Prediction of Resident Travel Modes Based on XGBoost and Analysis of Behavioral Interaction Characteristics

Abstract

Abstract Figuring out the characteristics of urban residents' travel mode choices is the key to the forecasting of residents' travel demand as well as an important basis for transportation system management and planning. The integrated learning model based on the Boosting framework has high prediction accuracy and strong feature selection and combination ability and has become the preferred algorithm for building travel demand prediction models.In this article, the authors use the resident travel survey data of Kunming City, choose four integrated learning classifiers, XGBoost, LightGBM, CatBoost, and GBDT, to predict the travel mode of the residents, select the best parameters of the model by using grid search and five-fold cross-validation, analyze the importance of the features of the prediction model by using TreeSHAP, and finally explore the selection of travel modes under the interaction of important feature variables. The results of the study show that (1) the XGBoost model performs better than the other models, and the accuracy, precision, recall, and F1 value of the XGBoost model reach 90%, respectively, and the prediction accuracy of the four modes of travel, namely walking, two-wheeled electric motorcycle, public transportation, and car, reaches 94%, 90%, 85%, and 90%, respectively, and the corresponding AUC values reach 0.99, 0.97, 0.96, and 0.98, respectively. (2) Compared with household size and annual income, the actual distance of travel paths, ownership of cars and 2-wheeled electric motorcycles, age and gender of travelers, and the built environment are more important factors influencing the prediction of residents' travel choices. (3) The characteristics of travel mode choice under the interaction of several factors are obvious; except for the group over 55 years old, the ownership of travel means of transportation in the family significantly affects the choice of travel mode of residents; men between 20 and 55 years old have more medium-distance and long-distance trips, and they are the main group of people who use cars; when the travel distance is less than 15km, the 2-wheeled electric motorcycle and cars have a certain mutual substitution effect. In order to comprehensively promote the high-quality development of transportation, it is necessary to focus on the travel needs of women and the elderly while controlling the number of motor vehicles in the household, introducing policies to encourage the use of two-wheeled electric motorcycles, and improving the city's public transportation and commercial support facilities.