Traffic Accident Prediction Based on LSTM-GBRT Model

Silamu Wushour,Wenzhong Yang,Zhihao Zhang

doi:10.1155/2020/4206919

Silamu Wushour, Wenzhong Yang + Show 1 more

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https://doi.org/10.1155/2020/4206919

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Abstract

Road traffic accidents are a concrete manifestation of road traffic safety levels. The current traffic accident prediction has a problem of low accuracy. In order to provide traffic management departments with more accurate forecast data, it can be applied in the traffic management system to help make scientific decisions. This paper establishes a traffic accident prediction model based on LSTM-GBRT (long short-term memory, gradient boosted regression trees) and predicts traffic accident safety level indicators by training traffic accident-related data. Compared with various regression models and neural network models, the experimental results show that the LSTM-GBRT model has a good fitting effect and robustness. The LSTM-GBRT model can accurately predict the safety level of traffic accidents, so that the traffic management department can better grasp the situation of traffic safety levels.

Highlights

Road traffic accidents are a concrete manifestation of road traffic safety levels
The long- and short-term memory neural network model (LSTM) [15] model proposed by Hochreiter et al is a variant of the recurrent neural network (RNN)
The future traffic accident trend forecasting work can help the traffic management department to grasp the trend dynamics in time, discover the rules of traffic accidents, formulate laws and regulations according to the rules, make scientific decisions, and construct the traffic system reasonably

Summary

Introduction

“By 2020, half the number of global deaths and injuries from road traffic accidents” is one target of the Sustainable Development Goals (SDGs) published by the United Nations (UN). Statistical regression methods include time series prediction and many classic traffic accident experience models The essence of the model is to find the dynamic relationship between the road traffic accident sequence data. Liu and Wu [11] proposed a grey Verhulst prediction model for road traffic accidents, which is suitable for nonmonotonic wobble development sequences or S-shaped sequences with saturation. The neural network prediction method has strong nonlinear mapping ability, high robustness, and powerful self-learning ability and has been widely used in many fields. He and Guo [13] proposed a traffic accident prediction model based on the BP neural network. The LSTM layer captures time-dependent information in the data; the GBRT model has the advantage of high robustness of ensemble learning for model training

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Result

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Comparative Analysis of Experiments

Conclusion