@article{ref1,
title="Traffic accident prediction methods based on multi-factor models",
journal="Lecture notes in computer science",
year="2021",
author="Zhao, HaoZhe and Rao, Guozheng and Qiu, Han and Zhang, Cheng and Fei, Zongming and Qiu, Meikang and Kung, Sun-Yuan",
volume="12817",
number="",
pages="41-52",
abstract="Road traffic accident prediction has always been a complex problem for intelligent transportation since it is affected by many factors. However, to simplify the calculation complexity, most of the current research considers the impact of a few key factors and ignores multiple factors' impact in reality. To address this problem, we propose traffic accident prediction methods based on multi-factor models. The model introduces information including the severity of the traffic accident, the weather in which the accident occurred, and the external geographic environment to construct a multiple factors model to improve the prediction accuracy. Also, we can use more factors to construct the multi-factor model with the enrichment of data information. The multi-factor model can overcome the shortcomings of existing models in filtering data fluctuations and achieve more accurate predictions by extracting time-periodic features in time series. Furthermore, we combine the multi-factor models with different deep learning models to propose multiple traffic accident prediction methods to explore multi-factor models' effects in traffic accident prediction. The experimental results on the 2004-2018 Connecticut Crash Date Repository data of the University of Connecticut show that the",
language="en",
issn="0302-9743",
doi="10.1007/978-3-030-82153-1_4",
url="http://dx.doi.org/10.1007/978-3-030-82153-1_4"
}