Forecast of Highway Passenger Volume in Spring Festival Travel Rush Based on System Dynamics Model

During the holidays represented by the Spring Festival, passenger stations and highway passenger transport enterprises are facing great pressure. Accurate prediction of highway passenger traffic during this period can make passenger stations and passenger transport enterprises carry out more reasonable capacity input and scheduling. Based on the principle of system dynamics, this paper constructs a forecasting model of highway passenger volume in the Spring Festival travel rush. Combined with the background of strong recovery of domestic tourism market, the influencing factors such as policy impact, residents’ demand, GDP, residents’ consumption level, and highway mileage are substituted into the forecasting model as quantitative indicators, which effectively avoids the problem that the traditional model only relies on historical data to analyze the linear relationship to predict highway passenger volume. Considering the nonlinear relationship among independent variables, a causal relationship diagram is constructed to analyze the causal feedback relationship, and the results are verified by comparing the historical data from 2019 to 2023 with the predicted values. The software Vensim is used to analyze and predict the total passenger traffic of the Spring Festival travel rush during the three scenarios of high, medium, and low economic growth in China from 2024 to 2026.