On demand forecasting of demand-responsive paratransit services with prior reservations

The present study investigates the determinants of the volatility of passenger demand for para-transit services and explores the feasibility of a data-driven model for medium-term forecast of the daily demand. Medium-term demand forecasting is a significant insight to optimise resource allocation (staff and vehicles) and reduce operations costs. Using operational data from the reservation platform of the paratransit services in Toulouse, France, and enriching them with exogenous information, the study derives statistical and deep learning models for medium-term forecast. These models include a seasonal ARIMAX model with rolling forecast, a Random Forest Regressor, a LSTM neural network with exogenous information and a CNN neural network with independent variables. The seasonal ARIMAX model yields the best performance, suggesting that when linear relationships are considered, econometric models and deep learning models do not have significant differences in their performance. All the models show limited ability to grasp unique events with multi-day impacts such as strikes. Albeit a highly volatile demand and limited knowledge ahead of the forecast, these models suggest the volume of early reservations is a good proxy for the daily demand.