TY  - JOUR
PY  - 2021//
TI  - Traffic accident prediction methods based on multi-factor models
JO  - Lecture notes in computer science
A1  - Zhao, HaoZhe
A1  - Rao, Guozheng
A1  - Qiu, Han
A1  - Zhang, Cheng
A1  - Fei, Zongming
A1  - Qiu, Meikang
A1  - Kung, Sun-Yuan
SP  - 41
EP  - 52
VL  - 12817
IS  - 
N2  - 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
LA  - en
SN  - 0302-9743
UR  - http://dx.doi.org/10.1007/978-3-030-82153-1_4
ID  - ref1
ER  -