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Machine Learning Based Demand Modelling for On-Demand Transit Services: A Case Study of Belleville, Ontario

The use of mobile applications apps and GPS service on smartphones for transportation management applications has enabled the new "on-demand mobility" service, where the transportation supply is following the users' schedule and routes. In September 2018, the City of Belleville in Canada and Pantonium operationalized the same idea, but for the public transit service in the city to develop an on-demand transit (ODT) service. An existing fixed route (RT 11) public transit service was converted into an on-demand service during the night as a pilot project to maintain a higher demand sensitivity and highest operation cost efficiency per trip. In this study, Random Forest (RF), Bagging, Artificial Neural Network (ANN), and Deep Neural Network (DNN) machine learning algorithms were adopted to develop a pickup demand model (trip generation) and a trip demand model (trip distribution model) for Belleville ODT service based on the dissemination areas' demographic characteristics and the existing trip characteristics. The developed models aim to explain the demand behavior, investigate the main factors affecting the trip pattern and their relative importance, and to predict the number of generated trips from any dissemination area as well as between any two dissemination areas. The results indicate that the developed models can predict 63% and 70% of the pickup and trip demand levels, respectively. Both models are most affected by the month of the year and the day of the week variables. In addition, the population density has a higher impact on the ODT service pickup demand levels than the other demographic characteristics followed by the working age percentages and median income characteristics. Whereas, the distribution of the trips depends on the demographic characteristics of the destination area more than the origin area.