Short-term traffic flow has the characteristics of complex, changeable, strong timeliness, and so on. So the traditional prediction algorithm is difficult to meet its high real-time and accuracy requirements. In this paper, a multiscale and high-precision LSTM-GASVR short-term traffic flow prediction algorithm is proposed. This method uses 15 min traffic flow data of the first 16 sections as input and completes the data preprocessing operation through reconstruction, normalization, and rising dimension by working day factor; establishing the prediction model based on the long- and short-term memory network (LSTM) and inverse normalization; and proposing the GA-SVR model to optimize the prediction results, so as to realize the real-time high-precision prediction of traffic flow. The prediction experiment is carried out according to the charge data of a toll station in Xi’an, Shaanxi Province, from May 2018 to May 2019. The comparison and analysis of various algorithms show that the prediction algorithm proposed in this paper is 20% higher than the LSTM, GRU, CNN, SAE, ARIMA, and SVR, and the R2 can reach 0.982, the explanatory variance is 0.982, and the MAPE is 0.118. The proposed traffic flow prediction algorithm provides strong support for traffic managers to judge the state of the road network to control traffic and guide traffic flow.
Vehicle dangerous behaviour warning plays an important role to improve road traffic safety and efficiency, so a safety assessment method of vehicle behaviour based on the improved Dempster–Shafer (D–S) evidence theory is proposed. Firstly, through analysis of vehicle collision accident mechanism, some factors closely related to vehicle safety are extracted. Also, multiple sensors are synthetically utilised to collect information, which realises the awareness of vehicle state, road attribute, driving environment etc. Then vehicle behaviour identification is accomplished according to the parameter information of the vehicle‐mounted sensors, as well as the related data of adjacent vehicles in vehicular ad hoc networks (VANET). Finally, a sequential type of weighted correction method based on evidence variance is used to integrate different levels of multi‐source heterogeneous information and to achieve safety assessment of vehicle behaviour. The experimental results show that the improved D–S evidence theory reduces the evidence conflict, increasing the accuracy and reliability of vehicle behaviour safety assessment. The study solves the fundamental core problem of active safety warning in VANET and provides a new means of traffic accident warning for the road traffic management department.
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