  In recent years, with the advancements in information and communication
technology, different emerging on-demand shared mobility services have been
introduced as innovative solutions in the low-density areas, including
on-demand transit (ODT), mobility on-demand (MOD) transit, and crowdsourced
mobility services. However, due to their infancy, there is a strong need to
understand and model the demand for these services. In this study, we developed
trip production and distribution models for ODT services at Dissemination areas
(DA) level using four machine learning algorithms: Random Forest (RF), Bagging,
Artificial Neural Network (ANN) and Deep Neural Network (DNN). The data used in
the modelling process were acquired from Belleville's ODT operational data and
2016 census data. Bayesian optimalization approach was used to find the optimal
architecture of the adopted algorithms. Moreover, post-hoc model was employed
to interpret the predictions and examine the importance of the explanatory
variables. The results showed that the land-use type was the most important
variable in the trip production model. On the other hand, the demographic
characteristics of the trip destination were the most important variables in
the trip distribution model. Moreover, the results revealed that higher trip
distribution levels are expected between dissemination areas with
commercial/industrial land-use type and dissemination areas with high-density
residential land-use. Our findings suggest that the performance of ODT services
can be further enhanced by (a) locating idle vehicles in the neighbourhoods
with commercial/industrial land-use and (b) using the spatio-temporal demand
models obtained in this work to continuously update the operating fleet size.
