LISA: Learning-Integrated Space Partitioning Framework for Traffic Accident Forecasting on Heterogeneous Spatiotemporal Data

• URL: http://arxiv.org/abs/2412.15365v1
• Date: Thu, 19 Dec 2024 19:52:19 GMT
• Title: LISA: Learning-Integrated Space Partitioning Framework for Traffic Accident Forecasting on Heterogeneous Spatiotemporal Data
• Authors: Bang An, Xun Zhou, Amin Vahedian, Nick Street, Jinping Guan, Jun Luo,
• Abstract summary: Traffic accident forecasting is an important task for intelligent transportation management and emergency response systems. Existing data-driven methods fail to handle the heterogeneous accident patterns over space at different scales. We propose a novel Learning-Integrated Space Partition Framework (LISA) to simultaneously learn partitions while training models.
• Score: 14.726248469735971
• License:
• Abstract: Traffic accident forecasting is an important task for intelligent transportation management and emergency response systems. However, this problem is challenging due to the spatial heterogeneity of the environment. Existing data-driven methods mostly focus on studying homogeneous areas with limited size (e.g. a single urban area such as New York City) and fail to handle the heterogeneous accident patterns over space at different scales. Recent advances (e.g. spatial ensemble) utilize pre-defined space partitions and learn multiple models to improve prediction accuracy. However, external knowledge is required to define proper space partitions before training models and pre-defined partitions may not necessarily reduce the heterogeneity. To address this issue, we propose a novel Learning-Integrated Space Partition Framework (LISA) to simultaneously learn partitions while training models, where the partitioning process and learning process are integrated in a way that partitioning is guided explicitly by prediction accuracy rather than other factors. Experiments using real-world datasets, demonstrate that our work can capture underlying heterogeneous patterns in a self-guided way and substantially improve baseline networks by an average of 13.0%.

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