Neural Spatiotemporal Point Process for City-Scale Traffic Accident Modeling

McGill University, Sep. 2020 ~ Nov. 2020
Advisor: Prof. Lijun Sun
Co-worker: Yuankai Wu

• Defined the conditional probability functions of accident timing and location as nonlinear functions of the history, whose representation could be effectively learned by sequence-to-sequence networks.
• Formulated the conditional probabilities of future accident timing and location as log-normal mixture models with parameters conditioned on the historical representation.
• Experiments on real-world datasets from three cities confirmed NSTPP’s capacity to outperform deep learning and conventional point process in accident prediction

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