Dynamic short-term crash risk prediction from traffic conflicts at signalized intersections with emerging mixed...

Dynamic short-term crash risk prediction derived from traffic conflicts can provide significant support for proactive safety management at signalized intersections. Especially after the formation of emerging mixed traffic flow, an accurate prediction of future crash risk can help conceive proactive crash prevention measures. However, the precision of crash risk estimation at signalized intersections with emerging mixed traffic flow is still subject to doubt, largely attributable to the lack of an exclusive conflict indicator. This situation presents considerable challenges to the dynamic short-term crash risk prediction at signalized intersections with emerging mixed traffic flow. Therefore, this study performs dynamic short-term crash risk prediction from traffic conflicts at signalized intersections with emerging mixed traffic flow by combining the non-stationary generalized extreme value (GEV) model and the self-attention mechanism-based online learning long short-term memory (SAM-OL-LSTM) approach. A novel conflict indicator, the time to avoid a crash (TTAC), is developed to describe traffic conflicts in the emerging mixed traffic flow. Based on TTAC, a non-stationary GEV model that considers acceleration variance as a covariate is developed to calculate the value at risk (VaR) for each minute, which is used to dynamically quantify crash risk at signalized intersections. Afterwards, the SAM-OL-LSTM approach that considers traffic volume and the uncertainty in vehicle speed distribution as two input features is proposed to dynamically predict the VaR for the future one minute based on the VaR time series data of the prior five minutes. The results indicate that: i) the proposed SAM-OL-LSTM approach outperforms baseline approaches under various MPRs in terms of prediction accuracy; ii) the application of VaR facilitates a dynamic quantification of the crash risk at an intra-minute temporal resolution; iii) the developed TTAC exhibits a strong capability in identifying traffic conflicts in the emerging mixed traffic flow at signalized intersections. The findings of this study can provide a theoretical foundation for proactive traffic control considering the future crash risk in the emerging mixed traffic flow at signalized intersections.