Domestic Tourism in COVID-19 Era: Travel Choice in Himalayas Correlates to Geographic Origin and Age

COVID-19 pandemic and subsequent has created recession in the tourism industry on the global scale impacting the livelihood of the millions of people worldwide. Speedy recovery of the tourism industry is essential to ensure the development progress do not retard drastically due to this pandemic. As the world is severely affected by the COVID-19 pandemic and international tourism will take quite a bit longer time to recover, domestic tourism could be a way forward for the recuperation of the industry. Therefore, this article aims to understand the potential of domestic tourism to keep the momentum of tourism development, after the pandemic situation gets over. Data on general characteristics of the respondents and their attitude towards travel after restrictions are over were collected using online survey. Descriptive and regression analyses were used to understand the relationship between travel decisions and respondents’ attributes. The travel decision was found significantly related to the age and geographic origin of the respondents indicating those who are less susceptible to infection are willing to travel sooner than other. Study indicates the expansion of tourism demand in relatively less popular destinations and diversified tourism products which might pose both the challenges and opportunities for tourism industry in post-COVID-19 situation. The findings of our study are expected to help in planning the post-pandemic recovery of the tourism industry in the country.

Travel Choice Behavior Model Based on Mental Accounting of Travel Time and Cost

This paper analyzes the utility calculation principle of travelers from the perspective of mental accounting and proposes a travel choice behavior model that considers travel time and cost (MA-TC model). Then, a questionnaire is designed to analyze the results of the travel choice under different decision-making scenarios. Model parameters are estimated using nonlinear regression, and the utility calculation principles are developed under different hypothetical scenarios. Then, new expressions for the utility function under deterministic and risky conditions are presented. For verification, the nonlinear correlation coefficient and hit rate are used to compare the proposed MA-TC model with the other two models: (1) the classical prospect theory with travel time and cost (PT-TC model) and (2) mental accounting based on the original hedonic editing criterion (MA-HE model). The results show that model parameters under deterministic and risky conditions are pretty different. In the deterministic case, travelers have similar sensitivity to the change in gain and loss of travel time and cost. The prediction accuracy of the MA-TC model is 3% lower than the PT-TC model and 6% higher than the MA-HE model. Under risky conditions, travelers are more sensitive to the change in loss than to the change in gain. Additionally, travelers tend to overestimate small probabilities and underestimate high probabilities when losing more than when gaining. The prediction accuracy of the MA-TC model is 2% higher than the PT-TC model and 6% higher than the MA-HE model.

Multi-Objective Optimization of Differentiated Urban Ring Road Bus Lines and Fares Based on Travelers’ Interactive Reinforcement Learning

This paper proposes a new multi-objective bi-level programming model for the ring road bus lines and fare design problems. The proposed model consists of two layers: the traffic management operator and travelers. In the upper level, we propose a multi-objective bus lines and fares optimization model in which the operator’s profit and travelers’ utility are set as objective functions. In the lower level, evolutionary multi agent model of travelers’ bounded rational reinforcement learning with social interaction is introduced. A solution algorithm for the multi-objective bi-level programming is developed on the basis of the equalization algorithm of OD matrix. A numerical example based on a real case was conducted to verify the proposed models and solution algorithm. The computational results indicated that travel choice models with different degrees of rationality significantly changed the optimization results of bus lines and the differentiated fares; furthermore, the multi-objective bi-level programming in this paper can generate the solution to reduce the maximum section flow, increase the profit, and reduce travelers’ generalized travel cost.

How More Options Decrease the Compromise Effect: Investigating Boundary Conditions for the Compromise Effect in Travel Decisions

Travelers often demonstrate the compromise effect—a tendency to choose the intermediate option(s) when facing difficult trade-off decisions. The compromise effect has been replicated in very specific settings where typically only two or three options were available. This research extends our understanding of the compromise effect by examining the impact of the number of options on travelers’ choices. Based on two different accounts (i.e., attribute distance account vs. decision complexity account), we predict that the compromise effect will be attenuated as the number of options in a choice set increases. Four experimental studies provide supporting evidence for this argument and support the attribute distance account as the main underlying mechanism. This research contributes to the extant tourism and travel choice literature by responding to the call to investigate the compromise effect in complex buying contexts.

Impacts of Real-Time Transit Information on Bus Passengers’ Travel Choices Based on Travel Behaviour Survey

Real-time transit information (RTI) service can provide travellers with information on public transport and guide them to arrange departure time and travel mode accordingly. This paper aims to analyse travellers’ choices under RTI by exploring the relationship between the related variables of RTI and passengers’ travel choice. Based on the stated preference (SP) survey data, the ordinal logistic regression model is established to analyse the changing probability of passengers’ travel behaviour under RTI. The model calculation results show that travellers getting off work are more likely to change their travel choice under RTI. When data from the control and experimental groups are compared, the differences in route selection are significant. Specifically, passengers with RTI have a more complex route selection than those without, including their changes of travel mode, departure time, vehicles, and stop choices. The research findings can provide insights into the optimisation of intelligent transit information systems and the strategy of RTI. Also, the analysis of passengers’ travel choice under RTI in the transit network can help to improve network planning.