Addressing Sparsity in Paratransit Demand and Cancellation Prediction: A Spatiotemporal Kernel Approach

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As an emerging mode of public transport, paratransit provides accessible and affordable mobility services to the elder and the disabled. Given the high operation cost of paratransit services, it is critical to have an accurate and reliable estimation of future demand and cancellations, which can be fed into advanced optimization applications, such as vehicle routing, crew scheduling, and fleet management, to name but a few. However, unlike mass transit services and popular on-demand mobility, the demand for paratransit service is essentially sparse over space and time. This sparsity nature (count data) limits the application of traditional time series-based models and emerging tensor learning and deep learning model, which often makes Gaussian assumptions. The other challenge is to make reliable estimation with different spatiotemporal scales/resolutions, from coarse regional level to fine postal code level, from daily level to minute by minute. To address these two challenges, in this Engage project we plan to develop a new spatiotemporal modeling framework that integrates the kernel approach with Gaussian process Poisson regression models to predict sparse demand accurately and reliably. To estimate the cancellation rate, we will develop a generalized classification model based on the detailed trip features (e.g., time of day, travel time, occupancy), individual feature (e.g., age, income), and other external features (e.g., weather condition).Our group (Smart Transportation Laboratory) at McGill University will work on the historical (two years) paratransit booking/cancellation data provided by GIRO and the new models will be integrated into the HASTUS-OnDemand software. Given that the spatiotemporal prediction models are ubiquitous, we expect the research outcomes to also benefit other products of GIRO, such as public transport demand prediction and postal demand prediction.