Supply-demand interactions across multimodal transportation networks

COVID-19 brought unprecedented changes to travel demand patterns in North America, shifting attention toward shared transportation modes and altering their levels of service. This highlights the need for post-COVID multimodal planning and understanding interactions between modes, for example, how changes in the supply of one mode influence the demand for others. This study addresses two key gaps: (1) limited research on supply–demand interactions across transportation modes, and (2) the challenge of analyzing pandemic data due to highly fluctuating demand. It examines how variations in the level of service affect travel demand across multiple modes while accounting for COVID-related fluctuations in ridership and heterogeneity across public transit service types.

The methodology focuses on the cross-elasticity of supply and demand, defined as the elasticity of public transit demand in response to changes in the level of service of surrounding modes. For example, how does the expansion of carsharing services affect public transit ridership? The analysis also accounts for service-type heterogeneity by estimating elasticities for a typology of bus lines to determine whether effects vary across service types. The study covers multiple modes in Montréal­­­ (bikesharing, free-floating and station-based carsharing, subway, bus, and private cars), using daily ridership and supply indicators from 2019 to 2023 within 500 m buffers around transit stops and stations.

To address fluctuating demand, a change-point detection algorithm identifies distinct ridership phases during and after COVID, incorporated into the model as exogenous dummy variables. The Autoregressive Integrated Moving Average (ARIMAX) model estimates elasticities over short-term (same-day), mid-term, and long-term periods. The model is developed at the route or station level, incorporating mode-specific supply variables (e.g., vehicle availability or bike availability hours), along with control variables including weather conditions, day type, and phase-specific dummies.

Preliminary results show distinct mode-specific patterns. Bus–subway interactions reveal a significant short-run complementarity role, particularly for frequent local routes. Bikesharing and carsharing exhibit both competitive and complementary relationships with bus ridership, depending on service density, service area, and route typology. These results highlight the importance of considering temporal dynamics and service typologies in multimodal planning, offering actionable insights for integrated post-pandemic mobility strategies.