Residents’ Travel Mode Choice Behavior In Post-COVID-19 Era Considering Preference Differences

Against the backdrop of the global outbreak of the COVID-19,economic and social development has been hit hard,and the transportation sector has been severely affected.Urban public transportation has been hit hard,with a sharp decline in transport ridership and a dampening of travel behavior and willingness.With the effective control of the COVID-19 and the classification of the COVID-19 as "Category B",the national epidemic prevention and control has entered the post-COVID-19 era.Although urban public transportation is gradually recovering,the recovery of public transport ridership is slow and still below the pre-COVID-19 level.The public travel structure continues to change and urban road congestion is increasing,and other issues need to be addressed.In this paper,we study the travel behavior of the public in the post-COVID-19 era,and obtain data through social networks and questionnaires to investigate two aspects: first,the study of public travel willingness in the post-COVID-19 era;second,the study of travel mode choice behavior in the post-COVID-19 era considering individual differences.The aim is to provide data support when responding to public health emergencies and to provide reference for the development of urban public transportation in the post-COVID-19 era.A study of public travel willingness in the post-COVID-19 era.Based on social network data,natural language processing techniques were used to quantify the public travel intention in the post-COVID-19 era.Firstly,we collected microblog texts about travel in the COVID-19 through web crawler technology;then we analyzed the travel sentiment based on the plain Bayesian classification algorithm,and used Spearman correlation to analyze the relationship between public travel intention and urban public transport ridership;finally,we used LDA topic modeling to thematically study the microblog texts during the COVID-19 and post-COVID-19 era.The results showed that the mean values of effective travel sentiment during the epidemic and post-COVID-19 era were-0.8197 and-0.0640,respectively,and compared with the epidemic period,the public’s fear of infection in traveling in the post-COVID-19 era was significantly better but still existed.The public’s willingness to travel directly affects the transport ridership of urban public transportation,and in the post-COVID-19 era,travelers are more concerned about the level of infection control and travel time in their areas.A study of residents’ travel mode choice behavior in the post-COVID-19 era.Using the choice experiment method,we obtained the choice behavior data based on the questionnaire survey,and constructed a mixed Logit model and a latent class conditional Logit model of travel mode choice.Stata software was used to calibrate the model parameters,and the main factors influencing residents’ travel mode choices were obtained.The results show that both models reflect the heterogeneity of individual travel mode choices.Compared with the mixed Logit model,the latent class conditional Logit model is capable of improving the goodness of fit by 13% and increase the prediction accuracy by 3.03%,which provides an effective tool for analyzing individual heterogeneity of travel behavior under public health emergencies.The latent class conditional Logit model divides residents into four and five groups according to the two scenarios of low and medium risk areas.From the perspective of travel mode attributes,the waiting time and the traveling time are the most important influencing factors for residents to choose the travel modes.From the perspective of personal socio-economic attributes,women with higher income are more inclined to choose private cars to travel.The older are more sensitive to travel costs,and men are more willing to choose bus and subway travel.