Urban incident detection based on hybrid convolutional neural networks and bidirectional long short-term memory

Abstract

Real-time incident detection is a major challenge in urban roads. This paper proposes an innovative hybrid method for incident detection, combining convolutional neural networks (CNN) and bidirectional-long short-term memory (Bi-LSTM). CNN extracts complex spatial features from raw data, while Bi-LSTMs are used for incident detection by capturing long-term temporal dependencies present in data. The proposed algorithm is evaluated using simulated data from the open-source software simulation of urban mobility (SUMO). This combination improves incident detection's accuracy and robustness by exploiting spatial and temporal information. Experimental results show that our hybrid approach outperforms the support vector machine (SVM), random forest (RF), and Bi-LSTM algorithms, with a substantial decrease in false positives and the speed of detecting urgent situations.