To improve the mode share of public transport and reduce the transition to private transport of passengers waiting at bus station, the mechanism of passengers’ decision-making procedure and influence factors of the travel mode choice were analyzed. Some latent variables such as safety, comfort, convenience, flexibility and economy were selected to reflect the satisfaction degree of passengers on the service level of public transport. Taking Jinan City as an example, the questionnaire of passengers’ travel choice behavior at bus station was designed and carried out. Based on the structure equation model (SEM), the relationship between the satisfaction degree and some latent variables such as safety and comfort was discussed. The SEM method analysis shows that, of the influence level of the latent variables to the service level of public transport, flexibility is the most significant variable affecting passenger’s satisfaction degree followed by safety, convenience, comfort and economy. Travel mode choice model of passengers waiting at bus station was established with an integration approach of SEM and nested logit (NL) model. The SEM-NL integration model results reveal that gender, monthly income, purpose of the trip, travel distance, safety and convenience service level have a significant effect on the choice of the upper model (public transport or private transport). Passenger’s age, vehicle ownership and bus ride frequency have great influence on the choice of the lower mode (ORB: original route bus; ARB: alternative route bus; Taxi; and Shared bike). Sensitivity analysis reveals that the transition probabilities from private transport to public transport can reach the highest point (respectively, 69.85%, 68.84% and 35.51%) when safety service reaches level 4, convenience service reaches level 3, or comfort service reaches level 2, indicating that the safety level equal to 4, convenience level equal to 3 and comfort level equal to 2 are the key threshold to increase the public transport mode share. Some proposals such as ensuring good accessibility of public transport, shortening the transfer distance of different routes, creating a comfortable travel environment and integrating bus ticket system have been put forward for the sustainable development of public transport system.

The question prompted is how to design the tour route to make the tourists get the maximum satisfactions considering the tourists’ demand. The influence factors of the tour route choices of tourists were analyzed and tourists’ behavior characteristics and psychological preferences were regarded as the important influence factors based on the tourist behavioral theories. A questionnaire of tourists’ tour route information and satisfaction degree was carried out. Some information about the scene spot and tourists demand and tour behaviors characteristic such as visit frequency, number of attractions visited was obtained and analyzed. Based on the convey datum, tour routes multiobjective optimization functions were prompted for the tour route design regarding the maximum satisfaction and the minimum tour distance as the optimal objective. The available routes are listed and categorized. Based on the particle swarm optimization model, the priorities of the tour route are calculated and finally the suggestion depth tour route and quick route tour routes are given considering the different tour demands of tourists. The results can offer constructive suggestions on how to design tour routes on the part of tourism enterprises and how to choose a proper tour route on the part 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.

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.

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.

Introducing private capital into the public transport system for its sustainable development has been increasing around the world. However, previous research ignores emissions and energy consumption impacts, which are important for private capital investment policy-making. To address this problem, the system dynamic (SD) approach was used to quantitatively analyze the cumulative effects of different private capital investment models in public transport from the environmental perspective. The SD model validity was verified in the case study of Jinan public traffic. Simulation results show that the fuel consumption and emission reductions are obvious when the private capital considering passenger value invests in public transport compared with the no private capital investment and traditional investment models. There are obvious cumulative reductions for fuel consumption, CO₂, CO, SO₂, and PM10 emissions for 100 months compared with no private capital investment. This research verifies the superiority of the passenger value investment model in public transport from the environmental point of view, and supplies a theoretical tool for administrators to evaluate the private capital investment effects systematically.