  The rapid increase in the cyber-physical nature of transportation,
availability of GPS data, mobile applications, and effective communication
technologies have led to the emergence of On-Demand Transit (ODT) systems. In
September 2018, the City of Belleville in Canada started an on-demand public
transit pilot project, where the late-night fixed-route (RT 11) was substituted
with the ODT providing a real-time ride-hailing service. We present an in-depth
analysis of the spatio-temporal demand and supply, level of service, and origin
and destination patterns of Belleville ODT users, based on the data collected
from September 2018 till May 2019. The independent and combined effects of the
demographic characteristics (population density, working-age, and median
income) on the ODT trip production and attraction levels were studied using GIS
and the K-means machine learning clustering algorithm. The results indicate
that ODT trips demand is highest for 11:00 pm-11:45 pm during the weekdays and
8:00 pm-8:30 pm during the weekends. We expect this to be the result of users
returning home from work or shopping. Results showed that 39% of the trips were
found to have a waiting time of smaller than 15 minutes, while 28% of trips had
a waiting time of 15-30 minutes. The dissemination areas with higher population
density, lower median income, or higher working-age percentages tend to have
higher ODT trip attraction levels, except for the dissemination areas that have
highly attractive places like commercial areas.
