Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference
Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference
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DOI：
10.1609/aaai.v30i1.10011
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发表时间：
2016-02
期刊：
影响因子：
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通讯作者：
Quanjun Chen;Xuan Song;Harutoshi Yamada;R. Shibasaki
中科院分区：
文献类型：
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作者：
Quanjun Chen;Xuan Song;Harutoshi Yamada;R. Shibasaki
随着城市化和公共交通系统的快速发展，交通事故的数量在过去几十年中在全球范围内显著增加，成为人类社会的一个大问题。面对这些可能发生的突发交通事故，了解交通事故的原因，并对可能发生的交通事故进行预警，对于制定有效的交通管理措施具有重要意义。然而，由于缺乏支持的传感数据，研究是非常有限的领域，实时更新交通事故风险。因此，在本文中，我们收集了大而异构的数据（7个月的交通事故数据和160万用户的GPS记录）来了解人类移动性如何影响交通事故风险。通过挖掘这些数据，我们开发了一个堆栈去噪自动编码器的深度模型，以学习人类移动的分层特征表示。并将这些特征用于交通事故风险等级的有效预测。一旦模型经过训练，我们的模型可以模拟相应的交通事故风险地图与给定的实时输入的人的流动性。实验结果表明，我们的模型的效率，并表明，交通事故的风险可以显着更可预测的，通过人的流动性。
With the rapid development of urbanization and public transportation system, the number of traffic accidents have significantly increased globally over the past decades and become a big problem for human society. Facing these possible and unexpected traffic accidents, understanding what causes traffic accident and early alarms for some possible ones will play a critical role on planning effective traffic management. However, due to the lack of supported sensing data, research is very limited on the field of updating traffic accident risk in real-time. Therefore, in this paper, we collect big and heterogeneous data (7 months traffic accident data and 1.6 million users' GPS records) to understand how human mobility will affect traffic accident risk. By mining these data, we develop a deep model of Stack denoise Autoencoder to learn hierarchical feature representation of human mobility. And these features are used for efficient prediction of traffic accident risk level. Once the model has been trained, our model can simulate corresponding traffic accident risk map with given real-time input of human mobility. The experimental results demonstrate the efficiency of our model and suggest that traffic accident risk can be significantly more predictable through human mobility.