Complementary intermodal commuting and resident travel satisfaction: A nonlinear and interaction analysis

While many studies focus on single-mode satisfaction drivers, few examine how intermodal commuting affects commuter travel satisfaction and efficiency in urban settings. This study investigates how complementary intermodal commuting—where multiple modes of transportation are used in a single journey—affects travel satisfaction in Shanghai. Using an XGBoost model, we specifically explore how intermodal commuting and its interactions with contextual factors, like trip characteristics and sociodemographic features, influence travel satisfaction. Data was collected from the 2018 Shanghai Urban Resident Survey, with 1,052 respondents. The results show that while the number of commuting modes (NUMOD) and types of mode combinations (TYPMOD) affect satisfaction, commuter seamlessness (fewer delays and interruptions) and commute time (COMTIM) are the most influential factors. Nonlinear analyses show that active travel-dominated combinations (Type 7) are linked to higher satisfaction, while public transit-dominant combinations (Type 5) in China often correlate with lower satisfaction due to overcrowding. Private car-involved combinations (Types 1 and 3) mainly impact long-distance commutes. Nonlinear interaction analyses further reveal that the interaction between intermodal commuting and trip characteristics has an enhanced effect on commuter satisfaction. Overall, this research provides valuable insights into the complex dynamics between intermodal commuting and travel satisfaction, beyond traditional linear analyses. It also offers meaningful guidance for devising rational transportation policies under intermodal commuting conditions.