congestion duration has a great influence on tourists’ destination decisions. When the congestion time increases from half an hour, the probability of changing tourist attractions increases by 6.23%, 11.13%, and 15.12%, respectively. It can be seen that the duration of road congestion around the original tourist attractions is an important factor influencing tourists’ choice of travel destination. Therefore, the detailed and accurate release of travel congestion information can help tourists make destination decisions before traveling.

_{7}is positive, indicating that the change in road congestion duration around the etourist attraction positively affects the probability of tourists carrying out non-holiday travel; that is, the longer the congestion duration, the more inclined tourists are to choose non-holiday travel. According to the elasticity analysis of Figure 5b, as the congestion duration of roads around original tourist attractions keeps increasing, the probability of choosing non-holiday travel keeps increasing, indicating that road congestion will affect tourists’ choice of non-congested travel times. An elasticity value between 0 and 1, however, shows that the congestion time weakly influences tourists’ decision to travel during the holiday season, perhaps because many tourists travel under strict time constraints, and their available travel time may only be during holidays. Thus, they may still choose to travel during peak times knowing they could face road congestion due to time limits or psychological factors. Especially for the tourist attractions that they have not been to, the desire and enthusiasm for tourism often give tourists a better expectation of road congestion. Therefore, the duration of road congestion around the tourist attraction is not the main factor affecting the decision of travel time.

_{12}is positive, indicating that when attractions increase holiday admission ticket prices, tourists are more inclined to visit a different tourist attraction. The analysis results of the elasticity value in Figure 7a show that with the rising holiday admission ticket prices of the original tourist attractions, the probability of choosing to replace the tourist attractions is constantly increasing, and the elasticity value is constantly increasing. When the proportion of the ticket price increase is less than 40%, the probability of tourists changing destinations is less affected, and within this scope, tourists are not sensitive to the increase in the ticket price. When the price increase is more than 40%, the elastic value E is greater than 1. In this case, the change in tourists affected by the ticket price increase is flexible, indicating that the holiday admission ticket price increase for the original tourist attractions has a significant impact on tourists’ travel destinations. From the full price to a 40% price increase, the probability of changing the tourist attractions increases from 48.77% to 69.11%, an increase of 20.34%. It can be seen that the implementation of ticket price increase strategies for crowded tourist attractions during holidays has a strong impact on tourists altering their travel destinations, and the spatial behavior of tourists at different peaks is more sensitive.

_{8}is negative, indicating that the ticket price of the original tourist attractions increases during holidays, and tourists are not inclined to travel on holidays. From the analysis results of Figure 7b, it can be concluded that with the increasing price of holiday admission tickets in the original tourist attractions, the probability of choosing holiday travel is constantly decreasing. The elasticity value E is negative. When the price increase is 80%, the absolute value of the elasticity value E is greater than 1. In this case, the change in tourists affected by the ticket price increase is flexible, indicating that the holiday admission ticket price increase for the original tourist attractions has a significant impact on tourists’ travel time. This shows that when making a decision on travel time, the ticket price increase for tourist attractions has a weaker impact on tourists, and tourists are more sensitive when the price increase is large. Therefore, the high ticket price increase strategy can enhance the willingness of tourists to change their travel time.

_{9}of non-holiday admission ticket prices is positive, indicating that the lower the ticket price that tourists are willing to pay during non-holiday times, the greater the ticket discount for tourist attractions, and the more tourists prefer to choose non-holiday travel. According to the analysis of the elastic value of Figure 5 and Figure 6, for every 20% decrease in the ticket discount, the probability of tourists choosing non-holiday travel increases by 2.57%, 5.02%, 7.33%, 9.5%, and 12.52% compared to the full price. The ticket price ranges from full price to free, the elastic value is between 0 and 1, and the elasticity change is small. The probability of tourists choosing holiday travel changes slightly with the tourist attraction ticket discount; that is, tourists are not sensitive to the peak ticket discount. It can be seen that the effect of encouraging tourists to adjust their travel time through the strategy of non-holiday admission ticket discounts is not obvious, which may be due to the limitation of available travel time or the mutual constraints between travel partners; thus, the effect of a flat peak ticket discount may be more obvious for tourists with relatively free time.

## 6. Conclusions

- (1)
- According to the calibration results of the NL model, occupation, age, family resident population, the size of the tourist group and the relationship with the group, tour duration time, number of visits, tourism motivation, travel range, tourist attraction type, tourist attraction grade, crowding degree, traffic congestion around tourist attractions, holiday admission ticket prices, and non-holiday admission ticket prices significantly impact tourists’ travel time and destination decisions.

- (2)
- The results of the elasticity analysis show that travel time and destination decision behavior are elastic to the number of visits, the degree of road congestion around the original tourist attraction, and holiday admission ticket prices.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 5.**Probability and point elasticity of tour duration time. (

**a**) Relationship with tourist destination. (

**b**) Relationship with travel time.

**Figure 7.**Probability and point elasticity of tour duration time. (

**a**) Relationship with tourist destination. (

**b**) Relationship with travel time.

Variable Category | Variable | Parameter Setting and Definition |
---|---|---|

Socioeconomic characteristics | Occupation | 1 = staff and worker; 0 = teacher, student, retired/unemployed, freelance |

Age (year) | 1 = 18–44; 0 = 45–65 | |

Family resident population | 1 = 1 and 2 people; 0 = 3 and more | |

Travel characteristics | Travel range | 1 = in the city and suburbs; 0 = cross-city |

Last trip time | 1 = during holidays; 0 = before holidays | |

Tourism characteristics | Tour duration time | divided into 1–7 days and above, value is 1–7, taking the actual value |

Tourist group | divided into 1–7 persons and above, value is 1–7, taking the actual value | |

Travel companion | 1 = family, friends/colleagues/classmates; 0 = alone and other | |

Tourism motivation | 1 = leisure, relaxation, and sightseeing attractions 0 = expand knowledge, enjoy delicious food and shopping, spend time with family | |

Tourist attraction characteristics | The number of visits | taking the actual value, 1 = the first tour, 2 = 1–2, 3 = 3–4, 4 = 5 times and more |

Tourist attraction tour time | 1 = within 4 h; 0 = 4 h or more | |

Original tourist attraction type | 1 = culture and nature; 0 = outdoor and entertainment | |

Original tourist attraction grade | 1 = 3A and above; 0 = 2A and below and no grades | |

Original attractions ticket price | 1 = RMB 100 or less; 0 = more than 100 yuan | |

Crowding degree of the original tourist attraction | divided into 1–5 grades, the crowding degree increases step by step, and the value is 1–5 | |

External conditions | Road congestion around the tourist attractions | divided into 0.5 h and 1 h, with values of 1 and 2 |

Holiday attraction ticket price | divided into 20% increase and 40% increase, with values of 1 and 2 | |

Non-holiday attraction ticket price | divided into 40% off and free, with values of 1 and 2 |

Upper Level (Travel Time Choice) | ||||||||
---|---|---|---|---|---|---|---|---|

Alternative sets | Select results | Age | Family resident population | Tour duration time | Travel range | Last trip time | Crowding degree | |

Upper level | Lower level | Xm_{1} | Xm_{2} | Xm_{3} | Xm_{4} | Xm_{5} | Xm_{6} | |

Holiday | Original tourist attraction | M_{11n} | 1 | 0 | 0 | 1 | 1 | 1 |

Change tourist attraction | M_{21n} | |||||||

Non-holiday | M_{2n} | 0 | 1 | 1 | 0 | 0 | 0 | |

Unknown parameter | λ_{2} | θ_{1} | θ_{2} | θ_{3} | θ_{4} | θ_{5} | θ_{6} | |

Upper Level (Travel Time Choice) | Lower Level (Destination Choice) | |||||||

Road congestion | Holiday attractions ticket price | Non-holiday attractions ticket price | Occupation | Tourist group | Relationship | Tour duration time | The number of visits | |

Xm_{7} | Xm_{8} | Xm_{9} | X(r|m)_{1} | X(r|m)_{2} | X(r|m)_{3} | X(r|m)_{4} | X(r|m)_{5} | |

0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | |

0 | 0 | 1 | 1 | 0 | ||||

1 | 0 | 1 | ||||||

θ_{7} | θ_{8} | θ_{9} | β_{1} | β_{2} | β_{3} | β_{4} | β_{5} | |

Lower Level (Destination Choice) | ||||||||

Tourism motivation | Tourist attraction tour time | Tourist attraction type | Tourist attraction grade | Attractions ticket price | Crowding degree | Road congestion | Holiday attractions ticket price | |

X(r|m)_{6} | X(r|m)_{7} | X(r|m)_{8} | X(r|m)_{9} | X(r|m)_{10} | X(r|m)_{11} | X(r|m)_{12} | X(r|m)_{13} | |

1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | |

0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | |

β_{6} | β_{7} | β_{8} | β_{9} | β_{10} | β_{11} | β_{12} | β_{13} |

Level | Variable | Estimate | Std. Err. | t Test | |
---|---|---|---|---|---|

Lower Level | Inherent dummy | ASC | 2.09 | 0.591 | 3.54 *** |

Occupation | X(r|m)_{1} | 0.449 | 0.159 | 2.83 *** | |

Tourist group | X(r|m)_{2} | 0.106 | 0.0484 | 2.19 ** | |

Relationship with tourist group | X(r|m)_{3} | −1.47 | 0.321 | −4.59 *** | |

Tour duration time | X(r|m)_{4} | 0.895 | 0.162 | 5.53 *** | |

The number of visits | X(r|m)_{5} | −1.64 | 0.224 | −7.35 *** | |

Tourism motivation | X(r|m)_{6} | 0.782 | 0.188 | 4.17 *** | |

Tourist attraction tour time | X(r|m)_{7} | 0.344 | 0.162 | 2.13 ** | |

Tourist attraction type | X(r|m)_{8} | 0.599 | 0.203 | 2.95 *** | |

Tourist attraction grade | X(r|m)_{9} | 0.343 | 0.177 | 1.94 ** | |

Attractions ticket price | X(r|m)_{10} | 0.422 | 0.185 | 2.28 ** | |

Crowding degree of spot | X(r|m)_{11} | −0.47 | 0.0809 | −5.82 *** | |

Road congestion | X(r|m)_{12} | 0.828 | 0.136 | 6.08 *** | |

Holiday attractions ticket price | X(r|m)_{13} | 0.556 | 0.135 | 4.11 *** | |

Model statistics: L(0): −813.755; L($\widehat{\theta}$): −652.612; −2(L(0) − L($\widehat{\theta}$)): 322.286; ${\rho}^{2}$: 0.213; ${\rho}^{-2}$: 0.202 | |||||

Level | Variable | Estimate | Std. Err. | t Test | |

Upper Level | Inherent dummy | ASC | 2.85 | 0.595 | 4.79 *** |

Age | X(m)_{1} | 0.246 | 0.142 | 1.73 * | |

Family resident population | X(m)_{2} | 0.571 | 0.131 | 4.37 *** | |

Tour duration time | X(m)_{3} | 0.139 | 0.0675 | 2.06 ** | |

Travel range | X(m)_{4} | 0.354 | 0.145 | 3.24 *** | |

Last trip time | X(m)_{5} | 0.53 | 0.185 | 2.86 *** | |

Crowding degree of spot | X(m)_{6} | −0.131 | 0.0606 | −2.16 ** | |

Road congestion | X(m)_{7} | 0.241 | 0.12 | 2.01 ** | |

Holiday attractions ticket price | X(m)_{8} | −0.267 | 0.122 | −2.2 ** | |

Non-holiday attractions ticket price | X(m)_{9} | 0.449 | 0.122 | 3.68 *** | |

Inclusive coefficients ${\lambda}_{2}$ | X(m)_{10} | 0.273 | 0.069 | 3.96 *** | |

Model statistics: L(0): −966.940; L($\widehat{\theta}$): −699.437; −2(L(0) − L($\widehat{\theta}$)): 535.007; ${\rho}^{2}$: 0.277; ${\rho}^{-2}$: 0.263 |

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## Share and Cite

**MDPI and ACS Style**

Zhu, H.; Guan, H.; Han, Y.; Li, W.
Study on Peak Travel Avoidance Behavior of Car Travelers during Holidays. *Sustainability* **2022**, *14*, 10744.
https://doi.org/10.3390/su141710744

**AMA Style**

Zhu H, Guan H, Han Y, Li W.
Study on Peak Travel Avoidance Behavior of Car Travelers during Holidays. *Sustainability*. 2022; 14(17):10744.
https://doi.org/10.3390/su141710744

**Chicago/Turabian Style**

Zhu, Haiyan, Hongzhi Guan, Yan Han, and Wanying Li.
2022. "Study on Peak Travel Avoidance Behavior of Car Travelers during Holidays" *Sustainability* 14, no. 17: 10744.
https://doi.org/10.3390/su141710744