Tourists are confronted with congestion caused by concentrated travel during public holidays. In order to guide tourists to make voluntary changes regarding their travel times during holidays, this paper focuses on exploring holiday rush-hour avoidance travel behavior (HRATB) considering psychological factors. First, based on the theory of planned behavior, the effects of psychological factors including attitude, subjective norm, and perceived behavior control on holiday avoidance travel intention and behavior were quantitatively analyzed by the structural equation model. Second, according to those three subjective psychological factors and the three objective factors of age, monthly income, and tourist group, the segmentation method of the latent class model was adopted to explore tourists’ preferences with regard to HRATB. Finally, an empirical analysis was carried out through questionnaire data. The results show that attitude, subjective norm, and perceived behavior control have significant impacts on intention and behavior with regard to holiday avoidance travel. There are significant differences in psychological observation variables such as rush-hour avoidance travel intention, attitude and subjective norm among the four segments of tourists, and cost sensitivity. In addition, this paper puts forward some countermeasures and suggestions for the four types of tourists. Conclusions provide a theoretical basis for formulating travel measures to attract different types of tourists.

Short-term prediction of holiday travel demand is a complex but key issue to the planning and management of tour transportation system in big cities. This paper develops an improved spatial and temporal correlation long short-term memory model (STC-LSTM) to forecast short-term holiday travel demand based on deep learning approach. Analysis results show six kinds of tourist flow correlations appears in different sets of tourist attractions, and 27.94 percent of the tourist attractions have mid- or high- positive tourist flow correlation with others, meaning that a positive synchronization mechanism exists between suburban tourist attractions in Beijing. The proposed model predicts the holiday travel demand on the basis of the historical data of the spatial and temporal related tourist flows, and the auxiliary data including meteorological data, temporal data, and Internet search index. Based on actual case study with tourist flow data of the suburban tourist attraction in Beijing, the proposed STC-LSTM is carried out to compared with other conventional prediction approaches. Results show that the proposed approach can improve the prediction accuracy and well capture the different spatial and temporal correlations of tourist flows.

An effective way to optimize traffic structures is by changing travel costs, thereby moving travelers from private transportation to public transportation. However, according to the existing studies, the traveler will not transfer from one mode to another unless the change in travel utility is greater than the indifference threshold. Therefore, the “indifference threshold” is one of the most important factors influencing a traveler’s choice of behavior. This study defines the “indifference threshold” as the traveler’s sensitivity to changes in travel utilities. In the framework of the theory of planned behavior (TPB), a structural equation model (SEM) considering the indifference threshold is established to analyze a traveler’s mode choice behavior. The analysis results showed that a travelers’ sensitivity to changes in travel utilities has the greatest impact on mode-choice behavior intentions and mode choice behavior. Perceptual behavior control has the strongest influence on travel choice behavior. In addition, in order to further explore the heterogeneity of a traveler’s behavior, the travelers were subdivided into four types, by establishing a latent class model (LCM) considering the indifference threshold. Finally, different traffic management suggestions are proposed for different types of travelers.

Mobility-as-a-Service (MaaS) system becomes a significant part of urban transportation network system based on the information and communication technologies, but rarely received attention in tourism transport. This paper explored the impact of travel mode preferences on habitual choice behavior for MaaS in tourism, and an online survey and stated preference experiments were designed and carried out. Respondents were divided into three categories including strictly preference (SP) group, weak preference (WP) group, and no preference (NP) group, accounting for 25.09%, 72.24%, and 2.67% respectively. Estimation results of the mode choice random parameter logit (RPL) models indicate that explanatory variables of the alternatives (e.g., the total travel time, in-vehicle cost, and walking distance) only affect the travel mode choices for WP group, which are insensitive to the SP group and NP group. Marginal effect analysis shows that mode choice preferences for MaaS are influenced as tour experience and daily travel habits change.

The adjustment of road toll is an important measure that can alleviate road traffic congestion by convincing car travelers to travel during off-peak times. In order to reduce congestion on the expressway on the first day of a holiday, factors that affect the departure times of holiday travelers must be comprehensively understood to determine the best strategy to persuade car travelers to avoid peak travel times. This paper takes holiday car travelers as the research object and explores the characteristics and rules of departure time choice behavior for different holiday lengths. Based on Utility Maximization Theory, a multinomial logit (MNL) model of departure time choice for a three-day short holiday and a seven-day long holiday was established. Model calibration and elastic analysis were carried out using Revealed Preference/Stated Preference (RP/SP) survey data. Additionally, the influence of the highway toll policy on departure times for long and short holidays was analyzed. The results show that the rate of first-day departures is much higher than that of other departure times for both short and long vacations under the current policy of free holiday passage on highways. Factors such as trip duration, size of the tourist group, the number of visits, travel range, travel time, monthly income, occupation, age and road toll have a significant influence on the departure time decisions of holiday car travelers, and the effect and degree of influence are markedly different for different holiday lengths. The effects of tolls for each departure time and different pricing scenarios on the choice behavior of travelers are different between long and short holidays. Furthermore, the effectiveness of the road toll policy also varies for travelers with different travel distances. This study can provide useful information for the guidance of holiday travelers, the management of holiday tolls on expressways and the formulation of holiday leave time.

This paper aims to deeply analyze the regularity of residents’ daily activity-travel behaviors to help traffic management departments predict travel demand and evaluate transportation policies. Taking time use data in American as empirical analysis, the influencing factors such as residents’ activity pattern, duration and competition were discussed and analyzed based on the competing risk model. Results can be concluded that: a) people feel happy and meaningful during most of the activities, while they feel sad or pain in relatively rare occasions; b) there is no significant difference between young people and the elderly as for the total number of activities during one day; c) estimation results show that personal, household and activity characteristics have significant influence on activity duration and pattern, and the pattern of last activity (N) has a great influence on the occurrence of next activity (N+1); d) the competition between residents’ daily activities is confirmed based on the competing risk model, and travel is closely related to various activities and can be seen as the derived demand of various activities. In all combinations of activity-travel chain schedule, personal — household (30.0%), household — recreation (25.9%), work — travel (45.2%), purchase — travel (33.7%), recreation — travel (22.5%), volunteer — travel (55.3%) and travel — purchase (28.4%) have the highest proportion respectively.